diff --git a/MODULE.bazel.lock b/MODULE.bazel.lock
index d0717c9ad..40b9a7fa6 100644
--- a/MODULE.bazel.lock
+++ b/MODULE.bazel.lock
@@ -585,7 +585,7 @@
     "@@rules_python~//python/extensions:pip.bzl%pip": {
       "os:osx,arch:aarch64": {
         "bzlTransitiveDigest": "Jt+duCAKrnU5xF3PFiPS7QkYjrCdUhqdPafeYppB9kE=",
-        "usagesDigest": "Z4dCjRhz/8yHQAyENSJZ2Zl0ejUdFftcgby1j0p9iOc=",
+        "usagesDigest": "+mPt+51LDygYXlUADj8qBGMURsoM8IkmXKMInJSe3j0=",
         "recordedFileInputs": {
           "@@//requirements-bazel.txt": "67857422abb923ffa1f9a8f3203cb46cc8fc8917ed1308ebde70ca06c93ffb3b"
         },
@@ -12730,7 +12730,7 @@
     "@@rules_python~//python/extensions:python.bzl%python": {
       "general": {
         "bzlTransitiveDigest": "oBPNwJABN+p6JSbMsW8KKLF4oq8ao8WyZfH4DbDfSlQ=",
-        "usagesDigest": "ACNUWsQXrnB8+KRLWeGigAW5SiBdkL9G7elMltNDNlI=",
+        "usagesDigest": "BDif4V0L8PiD6HjjVru23tQ+ww6vYTpaKrnJR7zWy4A=",
         "recordedFileInputs": {},
         "recordedDirentsInputs": {},
         "envVariables": {
diff --git a/poetry.lock b/poetry.lock
index 59535a35e..a4ac3a2e5 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -3083,6 +3083,21 @@ files = [
     {file = "iniconfig-2.0.0.tar.gz", hash = "sha256:2d91e135bf72d31a410b17c16da610a82cb55f6b0477d1a902134b24a455b8b3"},
 ]
 
+[[package]]
+name = "ipdb"
+version = "0.13.13"
+description = "IPython-enabled pdb"
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
+files = [
+    {file = "ipdb-0.13.13-py3-none-any.whl", hash = "sha256:45529994741c4ab6d2388bfa5d7b725c2cf7fe9deffabdb8a6113aa5ed449ed4"},
+    {file = "ipdb-0.13.13.tar.gz", hash = "sha256:e3ac6018ef05126d442af680aad863006ec19d02290561ac88b8b1c0b0cfc726"},
+]
+
+[package.dependencies]
+decorator = {version = "*", markers = "python_version >= \"3.11\""}
+ipython = {version = ">=7.31.1", markers = "python_version >= \"3.11\""}
+
 [[package]]
 name = "ipykernel"
 version = "6.28.0"
@@ -9369,4 +9384,4 @@ workflows = ["dataclasses-json", "dulwich", "flytekit", "google-api-python-clien
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.11, <3.13"
-content-hash = "6515b417ac411b998d0da76e7e48dd3cfe992e991736061e2db358568d373bd5"
+content-hash = "7137c10254e563d55f0aedbeb9c26173ebaea36600633ddba9d8db8ec6a5971f"
diff --git a/pyproject.toml b/pyproject.toml
index 9ebc4a46e..bebb0a1fa 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -45,6 +45,7 @@ click = ">=8.1.7"
 colorlog = ">=6.7.0"
 daft = ">=0.1.2"
 diffrax = ">=0.5.0"
+dill = ">=0.3.8"
 diskcache = ">=5.6.1"
 duckdb = ">=1.0.0"
 einops = ">=0.7.0"
@@ -60,6 +61,7 @@ fsspec = ">=2024.3.0"
 greenlet = ">=3.0.3"
 httpx = ">=0.27.0"
 h5py = ">=3.9.0"
+# hdf5plugin = ">=4.4.0"
 ibis-framework = { extras = ["duckdb"], version = ">=9.2.0" }
 jax = ">=0.4.23"
 jaxlib = ">=0.4.23"
@@ -136,6 +138,7 @@ google-api-python-client = { version = ">=2.79.0", optional = true }
 hydra-core = { version = ">=1.3.2", optional = true }
 hydra-zen = { version = ">=0.12.1", optional = true }
 hypothesis = { version = ">=6.71.0", optional = true }
+ipdb = { version = ">=0.13.13", optional = true }
 ipython = { version = ">=8.11.0", optional = true }
 ipywidgets = { version = ">=8.0.0", optional = true }
 # isort = { version = ">=5.10.1", optional = true }
@@ -218,6 +221,7 @@ optional = true
 [tool.poetry.group.test.dependencies]
 coverage = { version = ">=6.2", extras = ["toml"] }
 hypothesis = ">=6.72.1"
+ipdb = ">=0.13.13"
 ipython = ">=8.11.0"
 # poethepoet = ">=0.19.0"
 pygments = ">=2.15.0"
@@ -423,6 +427,9 @@ log_level = "INFO"
 # We exclude markers associated with slow tests by default
 # but run them in CI with the make target `test-cov-xml`
 # which overrides this behavior.
+# Add these options to enable the ipdb debugger:
+# --pdb
+# --pdbcls=IPython.terminal.debugger:Pdb
 addopts = """ 
 -m "not slow and not pyensembl"
 -rA
diff --git a/scripts/h5/h5.py b/scripts/h5/h5.py
new file mode 100644
index 000000000..2076d1d5f
--- /dev/null
+++ b/scripts/h5/h5.py
@@ -0,0 +1,202 @@
+import weakref
+from pathlib import Path
+
+import h5py
+import hdf5plugin
+import numpy as np
+import pandas as pd
+from beartype import beartype
+from beartype.typing import Any, Dict, Tuple
+
+from pyrovelocity.io.hash import hash_file
+from pyrovelocity.logging import configure_logging
+
+__all__ = ["save_to_h5", "load_from_h5"]
+
+logger = configure_logging(__name__)
+
+
+@beartype
+def save_to_h5(
+    data: Dict[str, Any],
+    filename: str | Path,
+) -> Tuple[Path, str]:
+    with h5py.File(filename, "w") as f:
+        for key, value in data.items():
+            if isinstance(value, np.ndarray):
+                f.create_dataset(
+                    key,
+                    data=value,
+                    **hdf5plugin.Blosc2(
+                        cname="zstd",
+                        clevel=3,
+                        filters=hdf5plugin.Blosc2.SHUFFLE,
+                    ),
+                )
+            elif isinstance(value, pd.DataFrame):
+                group = f.create_group(key)
+                for column in value.columns:
+                    group.create_dataset(
+                        column,
+                        data=value[column].values,
+                        **hdf5plugin.Blosc2(
+                            cname="zstd",
+                            clevel=3,
+                            filters=hdf5plugin.Blosc2.SHUFFLE,
+                        ),
+                    )
+                group.attrs["columns"] = value.columns.tolist()
+                group.attrs["index"] = value.index.tolist()
+            elif isinstance(value, list):
+                f.create_dataset(
+                    key,
+                    data=np.array(value, dtype=h5py.special_dtype(vlen=str)),
+                    **hdf5plugin.Blosc2(
+                        cname="zstd",
+                        clevel=3,
+                        filters=hdf5plugin.Blosc2.SHUFFLE,
+                    ),
+                )
+            else:
+                logger.warning(
+                    f"Skipping {key}: unsupported type {type(value)}"
+                )
+    file_hash = _log_hash(filename, mode="saved")
+    return Path(filename), file_hash
+
+
+class LazyArray:
+    def __init__(self, dataset):
+        self.dataset = dataset
+
+    def __getitem__(self, key):
+        return np.array(self.dataset[key])
+
+    @property
+    def shape(self):
+        return self.dataset.shape
+
+    @property
+    def dtype(self):
+        return self.dataset.dtype
+
+    def __getattr__(self, name):
+        if name.startswith("_"):
+            raise AttributeError(
+                f"'{self.__class__.__name__}' object has no attribute '{name}'"
+            )
+        return getattr(self.dataset, name)
+
+
+class LazyDataFrame:
+    def __init__(self, group):
+        self.group = group
+        self.columns = list(self.group.attrs["columns"])
+        self.index = list(self.group.attrs["index"])
+
+    def __getitem__(self, key):
+        if isinstance(key, str):
+            return LazyArray(self.group[key])
+        else:
+            df = pd.DataFrame(
+                {col: self.group[col][()] for col in self.columns}
+            )
+            df.index = self.index
+            return df[key]
+
+    @property
+    def shape(self):
+        return (len(self.index), len(self.columns))
+
+    def head(self, n=5):
+        df = pd.DataFrame({col: self.group[col][:n] for col in self.columns})
+        df.index = self.index[:n]
+        return df
+
+    def __getattr__(self, name):
+        if name.startswith("_"):
+            raise AttributeError(
+                f"'{self.__class__.__name__}' object has no attribute '{name}'"
+            )
+        if name in self.columns:
+            return LazyArray(self.group[name])
+        return getattr(pd.DataFrame, name)
+
+
+class H5Accessor:
+    def __init__(self, filename):
+        self._filename = filename
+        self._file = None
+        self._open_file()
+        self._finalizer = weakref.finalize(self, self._close_file)
+
+    def _open_file(self):
+        if self._file is None or not self._file.id:
+            self._file = h5py.File(self._filename, "r")
+
+    def _close_file(self):
+        if self._file is not None and self._file.id:
+            self._file.close()
+
+    def __getattr__(self, name):
+        if name.startswith("_"):
+            raise AttributeError(
+                f"'{self.__class__.__name__}' object has no attribute '{name}'"
+            )
+
+        try:
+            self._open_file()
+            if name not in self._file:
+                raise AttributeError(f"No such attribute: {name}")
+
+            item = self._file[name]
+            if isinstance(item, h5py.Dataset):
+                if item.dtype.kind == "S":
+                    return item[()].astype(str).tolist()
+                else:
+                    return LazyArray(item)
+            elif isinstance(item, h5py.Group):
+                if "columns" in item.attrs:
+                    return LazyDataFrame(item)
+                else:
+                    return {
+                        key: self.__getattr__(f"{name}/{key}")
+                        for key in item.keys()
+                    }
+            else:
+                return item
+        except Exception as e:
+            if not name.startswith("_"):
+                print(f"Error accessing {name}: {str(e)}")
+            return None
+
+    def __dir__(self):
+        try:
+            self._open_file()
+            return list(self._file.keys())
+        except Exception as e:
+            print(f"Error listing attributes: {str(e)}")
+            return []
+
+    def close(self):
+        self._close_file()
+
+    def __repr__(self):
+        return f"H5Accessor(filename='{self._filename}')"
+
+
+@beartype
+def load_from_h5(filename: str | Path) -> H5Accessor:
+    accessor = H5Accessor(filename)
+    _log_hash(filename, mode="loaded")
+    return accessor
+
+
+@beartype
+def _log_hash(file_path: str | Path, mode: str = "loaded or saved") -> str:
+    file_hash = hash_file(file_path=file_path)
+    logger.info(
+        f"\nSuccessfully {mode} file: {file_path}\n"
+        f"SHA-256 hash: {file_hash}\n"
+    )
+    return file_hash
diff --git a/scripts/h5/test_h5.py b/scripts/h5/test_h5.py
new file mode 100644
index 000000000..106a50a46
--- /dev/null
+++ b/scripts/h5/test_h5.py
@@ -0,0 +1,129 @@
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+import pytest
+
+from pyrovelocity.io.h5 import H5Accessor, load_from_h5, save_to_h5, hash_file
+
+
+@pytest.fixture
+def sample_data():
+    return {
+        "array": np.array([1, 2, 3, 4, 5]),
+        "dataframe": pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]}),
+        "list": ["a", "b", "c"],
+        "nested": {
+            "nested_array": np.array([6, 7, 8, 9, 10]),
+            "nested_df": pd.DataFrame({"C": [7, 8, 9], "D": [10, 11, 12]}),
+        },
+    }
+
+
+def test_save_and_load_h5(tmp_path, sample_data):
+    file_path = tmp_path / "test.h5"
+
+    saved_path, file_hash = save_to_h5(sample_data, file_path)
+    assert saved_path == file_path
+    assert isinstance(file_hash, str)
+    assert file_path.exists()
+
+    loaded_data = load_from_h5(file_path)
+    assert isinstance(loaded_data, H5Accessor)
+
+    np.testing.assert_array_equal(loaded_data.array[:], sample_data["array"])
+
+    pd.testing.assert_frame_equal(
+        loaded_data.dataframe[:], sample_data["dataframe"]
+    )
+
+    assert loaded_data.list == sample_data["list"]
+
+    np.testing.assert_array_equal(
+        loaded_data.nested["nested_array"][:],
+        sample_data["nested"]["nested_array"],
+    )
+    pd.testing.assert_frame_equal(
+        loaded_data.nested["nested_df"][:], sample_data["nested"]["nested_df"]
+    )
+
+
+def test_lazy_loading(tmp_path, sample_data):
+    file_path = tmp_path / "test_lazy.h5"
+    save_to_h5(sample_data, file_path)
+    loaded_data = load_from_h5(file_path)
+
+    assert isinstance(loaded_data.array, loaded_data.array.__class__)
+    assert loaded_data.array.shape == sample_data["array"].shape
+    assert loaded_data.array.dtype == sample_data["array"].dtype
+
+    assert isinstance(loaded_data.dataframe, loaded_data.dataframe.__class__)
+    assert loaded_data.dataframe.shape == sample_data["dataframe"].shape
+    assert all(
+        loaded_data.dataframe.columns == sample_data["dataframe"].columns
+    )
+
+
+def test_h5_accessor_methods(tmp_path, sample_data):
+    file_path = tmp_path / "test_accessor.h5"
+    save_to_h5(sample_data, file_path)
+    accessor = load_from_h5(file_path)
+
+    assert set(dir(accessor)) == set(sample_data.keys())
+
+    assert repr(accessor) == f"H5Accessor(filename='{file_path}')"
+
+    accessor.close()
+    with pytest.raises(Exception):
+        accessor.array[:]
+
+
+def test_unsupported_type_warning(tmp_path):
+    unsupported_data = {"unsupported": set([1, 2, 3])}
+    file_path = tmp_path / "test_unsupported.h5"
+
+    with pytest.warns(
+        UserWarning, match="Skipping unsupported: unsupported type"
+    ):
+        save_to_h5(unsupported_data, file_path)
+
+
+def test_nonexistent_attribute(tmp_path, sample_data):
+    file_path = tmp_path / "test_nonexistent.h5"
+    save_to_h5(sample_data, file_path)
+    loaded_data = load_from_h5(file_path)
+
+    with pytest.raises(AttributeError, match="No such attribute: nonexistent"):
+        loaded_data.nonexistent
+
+
+def test_dataframe_operations(tmp_path, sample_data):
+    file_path = tmp_path / "test_df_ops.h5"
+    save_to_h5(sample_data, file_path)
+    loaded_data = load_from_h5(file_path)
+
+    assert loaded_data.dataframe.head().equals(sample_data["dataframe"].head())
+
+    np.testing.assert_array_equal(
+        loaded_data.dataframe.A[:], sample_data["dataframe"]["A"]
+    )
+
+
+def test_file_hash_consistency(tmp_path, sample_data):
+    file_path = tmp_path / "test_hash.h5"
+
+    _, save_hash = save_to_h5(sample_data, file_path)
+
+    load_from_h5(file_path)
+
+    assert save_hash == hash_file(file_path)
+
+
+@pytest.mark.parametrize("file_path", ["test.h5", Path("test.h5")])
+def test_path_types(tmp_path, sample_data, file_path):
+    full_path = tmp_path / file_path
+    saved_path, _ = save_to_h5(sample_data, full_path)
+    assert saved_path == full_path
+
+    loaded_data = load_from_h5(full_path)
+    assert isinstance(loaded_data, H5Accessor)
diff --git a/src/pyrovelocity/BUILD.bazel b/src/pyrovelocity/BUILD.bazel
index 6a9120e68..fc3f13bfe 100644
--- a/src/pyrovelocity/BUILD.bazel
+++ b/src/pyrovelocity/BUILD.bazel
@@ -14,7 +14,9 @@ load("//bazel:python.bzl", "py_test_module_list", "xdoctest")
 
 xdoctest(
     files = glob(
-        include = ["**/*.py"],
+        include = [
+            "**/*.py",
+        ],
         exclude = [
             "workflows/**",
             "tests/**",
@@ -81,9 +83,12 @@ py_test_module_list(
 py_library(
     name = "pyrovelocity",
     srcs = glob(
-        ["**/*.py"],
+        [
+            "**/*.py",
+        ],
         exclude = ["tests/**/*.py"],
     ),
+    data = ["styles/common.mplstyle"],
     visibility = [
         "//src/pyrovelocity:__pkg__",
         "//src/pyrovelocity:__subpackages__",
diff --git a/src/pyrovelocity/analysis/analyze.py b/src/pyrovelocity/analysis/analyze.py
index 8eb596e8f..049302f3c 100644
--- a/src/pyrovelocity/analysis/analyze.py
+++ b/src/pyrovelocity/analysis/analyze.py
@@ -9,7 +9,11 @@
 from anndata import AnnData
 from astropy.stats import rayleightest
 from beartype import beartype
+from beartype.typing import Any
+from jaxtyping import Array, Num, jaxtyped
 from numpy import ndarray
+from numpy.typing import NDArray
+from pandas import DataFrame
 from sklearn.pipeline import Pipeline
 
 from pyrovelocity.analysis.cytotrace import compute_similarity2
@@ -140,43 +144,36 @@ def compute_mean_vector_field(
 
 @beartype
 def compute_volcano_data(
-    posterior_samples: List[Dict[str, ndarray]],
-    adata: List[AnnData],
+    posterior_samples: Dict[str, NDArray[Any] | DataFrame],
+    adata: AnnData,
     time_correlation_with: str = "s",
     selected_genes: Optional[List[str]] = None,
     negative: bool = False,
 ) -> Tuple[pd.DataFrame, List[str]]:
-    assert isinstance(posterior_samples, (tuple, list))
-    assert isinstance(adata, (tuple, list))
-    assert "s" in posterior_samples[0]
-    assert "alpha" in posterior_samples[0]
+    assert "s" in posterior_samples
+    assert "alpha" in posterior_samples
 
     maes_list = []
     cors = []
     genes = []
-    labels = []
-    switching = []
-    for p, ad, label in zip(posterior_samples, adata, ["train", "valid"]):
-        print(label)
-        for sample in range(p["alpha"].shape[0]):
-            maes_list.append(
-                mae_per_gene(
-                    p["s"][sample].squeeze(),
-                    ensure_numpy_array(ad.layers["raw_spliced"]),
-                )
-            )
-            df_genes_cors = compute_similarity2(
-                p[time_correlation_with][sample].squeeze(),
-                p["cell_time"][sample].squeeze().reshape(-1, 1),
+
+    for sample in range(posterior_samples["alpha"].shape[0]):
+        maes_list.append(
+            mae_per_gene(
+                posterior_samples["s"][sample].squeeze(),
+                ensure_numpy_array(adata.layers["raw_spliced"]),
             )
-            cors.append(df_genes_cors[0])
-            genes.append(ad.var_names.values)
-            labels.append([f"Poisson_{label}"] * len(ad.var_names.values))
+        )
+        df_genes_cors = compute_similarity2(
+            posterior_samples[time_correlation_with][sample].squeeze(),
+            posterior_samples["cell_time"][sample].squeeze().reshape(-1, 1),
+        )
+        cors.append(df_genes_cors[0])
+        genes.append(adata.var_names.values)
 
     volcano_data = pd.DataFrame(
         {
             "mean_mae": np.hstack(maes_list),
-            "label": np.hstack(labels),
             "time_correlation": np.hstack(cors),
             "genes": np.hstack(genes),
         }
@@ -308,11 +305,98 @@ def vector_field_uncertainty(
     return v_map_all, embeds_radian, fdri
 
 
-def mae_per_gene(pred_counts: ndarray, true_counts: ndarray) -> ndarray:
-    """Computes mean average error between counts and predicted probabilities."""
-    error = np.abs(true_counts - pred_counts).sum(-2)
-    total = np.clip(true_counts.sum(-2), 1, np.inf)
-    return -np.array(error / total)
+@jaxtyped(typechecker=beartype)
+def mae_per_gene(
+    pred_counts: NDArray[np.number] | Num[Array, "obs vars"],
+    true_counts: NDArray[np.number] | Num[Array, "obs vars"],
+) -> NDArray[np.number] | Num[Array, "vars"]:
+    """
+    Computes mean absolute error (MAE) between predictive samples and true counts.
+
+    The function returns the negative of the normalized MAE for
+    consistency with the convention that higher values should indicate
+    better performance in visualizations.
+
+    TODO: use jax and deprecate numpy
+
+    ```python
+    import jax.numpy as jnp
+    from jaxtyping import Array, Num, jaxtyped
+
+    @jaxtyped(typechecker=beartype)
+    def mae_per_gene(
+        pred_counts: Num[Array, "obs vars"],
+        true_counts: Num[Array, "obs vars"],
+    ) -> Num[Array, "vars"]:
+        pass
+    ```
+
+    Args:
+        pred_counts (NDArray[np.number] | Num[Array, "obs vars"]):
+            Predicted counts for all observations.
+        true_counts (NDArray[np.number] | Num[Array, "obs vars"]):
+            Observed counts for all observations.
+
+    Returns:
+        NDArray[np.number] | Num[Array, "vars"]: Negative mean absolute error for each gene.
+
+    Example:
+        >>> from pyrovelocity.analysis.analyze import mae_per_gene
+        >>> import numpy as np
+        >>> xp = np
+        >>> # import jax.numpy as jnp
+        >>> # xp = jnp
+        >>> true_counts = xp.array(
+        ...     [
+        ...         [1, 2, 3],
+        ...         [1, 2, 3],
+        ...         [1, 2, 3],
+        ...         [1, 2, 3],
+        ...     ]
+        ... )
+        >>> pred_counts = xp.array(
+        ...     [
+        ...         [1.1, 2.2, 3.3],
+        ...         [1.1, 2.2, 3.3],
+        ...         [1.1, 2.2, 3.3],
+        ...         [1.1, 2.2, 3.3],
+        ...     ]
+        ... )
+        >>> mae = mae_per_gene(pred_counts, true_counts)
+        >>> print(mae)
+        >>> result = xp.allclose(
+        ...     mae, xp.array([-0.1, -0.1, -0.1]), rtol=1e-2, atol=1e-2
+        ... )
+        >>> print(result.item() if hasattr(result, 'item') else result)
+        True
+        >>> true_counts = xp.array(
+        ...     [
+        ...         [10, 15, 0],
+        ...         [20, 25, 0],
+        ...     ]
+        ... )
+        >>> pred_counts = xp.array(
+        ...     [
+        ...         [12, 14, 0],
+        ...         [18, 26, 0],
+        ...     ]
+        ... )
+        >>> mae = mae_per_gene(pred_counts, true_counts)
+        >>> print(mae)
+        >>> result = xp.allclose(
+        ...     mae, xp.array([-0.133, -0.05, -0.]), rtol=1e-2, atol=1e-2
+        ... )
+        >>> print(result.item() if hasattr(result, 'item') else result)
+        True
+    """
+    xp = np
+    mean_true_counts = xp.mean(true_counts, axis=0)
+    absolute_errors = xp.abs(true_counts - pred_counts)
+    mae = xp.mean(absolute_errors, axis=0)
+
+    normalized_mae = xp.nan_to_num(mae / mean_true_counts, nan=0.0, posinf=0.0)
+
+    return -normalized_mae
 
 
 @beartype
@@ -320,6 +404,8 @@ def top_mae_genes(
     volcano_data: pd.DataFrame,
     mae_top_percentile: int | float = 10.0,
     min_genes_per_bin: int = 2,
+    max_genes_per_bin: Optional[int] = None,
+    gene_name_filter: str = "^Act|^Rpl|^Rps",
 ) -> List[str]:
     """
     Identify top genes based on Mean Absolute Error (MAE) percentile, excluding ribosomal genes,
@@ -329,6 +415,7 @@ def top_mae_genes(
         volcano_data (pd.DataFrame): DataFrame containing MAE and time correlation
         mae_top_percentile (float): Percentile threshold for selecting top MAE genes (0-100)
         min_genes_per_bin (int): Minimum number of genes to select from each bin
+        max_genes_per_bin (Optional[int]): Maximum number of genes to select from each bin
 
     Returns:
         List[str]: List of gene indices from volcano_data, sorted by time correlation.
@@ -337,9 +424,13 @@ def top_mae_genes(
         raise ValueError("mae_top_percentile must be between 0 and 100")
     if min_genes_per_bin < 0:
         raise ValueError("min_genes_per_bin must be non-negative")
+    if max_genes_per_bin is not None and max_genes_per_bin < min_genes_per_bin:
+        raise ValueError(
+            "max_genes_per_bin must be greater than or equal to min_genes_per_bin"
+        )
 
     filtered_data = volcano_data[
-        ~volcano_data.index.str.contains(("^Rpl|^Rps"), case=False)
+        ~volcano_data.index.str.contains((gene_name_filter), case=False)
     ]
 
     filtered_data["time_corr_bin"] = pd.cut(
@@ -365,6 +456,13 @@ def top_mae_genes(
 
         if len(top_genes_in_bin) < min_genes_per_bin:
             top_genes_in_bin = bin_data.nlargest(min_genes_per_bin, "mean_mae")
+        elif (
+            max_genes_per_bin is not None
+            and len(top_genes_in_bin) > max_genes_per_bin
+        ):
+            top_genes_in_bin = top_genes_in_bin.nlargest(
+                max_genes_per_bin, "mean_mae"
+            )
 
         selected_genes.append(top_genes_in_bin)
 
@@ -378,7 +476,8 @@ def top_mae_genes(
 
     logger.info(
         f"\nSelected {len(gene_indices)} genes based on top {mae_top_percentile}% MAE "
-        f"from 8 time correlation bins, with a minimum of {min_genes_per_bin} genes per bin, "
+        f"from 8 time correlation bins, with a minimum of {min_genes_per_bin} genes per bin"
+        f"{f' and a maximum of {max_genes_per_bin} genes per bin' if max_genes_per_bin else ''}, "
         f"sorted by time correlation:\n\n"
         f"  {gene_indices}\n\n"
     )
diff --git a/src/pyrovelocity/interfaces.py b/src/pyrovelocity/interfaces.py
index 9556d59b1..38f3415bd 100644
--- a/src/pyrovelocity/interfaces.py
+++ b/src/pyrovelocity/interfaces.py
@@ -1,4 +1,4 @@
-from dataclasses import make_dataclass
+from dataclasses import field, make_dataclass
 from enum import Enum
 
 from beartype.typing import TYPE_CHECKING, Any, Dict, Tuple, Type
@@ -61,6 +61,7 @@ class PyroVelocityTrainInterface(DataClassJSONMixin):
     "adata": (str, "data/external/simulated.h5ad"),
     "data_processed_path": (str, "data/processed"),
     "reports_processed_path": (str, "reports/processed"),
+    "selected_genes": (list[str], field(default_factory=lambda: [""])),
 }
 
 preprocess_data_fields = create_dataclass_from_callable(
diff --git a/src/pyrovelocity/io/compressedpickle.py b/src/pyrovelocity/io/compressedpickle.py
index f9684c975..a8dfd6315 100644
--- a/src/pyrovelocity/io/compressedpickle.py
+++ b/src/pyrovelocity/io/compressedpickle.py
@@ -1,8 +1,8 @@
 import os
-import pickle
 from os import PathLike
 from pathlib import Path
 
+import dill as pickle
 import numpy as np
 from beartype import beartype
 from beartype.typing import Any, Dict
@@ -13,6 +13,7 @@
     ZstdDecompressor,
 )
 
+from pyrovelocity.io.hash import hash_file
 from pyrovelocity.io.sparsity import densify_arrays, sparsify_arrays
 from pyrovelocity.logging import configure_logging
 
@@ -99,7 +100,7 @@ def save(
                     density_threshold=density_threshold,
                 )
             else:
-                logger.warning(
+                logger.debug(
                     """
                     The object is not a dictionary of numpy arrays or COO objects.
                     It cannot be automatically sparsified.
@@ -118,6 +119,7 @@ def save(
             with compression_context.stream_writer(f) as compressor:
                 pickle.dump(obj, compressor)
 
+        _log_hash(file_path=file_path, mode="saved")
         return file_path
 
     @staticmethod
@@ -155,10 +157,21 @@ def load(
             ):
                 obj = densify_arrays(obj)
             else:
-                logger.warning(
+                logger.debug(
                     """
                     The object is not a dictionary of numpy arrays or COO objects.
                     It cannot be automatically densified.
                     """
                 )
+        _log_hash(file_path=file_path, mode="loaded")
         return obj
+
+
+@beartype
+def _log_hash(file_path: str | Path, mode: str = "loaded or saved") -> str:
+    file_hash = hash_file(file_path=file_path)
+    logger.info(
+        f"\nSuccessfully {mode} file: {file_path}\n"
+        f"SHA-256 hash: {file_hash}\n"
+    )
+    return file_hash
diff --git a/src/pyrovelocity/io/datasets.py b/src/pyrovelocity/io/datasets.py
index 39418a254..a42a176eb 100644
--- a/src/pyrovelocity/io/datasets.py
+++ b/src/pyrovelocity/io/datasets.py
@@ -1,3 +1,4 @@
+import os
 from pathlib import Path
 
 import anndata
@@ -5,6 +6,29 @@
 import scvelo as scv
 from beartype import beartype
 
+from pyrovelocity.io.hash import hash_file
+from pyrovelocity.logging import configure_logging
+
+__all__ = [
+    "pbmc5k",
+    "pbmc10k",
+    "pons",
+    "larry",
+    "larry_neu",
+    "larry_mono",
+    "larry_cospar",
+    "larry_cytotrace",
+    "larry_dynamical",
+    "larry_tips",
+    "larry_multilineage",
+    "pancreas",
+    "bonemarrow",
+    "pbmc68k",
+]
+
+
+logger = configure_logging(__name__)
+
 
 @beartype
 def pbmc5k(
@@ -20,6 +44,10 @@ def pbmc5k(
     """
     url = "https://storage.googleapis.com/pyrovelocity/data/pbmc5k.h5ad"
     adata = sc.read(file_path, backup_url=url, sparse=True, cache=True)
+    expected_hash = (
+        "349028bdf7992f5b196a3c4efd7a83cebdf0624d3d42a712628967dad608ad35"
+    )
+    _check_hash(file_path, expected_hash)
     return adata
 
 
@@ -37,6 +65,10 @@ def pbmc10k(
     """
     url = "https://storage.googleapis.com/pyrovelocity/data/pbmc10k.h5ad"
     adata = sc.read(file_path, backup_url=url, sparse=True, cache=True)
+    expected_hash = (
+        "267d1d2710251a68413fcb82fa03f9bcfe8fe33a6bae05117603da983ebe2c5b"
+    )
+    _check_hash(file_path, expected_hash)
     return adata
 
 
@@ -55,6 +87,10 @@ def pons(
     """
     url = "https://storage.googleapis.com/pyrovelocity/data/oligo_lite.h5ad"
     adata = sc.read(file_path, backup_url=url, sparse=True, cache=True)
+    expected_hash = (
+        "d3a3286a6f33c307aca20bcbb127abb6ac52dbf6c968ee24df6ed9584b857de0"
+    )
+    _check_hash(file_path, expected_hash)
     return adata
 
 
@@ -73,6 +109,10 @@ def larry(
     """
     url = "https://figshare.com/ndownloader/files/37028569"
     adata = sc.read(file_path, backup_url=url, sparse=True, cache=True)
+    expected_hash = (
+        "7f567427f591e85678580ebaa8b1e59aae51e9be63864a68ef9e905a0cbe8575"
+    )
+    _check_hash(file_path, expected_hash)
     return adata
 
 
@@ -92,9 +132,22 @@ def larry_neu(
         Returns `AnnData` object
     """
     url = "https://figshare.com/ndownloader/files/37028575"
-    adata = sc.read(file_path, backup_url=url, sparse=True, cache=True)
-    adata = adata[adata.obs.state_info != "Centroid", :]
-    adata.write(file_path)
+
+    if os.path.isfile(file_path):
+        adata = sc.read(file_path, backup_url=url, sparse=True, cache=True)
+    else:
+        adata = sc.read(file_path, backup_url=url, sparse=True, cache=True)
+        premodification_hash = (
+            "ae4113834a1318168c92715887173d27bf88c57ccbd715e69481b13cf2539b92"
+        )
+        _check_hash(file_path, premodification_hash)
+        adata = adata[adata.obs.state_info != "Centroid", :]
+        adata.write(file_path)
+
+    expected_hash = (
+        "384784699c10e192677c006bb407aaedbdf3e3c66f1ca1f4d8d1284ddf8fa436"
+    )
+    _check_hash(file_path, expected_hash)
     return adata
 
 
@@ -113,9 +166,22 @@ def larry_mono(
         Returns `AnnData` object
     """
     url = "https://figshare.com/ndownloader/files/37028572"
-    adata = sc.read(file_path, backup_url=url, sparse=True, cache=True)
-    adata = adata[adata.obs.state_info != "Centroid", :]
-    adata.write(file_path)
+
+    if os.path.isfile(file_path):
+        adata = sc.read(file_path, backup_url=url, sparse=True, cache=True)
+    else:
+        adata = sc.read(file_path, backup_url=url, sparse=True, cache=True)
+        premodification_hash = (
+            "b880b7f72f0ccc8b11ca63c53d340983a6d478e214d4960a529d6e02a9ccd597"
+        )
+        _check_hash(file_path, premodification_hash)
+        adata = adata[adata.obs.state_info != "Centroid", :]
+        adata.write(file_path)
+
+    expected_hash = (
+        "75e59aa7f0d47d2d013dc7444f89a858363110ba32d7a576ac3dc819cac0afa8"
+    )
+    _check_hash(file_path, expected_hash)
     return adata
 
 
@@ -133,6 +199,10 @@ def larry_cospar(
     """
     url = "https://storage.googleapis.com/pyrovelocity/data/larry_cospar.h5ad"
     adata = sc.read(file_path, backup_url=url, sparse=True, cache=True)
+    expected_hash = (
+        "cdf2ff25c4e3222122beeff2da65539ba7541f4426547f4622813874fd9be070"
+    )
+    _check_hash(file_path, expected_hash)
     return adata
 
 
@@ -152,6 +222,10 @@ def larry_cytotrace(
         "https://storage.googleapis.com/pyrovelocity/data/larry_cytotrace.h5ad"
     )
     adata = sc.read(file_path, backup_url=url, sparse=True, cache=True)
+    expected_hash = (
+        "fdaa99408f93c52d993cd47bf7997f5c01f7cb88be1824864075c67afb04b625"
+    )
+    _check_hash(file_path, expected_hash)
     return adata
 
 
@@ -171,6 +245,10 @@ def larry_dynamical(
         "https://storage.googleapis.com/pyrovelocity/data/larry_dynamical.h5ad"
     )
     adata = sc.read(file_path, backup_url=url, sparse=True, cache=True)
+    expected_hash = (
+        "d3808463203f3d0a8fd1eb76ac723d8e6ab939223eb528434c3e38926a460863"
+    )
+    _check_hash(file_path, expected_hash)
     return adata
 
 
@@ -190,6 +268,10 @@ def larry_tips(
     adata = adata[adata.obs["time_info"] == 6.0]
     adata = adata[adata.obs["state_info"] != "Undifferentiated"]
     adata.write(file_path)
+    expected_hash = (
+        "e04e9d0b82651d170c93aaa9a1a0f764c91a986d8ae97d9401b4ee6c496f492c"
+    )
+    _check_hash(file_path, expected_hash)
     return adata
 
 
@@ -205,10 +287,17 @@ def larry_multilineage(
     Returns:
         Returns `AnnData` object
     """
-    adata_larry_mono = larry_mono()
-    adata_larry_neu = larry_neu()
-    adata = adata_larry_mono.concatenate(adata_larry_neu)
-    adata.write(file_path)
+    if os.path.isfile(file_path):
+        adata = sc.read(file_path, sparse=True, cache=True)
+    else:
+        adata_larry_mono = larry_mono()
+        adata_larry_neu = larry_neu()
+        adata = adata_larry_mono.concatenate(adata_larry_neu)
+        adata.write(file_path)
+    expected_hash = (
+        "9add35ae4f736aa5e11d076eadb3b1d842dbc88102047f029bd7fa0929f46be0"
+    )
+    _check_hash(file_path, expected_hash)
     return adata
 
 
@@ -231,6 +320,10 @@ def pancreas(
         Returns `AnnData` object
     """
     adata = scv.datasets.pancreas(file_path=file_path)
+    expected_hash = (
+        "9e3e459eca00ba06b496ec80def32941b5b2889918720e3e7aa6ffb811fbe7c6"
+    )
+    _check_hash(file_path, expected_hash)
     return adata
 
 
@@ -253,6 +346,10 @@ def bonemarrow(
         Returns `AnnData` object
     """
     adata = scv.datasets.bonemarrow(file_path=file_path)
+    expected_hash = (
+        "12222efe4a9dd5916fa279860a2a4fdec383bd2e0db0249a1cd18c549c4a4c2c"
+    )
+    _check_hash(file_path, expected_hash)
     return adata
 
 
@@ -274,8 +371,42 @@ def pbmc68k(
     Returns:
         Returns `AnnData` object
     """
-    adata = scv.datasets.pbmc68k(file_path=file_path)
-    scv.pp.remove_duplicate_cells(adata)
-    adata.obsm["X_tsne"][:, 0] *= -1
-    adata.write(file_path)
+    if os.path.isfile(file_path):
+        adata = scv.datasets.pbmc68k(file_path=file_path)
+    else:
+        adata = scv.datasets.pbmc68k(file_path=file_path)
+        premodification_hash = (
+            "c93b1ccad909b6a41539a57975737bd946ea1becce066c250aca129d8dfa26fb"
+        )
+        _check_hash(file_path, premodification_hash)
+        scv.pp.remove_duplicate_cells(adata)
+        adata.obsm["X_tsne"][:, 0] *= -1
+        adata.write(file_path)
+    expected_hash = (
+        "c6ce6ca3dac3b97012d12c7a5e5ec1953bbd2ad5535538b0ef549f54d9276f0b"
+    )
+    _check_hash(file_path, expected_hash)
     return adata
+
+
+@beartype
+def _check_hash(file_path: str | Path, expected_hash: str) -> None:
+    actual_hash = _log_hash(file_path)
+    if actual_hash != expected_hash:
+        logger.error(
+            f"\nHash mismatch for {file_path}.\n"
+            f"Expected: {expected_hash}\n"
+            f"Actual: {actual_hash}"
+        )
+    else:
+        logger.info(f"\nHash check passed: {file_path}")
+
+
+@beartype
+def _log_hash(file_path: str | Path) -> str:
+    adata_hash = hash_file(file_path=file_path)
+    logger.info(
+        f"\nSuccessfully read or created file: {file_path}\n"
+        f"SHA-256 hash: {adata_hash}\n"
+    )
+    return adata_hash
diff --git a/src/pyrovelocity/io/hash.py b/src/pyrovelocity/io/hash.py
new file mode 100644
index 000000000..144173f57
--- /dev/null
+++ b/src/pyrovelocity/io/hash.py
@@ -0,0 +1,19 @@
+import hashlib
+from pathlib import Path
+
+from beartype import beartype
+
+
+@beartype
+def hash_file(
+    file_path: str | Path,
+    chunk_size: int = 8192,
+):
+    sha256_hash = hashlib.sha256()
+    file_path = Path(file_path)
+
+    with file_path.open("rb") as f:
+        for byte_block in iter(lambda: f.read(chunk_size), b""):
+            sha256_hash.update(byte_block)
+
+    return sha256_hash.hexdigest()
diff --git a/src/pyrovelocity/io/json.py b/src/pyrovelocity/io/json.py
deleted file mode 100644
index 638f72420..000000000
--- a/src/pyrovelocity/io/json.py
+++ /dev/null
@@ -1,381 +0,0 @@
-import json
-import re
-from collections import defaultdict
-from datetime import timedelta
-from pathlib import Path
-from typing import Any, Dict
-
-import pandas as pd
-from beartype import beartype
-from beartype.typing import Any, Dict, Tuple
-from returns.result import Failure, Result, Success
-from rich.console import Console
-from rich.table import Table
-
-__all__ = [
-    "load_json",
-    "merge_json",
-    "combine_json_files",
-    "add_duration_to_run_info",
-    "generate_tables",
-]
-
-
-@beartype
-def load_json(file_path: Path) -> Result[Dict[str, Any], Exception]:
-    """
-    Load JSON data from a file.
-
-    Args:
-        file_path: Path to the JSON file.
-
-    Returns:
-        Result[Dict[str, Any], Exception]: A result object containing the loaded JSON data or an exception.
-
-    Examples:
-        >>> import tempfile
-        >>> tmp = getfixture('tmp_path')  # Get a temporary directory path
-        >>> json_file = tmp / 'test.json'
-        >>> json_file.write_text('{"key": "value"}')  # Create a test JSON file
-        >>> result = load_json(json_file)
-        >>> print(f"Result: {result}")
-        Result: <Success: {'key': 'value'}>
-        >>> assert result.unwrap() == {"key": "value"}
-        >>> non_existent_file = tmp / 'non_existent.json'
-        >>> result = load_json(non_existent_file)
-        >>> print(f"Result: {result}")
-        Result: <Failure: ...
-    """
-    try:
-        with file_path.open("r") as file:
-            return Success(json.load(file))
-    except Exception as e:
-        return Failure(e)
-
-
-@beartype
-def merge_json(dict1: Dict[str, Any], dict2: Dict[str, Any]) -> Dict[str, Any]:
-    """
-    Merge two JSON dictionaries.
-
-    Args:
-        dict1: The first JSON dictionary.
-        dict2: The second JSON dictionary.
-
-    Returns:
-        Dict[str, Any]: The merged dictionary.
-
-    Examples:
-        >>> dict1 = {"a": 1, "b": 2}
-        >>> dict2 = {"b": 3, "c": 4}
-        >>> result = merge_json(dict1, dict2)
-        >>> print(result)
-        {'a': 1, 'b': 3, 'c': 4}
-        >>> assert result == {"a": 1, "b": 3, "c": 4}
-    """
-    return {**dict1, **dict2}
-
-
-@beartype
-def combine_json_files(
-    file1: str | Path,
-    file2: str | Path,
-    output_file: str | Path,
-) -> Result[None, Exception]:
-    """
-    Combine two JSON files and write the result to a new file.
-
-    Args:
-        file1: Path to the first JSON file.
-        file2: Path to the second JSON file.
-        output_file: Path to the output JSON file.
-
-    Returns:
-        Result[None, Exception]: A result object encapsulating success or failure.
-
-    Examples:
-        >>> tmp = getfixture('tmp_path')  # Get a temporary directory path
-        >>> file1 = tmp / 'file1.json'
-        >>> file2 = tmp / 'file2.json'
-        >>> output_file = tmp / 'combined.json'
-        >>> file1.write_text('{"a": 1, "b": 2}')
-        >>> file2.write_text('{"b": 3, "c": 4}')
-        >>> result = combine_json_files(file1, file2, output_file)
-        >>> print(f"Result: {result}")
-        Result: <Success: None>
-        >>> assert output_file.exists()
-        >>> with open(output_file, 'r') as f:
-        ...     content = json.load(f)
-        ...     assert content == {"a": 1, "b": 3, "c": 4}
-    """
-    try:
-        data1_result = load_json(Path(file1))
-        data2_result = load_json(Path(file2))
-
-        if isinstance(data1_result, Failure):
-            return data1_result
-        if isinstance(data2_result, Failure):
-            return data2_result
-
-        data1 = data1_result.unwrap()
-        data2 = data2_result.unwrap()
-
-        combined_data = merge_json(data1, data2)
-
-        with output_file.open("w") as file:
-            json.dump(combined_data, file, indent=2)
-
-        return Success(None)
-    except Exception as e:
-        return Failure(e)
-
-
-@beartype
-def add_duration_to_run_info(
-    file_path: Path | str = "metrics.json"
-) -> Result[None, Exception]:
-    """
-    Load a JSON file containing run information, add a duration field,
-    and overwrite the file with the updated information.
-
-    The duration is calculated as the difference between 'end_time' and 'start_time',
-    and is added as a new field in the format "HH:MM:SS".
-
-    Args:
-        file_path (Path | str): Path to the JSON file. Defaults to "metrics.json".
-
-    Returns:
-        Result[None, Exception]: A Result object containing None if successful,
-                                 or an Exception if an error occurred.
-
-    Examples:
-        >>> import tempfile
-        >>> from pathlib import Path
-        >>> # Create a temporary JSON file
-        >>> with tempfile.NamedTemporaryFile(mode='w', delete=False, suffix='.json') as temp_file:
-        ...     json.dump({
-        ...         "start_time": 1723744878851,
-        ...         "end_time": 1723744887595,
-        ...         "other_field": "value"
-        ...     }, temp_file)
-        ...     temp_file_path = Path(temp_file.name)
-        >>> # Test the function
-        >>> result = add_duration_to_run_info(temp_file_path)
-        >>> print(f"Result: {result}")
-        Result: <Success: None>
-        >>> # Verify the file was updated correctly
-        >>> with open(temp_file_path, 'r') as f:
-        ...     updated_data = json.load(f)
-        >>> print(f"Duration added: {updated_data['duration']}")
-        Duration added: 00:00:08
-        >>> assert 'duration' in updated_data
-        >>> assert updated_data['duration'] == "00:00:08"
-        >>> assert updated_data['other_field'] == "value"
-        >>> # Clean up
-        >>> temp_file_path.unlink()
-
-        >>> # Test with non-existent file
-        >>> result = add_duration_to_run_info("non_existent.json")
-        >>> print(f"Result: {result}")
-        Result: <Failure: ...
-
-        >>> # Test with invalid JSON
-        >>> with tempfile.NamedTemporaryFile(mode='w', delete=False, suffix='.json') as temp_file:
-        ...     temp_file.write("Invalid JSON")
-        ...     temp_file_path = Path(temp_file.name)
-        >>> result = add_duration_to_run_info(temp_file_path)
-        >>> print(f"Result: {result}")
-        Result: <Failure: ...
-        >>> # Clean up
-        >>> temp_file_path.unlink()
-    """
-    file_path = Path(file_path)
-
-    try:
-        with file_path.open("r") as file:
-            data: Dict[str, Any] = json.load(file)
-
-        start_time = data.get("start_time")
-        end_time = data.get("end_time")
-
-        if start_time is not None and end_time is not None:
-            duration = timedelta(milliseconds=end_time - start_time)
-            hours, remainder = divmod(duration.seconds, 3600)
-            minutes, seconds = divmod(remainder, 60)
-
-            data["duration"] = f"{hours:02d}:{minutes:02d}:{seconds:02d}"
-        else:
-            return Failure(
-                ValueError(
-                    "Unable to calculate duration. Start time or end time is missing."
-                )
-            )
-
-        with file_path.open("w") as file:
-            json.dump(data, file, indent=2)
-
-        return Success(None)
-
-    except Exception as e:
-        return Failure(e)
-
-
-@beartype
-def generate_tables(json_data: Dict[str, Any]) -> Tuple[str, str, str, Table]:
-    """
-    Generate LaTeX, HTML, and rich tables from the provided JSON data.
-
-    Args:
-        json_data (Dict[str, Any]):
-            The JSON data containing metrics for different datasets and models.
-
-    Returns:
-        Tuple[str, str, Table]:
-            A tuple containing the LaTeX table string, HTML table string,
-            and rich Table object.
-
-    Examples:
-        >>> json_data = {
-        ...     "simulated_model1-123": {"run_name": "simulated_model1-123", "-ELBO": -7.339, "MAE": 1.094},
-        ...     "simulated_model2-456": {"run_name": "simulated_model2-456", "-ELBO": -7.512, "MAE": 1.123},
-        ...     "pancreas_model1-789": {"run_name": "pancreas_model1-789", "-ELBO": -8.234, "MAE": 0.987},
-        ...     "pancreas_model2-012": {"run_name": "pancreas_model2-012", "-ELBO": -8.123, "MAE": 0.998}
-        ... }
-        >>> latex, html, markdown, rich_table = generate_tables(json_data)
-    """
-
-    dataset_metrics = defaultdict(lambda: defaultdict(dict))
-    for run_name, metrics in json_data.items():
-        match = re.match(r"(\w+)_(model\d+)-", run_name)
-        if match:
-            dataset, model = match.groups()
-            dataset_metrics[dataset][model]["-ELBO"] = metrics.get(
-                "-ELBO", "N/A"
-            )
-            dataset_metrics[dataset][model]["MAE"] = metrics.get("MAE", "N/A")
-
-    data = []
-    for dataset, models in dataset_metrics.items():
-        row = {"Dataset": dataset}
-        for model in ["model1", "model2"]:
-            if model in models:
-                row[f"{model.capitalize()} -ELBO"] = models[model].get(
-                    "-ELBO", "N/A"
-                )
-                row[f"{model.capitalize()} MAE"] = models[model].get(
-                    "MAE", "N/A"
-                )
-            else:
-                row[f"{model.capitalize()} -ELBO"] = "N/A"
-                row[f"{model.capitalize()} MAE"] = "N/A"
-        data.append(row)
-
-    df = pd.DataFrame(data)
-
-    df.columns = pd.MultiIndex.from_tuples(
-        [
-            ("Dataset", ""),
-            ("Model 1", "-ELBO"),
-            ("Model 1", "MAE"),
-            ("Model 2", "-ELBO"),
-            ("Model 2", "MAE"),
-        ]
-    )
-
-    latex_table = df.to_latex(
-        index=False,
-        multicolumn=True,
-        multicolumn_format="c",
-        column_format="l|cc|cc",
-    )
-    latex_table = latex_table.replace("\\midrule", "\\midrule\n\\cline{2-5}")
-
-    html_table = """
-    <table border="1" class="dataframe">
-      <thead>
-        <tr>
-          <th>Dataset</th>
-          <th colspan="2">Model 1</th>
-          <th colspan="2">Model 2</th>
-        </tr>
-        <tr>
-          <th></th>
-          <th>-ELBO</th>
-          <th>MAE</th>
-          <th>-ELBO</th>
-          <th>MAE</th>
-        </tr>
-      </thead>
-      <tbody>
-        {rows}
-      </tbody>
-    </table>
-    """
-
-    rows = ""
-    for _, row in df.iterrows():
-        rows += f"""
-        <tr>
-          <td>{row[("Dataset", "")]}</td>
-          <td>{row[("Model 1", "-ELBO")]:.4f}</td>
-          <td>{row[("Model 1", "MAE")]:.4f}</td>
-          <td>{row[("Model 2", "-ELBO")]:.4f}</td>
-          <td>{row[("Model 2", "MAE")]:.4f}</td>
-        </tr>
-        """
-
-    html_table = html_table.format(rows=rows)
-
-    markdown_table = (
-        "| Dataset   | Model 1   |          | Model 2   |          |\n"
-    )
-    markdown_table += (
-        "|-----------|-----------|----------|-----------|----------|\n"
-    )
-    markdown_table += (
-        "|           | -ELBO     | MAE      | -ELBO     | MAE      |\n"
-    )
-    for _, row in df.iterrows():
-        markdown_table += (
-            f"| {row['Dataset', '']} "
-            f"| {row['Model 1', '-ELBO']:.4f} | {row['Model 1', 'MAE']:.4f} "
-            f"| {row['Model 2', '-ELBO']:.4f} | {row['Model 2', 'MAE']:.4f} |\n"
-        )
-
-    rich_table = Table(title="Model Comparison")
-    rich_table.add_column("Dataset", style="cyan", no_wrap=True)
-    rich_table.add_column("Model 1", style="magenta", no_wrap=True)
-    rich_table.add_column("", style="magenta", no_wrap=True)
-    rich_table.add_column("Model 2", style="green", no_wrap=True)
-    rich_table.add_column("", style="green", no_wrap=True)
-    rich_table.add_row("", "-ELBO", "MAE", "-ELBO", "MAE")
-
-    for _, row in df.iterrows():
-        rich_table.add_row(
-            row[("Dataset", "")],
-            f"{row[('Model 1', '-ELBO')]:.4f}"
-            if isinstance(row[("Model 1", "-ELBO")], float)
-            else str(row[("Model 1", "-ELBO")]),
-            f"{row[('Model 1', 'MAE')]:.4f}"
-            if isinstance(row[("Model 1", "MAE")], float)
-            else str(row[("Model 1", "MAE")]),
-            f"{row[('Model 2', '-ELBO')]:.4f}"
-            if isinstance(row[("Model 2", "-ELBO")], float)
-            else str(row[("Model 2", "-ELBO")]),
-            f"{row[('Model 2', 'MAE')]:.4f}"
-            if isinstance(row[("Model 2", "MAE")], float)
-            else str(row[("Model 2", "MAE")]),
-        )
-
-    console = Console()
-    console.print(latex_table)
-    console.print(html_table)
-    console.print(markdown_table)
-    console.print(rich_table)
-
-    return (
-        latex_table,
-        html_table,
-        markdown_table,
-        rich_table,
-    )
diff --git a/src/pyrovelocity/io/metrics.py b/src/pyrovelocity/io/metrics.py
new file mode 100644
index 000000000..cafc00576
--- /dev/null
+++ b/src/pyrovelocity/io/metrics.py
@@ -0,0 +1,585 @@
+import json
+import re
+from collections import defaultdict
+from datetime import timedelta
+from pathlib import Path
+from typing import Any, Dict
+
+import pandas as pd
+from beartype import beartype
+from beartype.typing import Any, Dict, List, Tuple
+from returns.result import Failure, Result, Success
+from rich.console import Console
+from rich.table import Table
+
+__all__ = [
+    "load_json",
+    "merge_json",
+    "combine_json_files",
+    "add_duration_to_run_info",
+    "generate_tables",
+    "generate_and_save_metric_tables",
+]
+
+
+@beartype
+def load_json(file_path: Path) -> Result[Dict[str, Any], Exception]:
+    """
+    Load JSON data from a file.
+
+    Args:
+        file_path: Path to the JSON file.
+
+    Returns:
+        Result[Dict[str, Any], Exception]: A result object containing the loaded JSON data or an exception.
+
+    Examples:
+        >>> import tempfile
+        >>> tmp = getfixture('tmp_path')  # Get a temporary directory path
+        >>> json_file = tmp / 'test.json'
+        >>> json_file.write_text('{"key": "value"}')  # Create a test JSON file
+        >>> result = load_json(json_file)
+        >>> print(f"Result: {result}")
+        Result: <Success: {'key': 'value'}>
+        >>> assert result.unwrap() == {"key": "value"}
+        >>> non_existent_file = tmp / 'non_existent.json'
+        >>> result = load_json(non_existent_file)
+        >>> print(f"Result: {result}")
+        Result: <Failure: ...
+    """
+    try:
+        with file_path.open("r") as file:
+            return Success(json.load(file))
+    except Exception as e:
+        return Failure(e)
+
+
+@beartype
+def merge_json(dict1: Dict[str, Any], dict2: Dict[str, Any]) -> Dict[str, Any]:
+    """
+    Merge two JSON dictionaries.
+
+    Args:
+        dict1: The first JSON dictionary.
+        dict2: The second JSON dictionary.
+
+    Returns:
+        Dict[str, Any]: The merged dictionary.
+
+    Examples:
+        >>> dict1 = {"a": 1, "b": 2}
+        >>> dict2 = {"b": 3, "c": 4}
+        >>> result = merge_json(dict1, dict2)
+        >>> print(result)
+        {'a': 1, 'b': 3, 'c': 4}
+        >>> assert result == {"a": 1, "b": 3, "c": 4}
+    """
+    return {**dict1, **dict2}
+
+
+@beartype
+def combine_json_files(
+    file1: str | Path,
+    file2: str | Path,
+    output_file: str | Path,
+) -> Result[None, Exception]:
+    """
+    Combine two JSON files and write the result to a new file.
+
+    Args:
+        file1: Path to the first JSON file.
+        file2: Path to the second JSON file.
+        output_file: Path to the output JSON file.
+
+    Returns:
+        Result[None, Exception]: A result object encapsulating success or failure.
+
+    Examples:
+        >>> tmp = getfixture('tmp_path')  # Get a temporary directory path
+        >>> file1 = tmp / 'file1.json'
+        >>> file2 = tmp / 'file2.json'
+        >>> output_file = tmp / 'combined.json'
+        >>> file1.write_text('{"a": 1, "b": 2}')
+        >>> file2.write_text('{"b": 3, "c": 4}')
+        >>> result = combine_json_files(file1, file2, output_file)
+        >>> print(f"Result: {result}")
+        Result: <Success: None>
+        >>> assert output_file.exists()
+        >>> with open(output_file, 'r') as f:
+        ...     content = json.load(f)
+        ...     assert content == {"a": 1, "b": 3, "c": 4}
+    """
+    try:
+        data1_result = load_json(Path(file1))
+        data2_result = load_json(Path(file2))
+
+        if isinstance(data1_result, Failure):
+            return data1_result
+        if isinstance(data2_result, Failure):
+            return data2_result
+
+        data1 = data1_result.unwrap()
+        data2 = data2_result.unwrap()
+
+        combined_data = merge_json(data1, data2)
+
+        with output_file.open("w") as file:
+            json.dump(combined_data, file, indent=2)
+
+        return Success(None)
+    except Exception as e:
+        return Failure(e)
+
+
+@beartype
+def add_duration_to_run_info(
+    file_path: Path | str = "metrics.json"
+) -> Result[None, Exception]:
+    """
+    Load a JSON file containing run information, add a duration field,
+    and overwrite the file with the updated information.
+
+    The duration is calculated as the difference between 'end_time' and 'start_time',
+    and is added as a new field in the format "HH:MM:SS".
+
+    Args:
+        file_path (Path | str): Path to the JSON file. Defaults to "metrics.json".
+
+    Returns:
+        Result[None, Exception]: A Result object containing None if successful,
+                                 or an Exception if an error occurred.
+
+    Examples:
+        >>> import tempfile
+        >>> from pathlib import Path
+        >>> # Create a temporary JSON file
+        >>> with tempfile.NamedTemporaryFile(mode='w', delete=False, suffix='.json') as temp_file:
+        ...     json.dump({
+        ...         "start_time": 1723744878851,
+        ...         "end_time": 1723744887595,
+        ...         "other_field": "value"
+        ...     }, temp_file)
+        ...     temp_file_path = Path(temp_file.name)
+        >>> # Test the function
+        >>> result = add_duration_to_run_info(temp_file_path)
+        >>> print(f"Result: {result}")
+        Result: <Success: None>
+        >>> # Verify the file was updated correctly
+        >>> with open(temp_file_path, 'r') as f:
+        ...     updated_data = json.load(f)
+        >>> print(f"Duration added: {updated_data['duration']}")
+        Duration added: 00:00:08
+        >>> assert 'duration' in updated_data
+        >>> assert updated_data['duration'] == "00:00:08"
+        >>> assert updated_data['other_field'] == "value"
+        >>> # Clean up
+        >>> temp_file_path.unlink()
+
+        >>> # Test with non-existent file
+        >>> result = add_duration_to_run_info("non_existent.json")
+        >>> print(f"Result: {result}")
+        Result: <Failure: ...
+
+        >>> # Test with invalid JSON
+        >>> with tempfile.NamedTemporaryFile(mode='w', delete=False, suffix='.json') as temp_file:
+        ...     temp_file.write("Invalid JSON")
+        ...     temp_file_path = Path(temp_file.name)
+        >>> result = add_duration_to_run_info(temp_file_path)
+        >>> print(f"Result: {result}")
+        Result: <Failure: ...
+        >>> # Clean up
+        >>> temp_file_path.unlink()
+    """
+    file_path = Path(file_path)
+
+    try:
+        with file_path.open("r") as file:
+            data: Dict[str, Any] = json.load(file)
+
+        start_time = data.get("start_time")
+        end_time = data.get("end_time")
+
+        if start_time is not None and end_time is not None:
+            duration = timedelta(milliseconds=end_time - start_time)
+            hours, remainder = divmod(duration.seconds, 3600)
+            minutes, seconds = divmod(remainder, 60)
+
+            data["duration"] = f"{hours:02d}:{minutes:02d}:{seconds:02d}"
+        else:
+            return Failure(
+                ValueError(
+                    "Unable to calculate duration. Start time or end time is missing."
+                )
+            )
+
+        with file_path.open("w") as file:
+            json.dump(data, file, indent=2)
+
+        return Success(None)
+
+    except Exception as e:
+        return Failure(e)
+
+
+@beartype
+def generate_metric_tables(
+    json_data: Dict[str, Any], separate: bool = False
+) -> Tuple[Dict[str, Dict[str, str] | str], Dict[str, Table]]:
+    """
+    Generate LaTeX, HTML, and Markdown tables for ELBO and MAE metrics from the provided JSON data.
+
+    This function can generate either combined tables (ELBO and MAE together) or separate tables for each metric.
+
+    Args:
+        json_data (Dict[str, Any]): The JSON data containing metrics for different datasets and models.
+        separate (bool): If True, generate separate tables for ELBO and MAE. If False, generate combined tables. Default is False.
+
+    Returns:
+        Tuple[Dict[str, str], Dict[str, Table]]: A tuple containing two dictionaries:
+            1. A dictionary with keys 'latex', 'html', and 'markdown', each containing the respective table strings.
+            2. A dictionary with rich Table objects.
+
+    Examples:
+        >>> json_data = {
+        ...     "simulated_model1-123": {"run_name": "simulated_model1-123", "-ELBO": -7.339, "MAE": 1.094},
+        ...     "simulated_model2-456": {"run_name": "simulated_model2-456", "-ELBO": -7.512, "MAE": 1.123},
+        ...     "pancreas_model1-789": {"run_name": "pancreas_model1-789", "-ELBO": -8.234, "MAE": 0.987},
+        ...     "pancreas_model2-012": {"run_name": "pancreas_model2-012", "-ELBO": -8.123, "MAE": 0.998}
+        ... }
+        >>> table_strings, rich_tables = generate_metric_tables(json_data)
+        >>> separate_table_strings, separate_rich_tables = generate_metric_tables(json_data, separate=True)
+    """
+    dataset_metrics = process_json_data(json_data)
+
+    if separate:
+        return generate_separate_tables(dataset_metrics)
+    else:
+        return generate_combined_tables(dataset_metrics)
+
+
+def process_json_data(
+    json_data: Dict[str, Any]
+) -> Dict[str, Dict[str, Dict[str, float]]]:
+    dataset_metrics = defaultdict(lambda: defaultdict(dict))
+    for run_name, metrics in json_data.items():
+        match = re.match(r"(\w+)_(model\d+)-", run_name)
+        if match:
+            dataset, model = match.groups()
+            dataset = dataset.replace("_", " ")
+            if "simulated" in dataset.lower():
+                continue
+            dataset_metrics[dataset][model]["ELBO"] = metrics.get(
+                "-ELBO", "N/A"
+            )
+            dataset_metrics[dataset][model]["MAE"] = metrics.get("MAE", "N/A")
+    return dataset_metrics
+
+
+def generate_combined_tables(
+    dataset_metrics: Dict[str, Dict[str, Dict[str, float]]]
+) -> Tuple[Dict[str, str], Dict[str, Table]]:
+    data = []
+    for dataset, models in dataset_metrics.items():
+        row = {"Dataset": dataset}
+        for model in ["model1", "model2"]:
+            formatted_model = format_model_name(model)
+            if model in models:
+                row[f"{formatted_model} ELBO"] = models[model].get(
+                    "ELBO", "N/A"
+                )
+                row[f"{formatted_model} MAE"] = models[model].get("MAE", "N/A")
+            else:
+                row[f"{formatted_model} ELBO"] = "N/A"
+                row[f"{formatted_model} MAE"] = "N/A"
+        data.append(row)
+
+    df = pd.DataFrame(data)
+
+    latex_table = generate_latex_table(df)
+    html_table = generate_html_table(df)
+    markdown_table = generate_markdown_table(df)
+    rich_table = generate_rich_table(df)
+
+    console = Console()
+    console.print(rich_table)
+
+    return {
+        "latex": latex_table,
+        "html": html_table,
+        "markdown": markdown_table,
+    }, {"combined": rich_table}
+
+
+def format_model_name(model: str) -> str:
+    return f"Model {model[-1]}"
+
+
+def generate_separate_tables(
+    dataset_metrics: Dict[str, Dict[str, Dict[str, float]]]
+) -> Tuple[Dict[str, Dict[str, str]], Dict[str, Table]]:
+    data_elbo, data_mae = [], []
+    for dataset, models in dataset_metrics.items():
+        row_elbo = {"Dataset": dataset}
+        row_mae = {"Dataset": dataset}
+        for model in ["model1", "model2"]:
+            formatted_model = format_model_name(model)
+            if model in models:
+                row_elbo[formatted_model] = models[model].get("ELBO", "N/A")
+                row_mae[formatted_model] = models[model].get("MAE", "N/A")
+            else:
+                row_elbo[formatted_model] = "N/A"
+                row_mae[formatted_model] = "N/A"
+        data_elbo.append(row_elbo)
+        data_mae.append(row_mae)
+
+    df_elbo = pd.DataFrame(data_elbo)
+    df_mae = pd.DataFrame(data_mae)
+
+    latex_elbo = generate_latex_table(df_elbo, separate=True)
+    latex_mae = generate_latex_table(df_mae, separate=True)
+    html_elbo = generate_html_table(df_elbo, separate=True)
+    html_mae = generate_html_table(df_mae, separate=True)
+    markdown_elbo = generate_markdown_table(df_elbo, separate=True)
+    markdown_mae = generate_markdown_table(df_mae, separate=True)
+    rich_table_elbo = generate_rich_table(
+        df_elbo, title="ELBO Comparison", separate=True
+    )
+    rich_table_mae = generate_rich_table(
+        df_mae, title="MAE Comparison", separate=True
+    )
+
+    console = Console()
+    console.print(rich_table_elbo)
+    console.print(rich_table_mae)
+
+    return {
+        "latex": {"ELBO": latex_elbo, "MAE": latex_mae},
+        "html": {"ELBO": html_elbo, "MAE": html_mae},
+        "markdown": {"ELBO": markdown_elbo, "MAE": markdown_mae},
+    }, {"ELBO": rich_table_elbo, "MAE": rich_table_mae}
+
+
+def wrap_latex_table(table_content: str) -> str:
+    """
+    Wrap the LaTeX table content in a complete LaTeX document structure.
+
+    Args:
+        table_content (str): The LaTeX table content generated by df.to_latex()
+
+    Returns:
+        str: The complete LaTeX document including the table
+    """
+    latex_document = r"""
+\documentclass[border=2pt]{standalone}
+\usepackage{booktabs}
+\renewcommand{\familydefault}{\sfdefault}
+
+\begin{document}
+
+%s
+\end{document}
+"""
+    return latex_document % table_content
+
+
+def generate_latex_table(df: pd.DataFrame, separate: bool = False) -> str:
+    if separate:
+        table_content = df.to_latex(index=False, column_format="l|cc")
+    else:
+        table_content = df.to_latex(index=False, column_format="l|cc|cc")
+    # return wrap_latex_table(table_content)
+    return table_content
+
+
+def generate_html_table(df: pd.DataFrame, separate: bool = False) -> str:
+    if separate:
+        header = """
+        <table border="1" class="dataframe">
+          <thead>
+            <tr style="text-align: right;">
+              <th>Dataset</th>
+              <th>Model 1</th>
+              <th>Model 2</th>
+            </tr>
+          </thead>
+          <tbody>
+        """
+    else:
+        header = """
+        <table border="1" class="dataframe">
+          <thead>
+            <tr>
+              <th>Dataset</th>
+              <th colspan="2">Model 1</th>
+              <th colspan="2">Model 2</th>
+            </tr>
+            <tr>
+              <th></th>
+              <th>ELBO</th>
+              <th>MAE</th>
+              <th>ELBO</th>
+              <th>MAE</th>
+            </tr>
+          </thead>
+          <tbody>
+        """
+
+    body = generate_html_rows(df, separate)
+    footer = """
+          </tbody>
+        </table>
+        """
+    return header + body + footer
+
+
+def generate_markdown_table(df: pd.DataFrame, separate: bool = False) -> str:
+    if separate:
+        header = "| Dataset   | Model 1 | Model 2 |\n|-----------|---------|----------|\n"
+    else:
+        header = "| Dataset   | Model 1 ELBO | Model 1 MAE | Model 2 ELBO | Model 2 MAE |\n|-----------|--------------|-------------|--------------|-------------|\n"
+
+    body = generate_markdown_rows(df, separate)
+    return header + body
+
+
+def generate_rich_table(
+    df: pd.DataFrame, title: str = "Model Comparison", separate: bool = False
+) -> Table:
+    rich_table = Table(title=title)
+    rich_table.add_column("Dataset", style="cyan", no_wrap=True)
+    if separate:
+        rich_table.add_column("Model 1", style="magenta")
+        rich_table.add_column("Model 2", style="green")
+        for _, row in df.iterrows():
+            rich_table.add_row(
+                row["Dataset"],
+                format_value(row["Model 1"]),
+                format_value(row["Model 2"]),
+            )
+    else:
+        rich_table.add_column("Model 1 ELBO", style="magenta")
+        rich_table.add_column("Model 1 MAE", style="magenta")
+        rich_table.add_column("Model 2 ELBO", style="green")
+        rich_table.add_column("Model 2 MAE", style="green")
+        for _, row in df.iterrows():
+            rich_table.add_row(
+                row["Dataset"],
+                format_value(row["Model 1 ELBO"]),
+                format_value(row["Model 1 MAE"]),
+                format_value(row["Model 2 ELBO"]),
+                format_value(row["Model 2 MAE"]),
+            )
+    return rich_table
+
+
+def generate_html_rows(df: pd.DataFrame, separate: bool = False) -> str:
+    rows = ""
+    for _, row in df.iterrows():
+        if separate:
+            rows += f"""
+            <tr>
+              <td>{row['Dataset']}</td>
+              <td>{format_value(row['Model 1'])}</td>
+              <td>{format_value(row['Model 2'])}</td>
+            </tr>
+            """
+        else:
+            rows += f"""
+            <tr>
+              <td>{row['Dataset']}</td>
+              <td>{format_value(row['Model 1 ELBO'])}</td>
+              <td>{format_value(row['Model 1 MAE'])}</td>
+              <td>{format_value(row['Model 2 ELBO'])}</td>
+              <td>{format_value(row['Model 2 MAE'])}</td>
+            </tr>
+            """
+    return rows
+
+
+def generate_markdown_rows(df: pd.DataFrame, separate: bool = False) -> str:
+    rows = ""
+    for _, row in df.iterrows():
+        if separate:
+            rows += f"| {row['Dataset']} | {format_value(row['Model 1'])} | {format_value(row['Model 2'])} |\n"
+        else:
+            rows += f"| {row['Dataset']} | {format_value(row['Model 1 ELBO'])} | {format_value(row['Model 1 MAE'])} | {format_value(row['Model 2 ELBO'])} | {format_value(row['Model 2 MAE'])} |\n"
+    return rows
+
+
+def format_value(value: Any) -> str:
+    return f"{value:.4f}" if isinstance(value, float) else str(value)
+
+
+@beartype
+def generate_and_save_metric_tables(
+    json_data: Dict[str, Any],
+    output_dir: Path = Path("reports"),
+) -> List[Path]:
+    """
+    Generate and save tables for ELBO, MAE, and combined metrics in LaTeX, HTML, and Markdown formats.
+
+    This function uses the `generate_metric_tables` function to create both separate and combined tables
+    for different metrics and formats, then saves them to files in the specified output directory.
+
+    Args:
+        json_data (Dict[str, Any]): The input JSON data containing metrics for different datasets and models.
+        output_dir (Path): The directory where the output files will be saved.
+
+    Returns:
+        None
+
+    Raises:
+        IOError: If there's an error writing to the output files.
+
+    Example:
+        >>> json_data = {
+        ...     "simulated_model1-123": {"run_name": "simulated_model1-123", "-ELBO": -7.339, "MAE": 1.094},
+        ...     "simulated_model2-456": {"run_name": "simulated_model2-456", "-ELBO": -7.512, "MAE": 1.123},
+        ...     "pancreas_model1-789": {"run_name": "pancreas_model1-789", "-ELBO": -8.234, "MAE": 0.987},
+        ...     "pancreas_model2-012": {"run_name": "pancreas_model2-012", "-ELBO": -8.123, "MAE": 0.998}
+        ... }
+        >>> output_dir = Path("output")
+        >>> generate_and_save_metric_tables(json_data, output_dir)
+    """
+    output_dir.mkdir(parents=True, exist_ok=True)
+
+    json_file = output_dir / "input_metrics.json"
+    with json_file.open("w") as f:
+        json.dump(json_data, f, indent=2)
+
+    separate_table_strings, _ = generate_metric_tables(json_data, separate=True)
+
+    combined_table_strings, _ = generate_metric_tables(
+        json_data, separate=False
+    )
+
+    files_to_save = {
+        "combined_table.html": combined_table_strings["html"],
+        "combined_table.tex": wrap_latex_table(
+            combined_table_strings["latex"],
+        ),
+        "combined_table.md": combined_table_strings["markdown"],
+        "elbo_table.html": separate_table_strings["html"]["ELBO"],
+        "elbo_table.tex": wrap_latex_table(
+            separate_table_strings["latex"]["ELBO"],
+        ),
+        "elbo_table.md": separate_table_strings["markdown"]["ELBO"],
+        "mae_table.html": separate_table_strings["html"]["MAE"],
+        "mae_table.tex": wrap_latex_table(
+            separate_table_strings["latex"]["MAE"],
+        ),
+        "mae_table.md": separate_table_strings["markdown"]["MAE"],
+    }
+
+    output_file_paths = [json_file]
+    for file_name, content in files_to_save.items():
+        file_path = output_dir / file_name
+        try:
+            with file_path.open("w") as f:
+                f.write(content)
+            output_file_paths.append(file_path)
+        except IOError as e:
+            raise IOError(f"Error writing to file {file_path}: {e}")
+
+    print(f"All tables have been generated and saved in {output_dir}")
+    return output_file_paths
diff --git a/src/pyrovelocity/io/serialization.py b/src/pyrovelocity/io/serialization.py
index b8dfd04ad..4fcf29cf3 100644
--- a/src/pyrovelocity/io/serialization.py
+++ b/src/pyrovelocity/io/serialization.py
@@ -9,7 +9,13 @@
 from beartype.typing import Any, Dict
 from scipy import sparse
 
-from pyrovelocity.utils import ensure_numpy_array
+from pyrovelocity.io.hash import hash_file
+from pyrovelocity.utils import (
+    configure_logging,
+    ensure_numpy_array,
+    pretty_log_dict,
+    pretty_print_dict,
+)
 
 __all__ = [
     "serialize_anndata",
@@ -19,6 +25,8 @@
     "create_sample_anndata",
 ]
 
+logger = configure_logging(__name__)
+
 
 @beartype
 def serialize_anndata(adata: AnnData | AnnDataRaw) -> Dict[str, Any]:
@@ -76,6 +84,11 @@ def serialize_anndata(adata: AnnData | AnnDataRaw) -> Dict[str, Any]:
     if adata.raw is not None:
         serialized["raw"] = serialize_anndata(adata.raw)
 
+    logger.debug(
+        "\nSerializing AnnData object from dictionary:\n\n"
+        f"{pretty_log_dict(serialized)}\n\n"
+    )
+
     return serialized
 
 
@@ -135,9 +148,30 @@ def deserialize_anndata(data: Dict[str, Any]) -> AnnData | AnnDataRaw:
 
     if "uns" in data:
         adata_dict["uns"] = data["uns"]
+        for key in [
+            "clusters_coarse_colors",
+            "clusters_colors",
+            "day_colors",
+            "velocity_graph",
+            "velocity_graph_neg",
+        ]:
+            if key in adata_dict["uns"] and isinstance(
+                adata_dict["uns"][key], list
+            ):
+                adata_dict["uns"][key] = np.array(adata_dict["uns"][key])
 
     adata = AnnData(**adata_dict)
 
+    category_columns = ["clusters", "clusters_coarse", "leiden"]
+    for col in category_columns:
+        if col in adata.obs.columns:
+            adata.obs[col] = adata.obs[col].astype("category")
+
+    if "highly_variable_genes" in adata.var.columns:
+        adata.var["highly_variable_genes"] = adata.var[
+            "highly_variable_genes"
+        ].astype("category")
+
     if "raw" in data:
         raw_data = deserialize_anndata(data["raw"])
         if isinstance(raw_data, AnnData):
@@ -152,31 +186,71 @@ def deserialize_anndata(data: Dict[str, Any]) -> AnnData | AnnDataRaw:
 def save_anndata_to_json(
     adata: AnnData,
     filename: str | Path,
-) -> None:
+    expected_hash: str | None = None,
+) -> str:
     """
     Save an AnnData object to a JSON file.
 
     Args:
         adata: AnnData object to save
         filename: Name of the JSON file to save to
+        expected_hash: Optional hash to validate against
+
+    Returns:
+        SHA-256 hash of the saved file
     """
+    filename = Path(filename)
     adata_dict = serialize_anndata(adata)
-    with open(filename, "w") as f:
+
+    with filename.open("w") as f:
         json.dump(adata_dict, f, indent=4, cls=NumpyEncoder)
 
+    file_hash = hash_file(filename)
+    logger.info(f"\nSaved file: {filename}\nSHA-256 hash: {file_hash}\n")
+
+    if expected_hash is not None:
+        if file_hash == expected_hash:
+            logger.info("Hash validation succeeded.")
+        else:
+            logger.warning(
+                f"\nHash mismatch.\n"
+                f"Expected: {expected_hash},\n"
+                f"Actual: {file_hash}\n\n"
+            )
+
+    return file_hash
+
 
 @beartype
-def load_anndata_from_json(filename: str | Path) -> AnnData:
+def load_anndata_from_json(
+    filename: str | Path,
+    expected_hash: str | None = None,
+) -> AnnData:
     """
     Load an AnnData object from a JSON file.
 
     Args:
         filename: Name of the JSON file to load from
+        expected_hash: Optional hash to validate against
 
     Returns:
         Reconstructed AnnData object
     """
-    with open(filename, "r") as f:
+    filename = Path(filename)
+    file_hash = hash_file(filename)
+    logger.info(f"\nLoading file: {filename}\nSHA-256 hash: {file_hash}\n\n")
+
+    if expected_hash is not None:
+        if file_hash == expected_hash:
+            logger.info("Hash validation succeeded.")
+        else:
+            logger.warning(
+                f"\nHash mismatch.\n"
+                f"Expected: {expected_hash}\n"
+                f"Actual: {file_hash}\n\n"
+            )
+
+    with filename.open("r") as f:
         adata_dict = json.load(f)
     return deserialize_anndata(adata_dict)
 
@@ -225,6 +299,8 @@ def default(self, obj):
             return obj.to_dict(orient="list")
         if isinstance(obj, pd.Series):
             return obj.to_dict()
+        elif isinstance(obj, np.bool_):
+            return bool(obj)
         return super().default(obj)
 
 
diff --git a/src/pyrovelocity/io/subset_data.py b/src/pyrovelocity/io/subset_data.py
new file mode 100644
index 000000000..c3cc3b798
--- /dev/null
+++ b/src/pyrovelocity/io/subset_data.py
@@ -0,0 +1,243 @@
+from pathlib import Path
+from typing import Optional, Tuple
+
+import numpy as np
+import scanpy as sc
+from anndata import AnnData
+from beartype import beartype
+from scipy.sparse import csr_matrix
+
+from pyrovelocity.logging import configure_logging
+from pyrovelocity.utils import print_anndata
+
+__all__ = [
+    "subset_anndata",
+    "subset_vars",
+    "subset_randomly",
+    "subset_by_lineage_timepoints",
+    "update_uns_attributes",
+    "save_subset_to_file",
+]
+
+logger = configure_logging(__name__)
+
+
+@beartype
+def subset_anndata(
+    file_path: Optional[str | Path] = None,
+    adata: Optional[AnnData] = None,
+    n_obs: int = 100,
+    n_vars: Optional[int] = None,
+    save_subset: bool = False,
+    output_path: Optional[str | Path] = None,
+    use_lineage_timepoints: bool = False,
+) -> Tuple[AnnData, Optional[str | Path]]:
+    """
+    Randomly sample observations from a dataset given by file path or AnnData object.
+    If use_lineage_timepoints is True, it splits the observations equally between two timepoints.
+
+    Args:
+        file_path (str): Path to a .h5ad file containing a dataset. Takes precedence over adata.
+        adata (AnnData): AnnData object. If None, file_path must be provided.
+        n_obs (int): Number of observations to sample. Defaults to 100.
+        n_vars (Optional[int]): Number of variables to subset. If None, all variables are kept.
+        save_subset (bool): If True, save the subset to a file. Defaults to False.
+        output_path (str): Path to save the subset. Defaults to None.
+        use_lineage_timepoints (bool): If True, split observations equally between two timepoints. Defaults to False.
+
+    Raises:
+        ValueError: If neither file_path nor adata is provided.
+
+    Returns:
+        Tuple[AnnData, Optional[str | Path]]: Subset of the dataset and the output path if saved.
+
+    Examples:
+        >>> # xdoctest: +SKIP
+        >>> data_path = download_dataset(data_set_name="pancreas")
+        >>> adata = subset_anndata(file_path=data_path, n_obs=100, save_subset=True)
+        >>> print_anndata(adata)
+        >>> print_attributes(adata)
+        ...
+        >>> # use_lineage_timepoints=True
+        >>> from pyrovelocity.io.datasets import larry_cospar
+        >>> from pyrovelocity.io.serialization import save_anndata_to_json
+        >>> adata_cospar = larry_cospar()
+        >>> adata_cospar_subset, outpath = subset_anndata(
+        ...     adata=adata_cospar,
+        ...     n_obs=4,
+        ...     n_vars=2,
+        ...     use_lineage_timepoints=True,
+        ... )
+        >>> save_anndata_to_json(adata_cospar_subset, "adata_cospar_subset.json")
+    """
+    if file_path is not None:
+        file_path = Path(file_path)
+        adata = sc.read(file_path, cache=True)
+    if adata is None:
+        raise ValueError("Either file_path or adata must be provided")
+
+    logger.info("constructing data subset")
+    print_anndata(adata)
+
+    if n_vars is not None:
+        adata = subset_vars(adata, n_vars)
+
+    if use_lineage_timepoints:
+        adata_subset = subset_by_lineage_timepoints(adata, n_obs)
+    else:
+        adata_subset = subset_randomly(adata, n_obs)
+
+    adata_subset.obs_names_make_unique()
+    adata_subset.var_names_make_unique()
+
+    if save_subset:
+        output_path = save_subset_to_file(
+            adata_subset, file_path, output_path, n_obs
+        )
+
+    print_anndata(adata_subset)
+    return adata_subset.copy(), output_path
+
+
+@beartype
+def subset_vars(adata: AnnData, n_vars: int) -> AnnData:
+    """Subset variables of the AnnData object."""
+    if n_vars > adata.n_vars:
+        logger.warning(
+            f"n_vars ({n_vars}) is greater than the number of variables in the dataset ({adata.n_vars})"
+        )
+        n_vars = adata.n_vars
+    selected_vars_indices = np.random.choice(
+        adata.n_vars, n_vars, replace=False
+    )
+    logger.info(f"selected {n_vars} vars from {adata.n_vars}")
+    return adata[:, selected_vars_indices]
+
+
+def subset_randomly(adata: AnnData, n_obs: int) -> AnnData:
+    """Randomly subset observations of the AnnData object."""
+    if n_obs > adata.n_obs:
+        logger.warning(
+            f"n_obs ({n_obs}) is greater than the number of observations in the dataset ({adata.n_obs})"
+        )
+        n_obs = adata.n_obs
+    selected_obs_indices = np.random.choice(adata.n_obs, n_obs, replace=False)
+    logger.info(f"selected {n_obs} obs from {adata.n_obs}")
+    return adata[selected_obs_indices]
+
+
+@beartype
+def subset_by_lineage_timepoints(
+    adata: AnnData,
+    n_obs: int,
+    n_subset_pcs=5,
+) -> AnnData:
+    """Subset observations equally between two lineage timepoints.
+
+    Examples:
+        >>> # xdoctest: +SKIP
+        >>> data_path = download_dataset(data_set_name="pancreas")
+        >>> adata, _ = subset_anndata(file_path=data_path, n_obs=100, save_subset=True)
+        >>> adata_subset = subset_by_lineage_timepoints(adata, n_obs=10)
+        >>> print_anndata(adata
+    """
+    required_keys = ["clonal_cell_id_t1", "clonal_cell_id_t2"]
+    if not all(key in adata.uns for key in required_keys):
+        raise ValueError(
+            f"AnnData object is missing one or more required uns keys: {required_keys}"
+        )
+
+    n_obs_per_timepoint = n_obs // 2
+    t1_available = len(adata.uns["clonal_cell_id_t1"])
+    t2_available = len(adata.uns["clonal_cell_id_t2"])
+
+    if n_obs_per_timepoint > min(t1_available, t2_available):
+        n_obs_per_timepoint = min(t1_available, t2_available)
+        logger.warning(
+            f"Requested number of observations per timepoint ({n_obs // 2}) "
+            f"exceeds available observations. Using {n_obs_per_timepoint} per timepoint."
+        )
+
+    t1_indices = np.random.choice(
+        adata.uns["clonal_cell_id_t1"], n_obs_per_timepoint, replace=False
+    )
+    t2_indices = np.random.choice(
+        adata.uns["clonal_cell_id_t2"], n_obs_per_timepoint, replace=False
+    )
+    selected_obs_indices = np.concatenate([t1_indices, t2_indices])
+
+    logger.info(
+        f"selected {n_obs_per_timepoint} obs from each timepoint, total {len(selected_obs_indices)} obs"
+    )
+    adata_subset = adata[selected_obs_indices].copy()
+    update_uns_attributes(adata_subset, adata, t1_indices, t2_indices)
+    adata_subset.obsm["X_clone"] = np.eye(adata_subset.n_obs)
+    adata_subset.obsm["X_pca"] = adata_subset.obsm["X_pca"][:, :n_subset_pcs]
+    adata_subset.varm["PCs"] = adata_subset.varm["PCs"][:, :n_subset_pcs]
+    adata_subset.uns["pca"]["variance"] = adata_subset.uns["pca"]["variance"][
+        :n_subset_pcs
+    ]
+    adata_subset.uns["pca"]["variance_ratio"] = adata_subset.uns["pca"][
+        "variance_ratio"
+    ][:n_subset_pcs]
+    return adata_subset
+
+
+@beartype
+def update_uns_attributes(
+    adata_subset: AnnData,
+    adata: AnnData,
+    t1_indices: np.ndarray,
+    t2_indices: np.ndarray,
+):
+    """Update uns attributes in the subsetted AnnData object."""
+    adata_subset.uns["clonal_cell_id_t1"] = np.arange(len(t1_indices))
+    adata_subset.uns["clonal_cell_id_t2"] = np.arange(
+        len(t1_indices), len(t1_indices) + len(t2_indices)
+    )
+    adata_subset.uns["Tmap_cell_id_t1"] = adata_subset.uns["clonal_cell_id_t1"]
+    adata_subset.uns["Tmap_cell_id_t2"] = adata_subset.uns["clonal_cell_id_t2"]
+
+    for key in ["intraclone_transition_map", "transition_map"]:
+        if key in adata.uns:
+            original_map = adata.uns[key]
+            new_map = csr_matrix((len(t1_indices), len(t2_indices)))
+
+            t1_mapping = {orig: new for new, orig in enumerate(t1_indices)}
+            t2_mapping = {orig: new for new, orig in enumerate(t2_indices)}
+
+            rows, cols = original_map.nonzero()
+            for row, col in zip(rows, cols):
+                if row in t1_mapping and col in t2_mapping:
+                    new_row = t1_mapping[row]
+                    new_col = t2_mapping[col]
+                    new_map[new_row, new_col] = original_map[row, col]
+
+            adata_subset.uns[key] = new_map.toarray()
+
+    for key in adata.uns:
+        if key not in adata_subset.uns:
+            adata_subset.uns[key] = adata.uns[key]
+    adata_subset.uns["sp_idx"] = np.ones(adata_subset.shape[0]).astype(bool)
+
+
+@beartype
+def save_subset_to_file(
+    adata_subset: AnnData,
+    file_path: Optional[Path],
+    output_path: Optional[str | Path],
+    n_obs: int,
+) -> Path:
+    """Save the subsetted AnnData object to a file."""
+    if output_path is None and file_path is not None:
+        output_path = file_path.parent / Path(
+            f"{file_path.stem}_{n_obs}obs{file_path.suffix}"
+        )
+    if output_path is None:
+        raise ValueError(
+            "output_path must be provided if save_subset is True and file_path is None"
+        )
+    output_path = Path(output_path)
+    adata_subset.write(output_path)
+    logger.info(f"saved {n_obs} obs subset: {output_path}")
+    return output_path
diff --git a/src/pyrovelocity/models/_velocity.py b/src/pyrovelocity/models/_velocity.py
index 4022bc246..b806c06b3 100644
--- a/src/pyrovelocity/models/_velocity.py
+++ b/src/pyrovelocity/models/_velocity.py
@@ -503,13 +503,11 @@ def compute_statistics_from_posterior_samples(
         ]
 
         gene_ranking, genes = compute_volcano_data(
-            [posterior_samples], [adata], time_correlation_with="st"
-        )
-        gene_ranking = (
-            gene_ranking.sort_values("mean_mae", ascending=False)
-            .head(300)
-            .sort_values("time_correlation", ascending=False)
+            posterior_samples, adata, time_correlation_with="st"
         )
+        gene_ranking = gene_ranking.sort_values(
+            "mean_mae", ascending=False
+        ).sort_values("time_correlation", ascending=False)
         posterior_samples["gene_ranking"] = gene_ranking
         posterior_samples[
             "original_spaces_embeds_magnitude"
@@ -526,6 +524,8 @@ def compute_statistics_from_posterior_samples(
         posterior_samples["embeds_angle"] = embeds_radian
         posterior_samples["ut_mean"] = posterior_samples["ut"].mean(0).squeeze()
         posterior_samples["st_mean"] = posterior_samples["st"].mean(0).squeeze()
+        posterior_samples["ut_std"] = posterior_samples["ut"].std(0).squeeze()
+        posterior_samples["st_std"] = posterior_samples["st"].std(0).squeeze()
 
         (
             pca_vector_field_posterior_samples,
diff --git a/src/pyrovelocity/models/_velocity_model.py b/src/pyrovelocity/models/_velocity_model.py
index 63ca3fd8a..ddc74acb2 100644
--- a/src/pyrovelocity/models/_velocity_model.py
+++ b/src/pyrovelocity/models/_velocity_model.py
@@ -1,28 +1,19 @@
-from typing import Optional
-from typing import Tuple
-from typing import Union
+from typing import Optional, Tuple, Union
 
 import pyro
 import torch
 from beartype import beartype
-from jaxtyping import Float
-from jaxtyping import jaxtyped
+from jaxtyping import Float, jaxtyped
 from pyro import poutine
-from pyro.distributions import Bernoulli
-from pyro.distributions import LogNormal
-from pyro.distributions import Normal
-from pyro.distributions import Poisson
-from pyro.nn import PyroModule
-from pyro.nn import PyroSample
+from pyro.distributions import Bernoulli, LogNormal, Normal, Poisson
+from pyro.nn import PyroModule, PyroSample
 from pyro.primitives import plate
 from scvi.nn import Decoder
-from torch.nn.functional import relu
-from torch.nn.functional import softplus
+from torch.nn.functional import relu, softplus
 
 from pyrovelocity.logging import configure_logging
 from pyrovelocity.models._transcription_dynamics import mrna_dynamics
 
-
 logger = configure_logging(__name__)
 
 RNAInputType = Union[
@@ -83,9 +74,11 @@ def __init__(
         Args:
             num_cells (int): number of cells.
             num_genes (int): number of genes.
-            likelihood (str, optional): Type of likelihood to use. Defaults to "Poisson".
+            likelihood (str, optional):
+                Type of likelihood to use. Defaults to "Poisson".
             plate_size (int, optional): Number of plates. Defaults to 2.
-            correct_library_size (Union[bool, str], optional): Flag to control normalization by library size. Defaults to True.
+            correct_library_size (Union[bool, str], optional):
+                Flag to control normalization by library size. Defaults to True.
         """
         logger.info("Initializing LogNormalModel")
         assert num_cells > 0 and num_genes > 0
@@ -130,7 +123,7 @@ def create_plates(
         Create cell and gene plates for the model. note that usage in
         pyro.infer.autoguide.AutoGuideList requires the set of arguments be
         equivalent to the model's forward method. See
-        https://github.com/pyro-ppl/pyro/blob/670e9cb57003b52aea02deeb1316a42db3e1de1f/pyro/infer/autoguide/guides.py#L60-L63.
+        https://github.com/pyro-ppl/pyro/blob/1.9.1/pyro/infer/autoguide/guides.py#L60-L63
 
         Args:
             u_obs (Optional[torch.Tensor], optional): _description_. Defaults to None.
@@ -174,10 +167,12 @@ def u_scale(self):
     def s_scale(self):
         return self._pyrosample_helper(0.1)
 
+    # TODO: remove unused
     @PyroSample
     def u_inf(self):
         return self._pyrosample_helper(0.1)
 
+    # TODO: remove unused
     @PyroSample
     def s_inf(self):
         return self._pyrosample_helper(0.1)
@@ -186,14 +181,17 @@ def s_inf(self):
     def dt_switching(self):
         return self._pyrosample_helper(1.0)
 
+    # TODO: remove unused
     @PyroSample
     def gene_offset(self):
         return Normal(self.zero, self.one)
 
+    # TODO: remove unused
     @PyroSample
     def t_scale(self):
         return Normal(self.zero, self.one * 0.1)
 
+    # TODO: remove unused
     @PyroSample
     def latent_time(self):
         if self.shared_time & self.plate_size == 2:
@@ -205,6 +203,7 @@ def latent_time(self):
                 .to_event(1)
             )
 
+    # TODO: remove unused
     @PyroSample
     def cell_time(self):
         if self.plate_size == 2 and self.shared_time:
@@ -215,12 +214,13 @@ def cell_time(self):
     def _pyrosample_helper(self, scale: float):
         if self.plate_size == 2:
             return LogNormal(self.zero, self.one * scale)
-        return (
-            LogNormal(self.zero, self.one * 0.1)
-            .expand((self.num_genes,))
-            .to_event(1)
-            .mask(False)
-        )
+        else:
+            return (
+                LogNormal(self.zero, self.one * 0.1)
+                .expand((self.num_genes,))
+                .to_event(1)
+                .mask(False)
+            )
 
     @beartype
     def get_likelihood(
@@ -244,19 +244,31 @@ def get_likelihood(
         Args:
             ut (torch.Tensor): Tensor representing unspliced transcripts.
             st (torch.Tensor): Tensor representing spliced transcripts.
-            u_log_library (Optional[torch.Tensor], optional): Log library tensor for unspliced transcripts. Defaults to None.
-            s_log_library (Optional[torch.Tensor], optional): Log library tensor for spliced transcripts. Defaults to None.
-            u_scale (Optional[torch.Tensor], optional): Scale tensor for unspliced transcripts. Defaults to None.
-            s_scale (Optional[torch.Tensor], optional): Scale tensor for spliced transcripts. Defaults to None.
-            u_read_depth (Optional[torch.Tensor], optional): Read depth tensor for unspliced transcripts. Defaults to None.
-            s_read_depth (Optional[torch.Tensor], optional): Read depth tensor for spliced transcripts. Defaults to None.
-            u_cell_size_coef (Optional[Any], optional): Cell size coefficient for unspliced transcripts. Defaults to None.
-            ut_coef (Optional[Any], optional): Coefficient for unspliced transcripts. Defaults to None.
-            s_cell_size_coef (Optional[Any], optional): Cell size coefficient for spliced transcripts. Defaults to None.
-            st_coef (Optional[Any], optional): Coefficient for spliced transcripts. Defaults to None.
+            u_log_library (Optional[torch.Tensor], optional):
+                Log library tensor for unspliced transcripts. Defaults to None.
+            s_log_library (Optional[torch.Tensor], optional):
+                Log library tensor for spliced transcripts. Defaults to None.
+            u_scale (Optional[torch.Tensor], optional):
+                Scale tensor for unspliced transcripts. Defaults to None.
+            s_scale (Optional[torch.Tensor], optional):
+                Scale tensor for spliced transcripts. Defaults to None.
+            u_read_depth (Optional[torch.Tensor], optional):
+                Read depth tensor for unspliced transcripts. Defaults to None.
+            s_read_depth (Optional[torch.Tensor], optional):
+                Read depth tensor for spliced transcripts. Defaults to None.
+            u_cell_size_coef (Optional[Any], optional):
+                Cell size coefficient for unspliced transcripts. Defaults to None.
+            ut_coef (Optional[Any], optional):
+                Coefficient for unspliced transcripts. Defaults to None.
+            s_cell_size_coef (Optional[Any], optional):
+                Cell size coefficient for spliced transcripts. Defaults to None.
+            st_coef (Optional[Any], optional):
+                Coefficient for spliced transcripts. Defaults to None.
 
         Returns:
-            Tuple[Poisson, Poisson]: A tuple of Poisson distributions for unspliced and spliced transcripts, respectively.
+            Tuple[Poisson, Poisson]:
+                A tuple of Poisson distributions for unspliced and spliced transcripts,
+                respectively.
 
         Example:
             >>> import torch
@@ -271,7 +283,12 @@ def get_likelihood(
             >>> st = torch.rand(num_cells, num_genes)
             >>> u_read_depth = torch.rand(num_cells, 1)
             >>> s_read_depth = torch.rand(num_cells, 1)
-            >>> u_dist, s_dist = model.get_likelihood(ut, st, u_read_depth=u_read_depth, s_read_depth=s_read_depth)
+            >>> u_dist, s_dist = model.get_likelihood(
+            ...     ut,
+            ...     st,
+            ...     u_read_depth=u_read_depth,
+            ...     s_read_depth=s_read_depth,
+            ... )
             >>> logger.info(f"u_dist: {u_dist}")
             >>> logger.info(f"s_dist: {s_dist}")
             >>> assert isinstance(u_dist, torch.distributions.Poisson)
@@ -283,7 +300,6 @@ def get_likelihood(
                 "member of a sum type over supported distributions"
             )
             raise NotImplementedError(likelihood_not_implemented_msg)
-
         if self.correct_library_size:
             ut = relu(ut) + self.one * 1e-6
             st = relu(st) + self.one * 1e-6
@@ -409,7 +425,6 @@ def __init__(
             self.decoder = Decoder(1, self.num_genes, n_layers=2)
 
         self.enumeration = "parallel"
-        # self.set_enumeration_strategy()
 
     def sample_cell_gene_state(self, t, switching):
         return (
@@ -476,12 +491,16 @@ def get_rna(
             u0 (torch.Tensor): Unspliced RNA initial expression.
             s0 (torch.Tensor): Spliced RNA initial expression.
             t0 (torch.Tensor): Initial cell time.
-            switching (Optional[torch.Tensor], optional): Switching time. Default is None.
-            u_inf (Optional[torch.Tensor], optional): Unspliced RNA expression at switching time. Default is None.
-            s_inf (Optional[torch.Tensor], optional): Spliced RNA expression at switching time. Default is None.
+            switching (Optional[torch.Tensor], optional):
+                Switching time. Default is None.
+            u_inf (Optional[torch.Tensor], optional):
+                Unspliced RNA expression at switching time. Default is None.
+            s_inf (Optional[torch.Tensor], optional):
+                Spliced RNA expression at switching time. Default is None.
 
         Returns:
-            Tuple[torch.Tensor, torch.Tensor]: The unspliced (u) and spliced (s) RNA expression levels.
+            Tuple[torch.Tensor, torch.Tensor]:
+                The unspliced (u) and spliced (s) RNA expression levels.
 
 
         Examples:
@@ -524,14 +543,24 @@ def get_rna(
             >>> logger.info(f"u: {u}")
             >>> logger.info(f"s: {s}")
         """
-        state = self.sample_cell_gene_state(t, switching)
+        state = self.sample_cell_gene_state(
+            t=t,
+            switching=switching,
+        )
         alpha_off = self.zero
         u0_vec = torch.where(state, u0, u_inf)
         s0_vec = torch.where(state, s0, s_inf)
         alpha_vec = torch.where(state, alpha, alpha_off)
         tau = softplus(torch.where(state, t - t0, t - switching))
 
-        ut, st = mrna_dynamics(tau, u0_vec, s0_vec, alpha_vec, beta, gamma)
+        ut, st = mrna_dynamics(
+            tau=tau,
+            u0=u0_vec,
+            s0=s0_vec,
+            alpha=alpha_vec,
+            beta=beta,
+            gamma=gamma,
+        )
         ut = ut * u_scale / s_scale
         return ut, st
 
@@ -555,20 +584,33 @@ def forward(
         (s) RNA expression levels given the observations and model parameters.
 
         Args:
-            u_obs (Optional[torch.Tensor], optional): Observed unspliced RNA expression. Default is None.
-            s_obs (Optional[torch.Tensor], optional): Observed spliced RNA expression. Default is None.
-            u_log_library (Optional[torch.Tensor], optional): Log-transformed library size for unspliced RNA. Default is None.
-            s_log_library (Optional[torch.Tensor], optional): Log-transformed library size for spliced RNA. Default is None.
-            u_log_library_loc (Optional[torch.Tensor], optional): Mean of log-transformed library size for unspliced RNA. Default is None.
-            s_log_library_loc (Optional[torch.Tensor], optional): Mean of log-transformed library size for spliced RNA. Default is None.
-            u_log_library_scale (Optional[torch.Tensor], optional): Scale of log-transformed library size for unspliced RNA. Default is None.
-            s_log_library_scale (Optional[torch.Tensor], optional): Scale of log-transformed library size for spliced RNA. Default is None.
-            ind_x (Optional[torch.Tensor], optional): Indices for the cells. Default is None.
-            cell_state (Optional[torch.Tensor], optional): Cell state information. Default is None.
-            time_info (Optional[torch.Tensor], optional): Time information for the cells. Default is None.
+            u_obs (Optional[torch.Tensor], optional):
+                Observed unspliced RNA expression. Default is None.
+            s_obs (Optional[torch.Tensor], optional):
+                Observed spliced RNA expression. Default is None.
+            u_log_library (Optional[torch.Tensor], optional):
+                Log-transformed library size for unspliced RNA. Default is None.
+            s_log_library (Optional[torch.Tensor], optional):
+                Log-transformed library size for spliced RNA. Default is None.
+            u_log_library_loc (Optional[torch.Tensor], optional):
+                Mean of log-transformed library size for unspliced RNA. Default is None.
+            s_log_library_loc (Optional[torch.Tensor], optional):
+                Mean of log-transformed library size for spliced RNA. Default is None.
+            u_log_library_scale (Optional[torch.Tensor], optional):
+                Scale of log-transformed library size for unspliced RNA.
+                Default is None.
+            s_log_library_scale (Optional[torch.Tensor], optional):
+                Scale of log-transformed library size for spliced RNA. Default is None.
+            ind_x (Optional[torch.Tensor], optional):
+                Indices for the cells. Default is None.
+            cell_state (Optional[torch.Tensor], optional):
+                Cell state information. Default is None.
+            time_info (Optional[torch.Tensor], optional):
+                Time information for the cells. Default is None.
 
         Returns:
-            Tuple[torch.Tensor, torch.Tensor]: The unspliced (u) and spliced (s) RNA expression levels.
+            Tuple[torch.Tensor, torch.Tensor]:
+                The unspliced (u) and spliced (s) RNA expression levels.
 
         Examples:
             >>> import torch
@@ -613,17 +655,17 @@ def forward(
                     [ 0.,  0.,  7.,  4.]]))
         """
         cell_plate, gene_plate = self.create_plates(
-            u_obs,
-            s_obs,
-            u_log_library,
-            s_log_library,
-            u_log_library_loc,
-            s_log_library_loc,
-            u_log_library_scale,
-            s_log_library_scale,
-            ind_x,
-            cell_state,
-            time_info,
+            u_obs=u_obs,
+            s_obs=s_obs,
+            u_log_library=u_log_library,
+            s_log_library=s_log_library,
+            u_log_library_loc=u_log_library_loc,
+            s_log_library_loc=s_log_library_loc,
+            u_log_library_scale=u_log_library_scale,
+            s_log_library_scale=s_log_library_scale,
+            ind_x=ind_x,
+            cell_state=cell_state,
+            time_info=time_info,
         )
 
         with gene_plate, poutine.mask(mask=self.include_prior):
@@ -644,7 +686,12 @@ def forward(
 
             dt_switching = self.dt_switching
             u_inf, s_inf = mrna_dynamics(
-                dt_switching, u0, s0, alpha, beta, gamma
+                tau=dt_switching,
+                u0=u0,
+                s0=s0,
+                alpha=alpha,
+                beta=beta,
+                gamma=gamma,
             )
             u_inf = pyro.deterministic("u_inf", u_inf, event_dim=0)
             s_inf = pyro.deterministic("s_inf", s_inf, event_dim=0)
@@ -668,26 +715,26 @@ def forward(
             )
             with gene_plate:
                 ut, st = self.get_rna(
-                    u_scale,
-                    s_scale,
-                    alpha,
-                    beta,
-                    gamma,
-                    t,
-                    u0,
-                    s0,
-                    t0,
-                    switching,
-                    u_inf,
-                    s_inf,
+                    u_scale=u_scale,
+                    s_scale=s_scale,
+                    alpha=alpha,
+                    beta=beta,
+                    gamma=gamma,
+                    t=t,
+                    u0=u0,
+                    s0=s0,
+                    t0=t0,
+                    switching=switching,
+                    u_inf=u_inf,
+                    s_inf=s_inf,
                 )
                 u_dist, s_dist = self.get_likelihood(
-                    ut,
-                    st,
-                    u_log_library,
-                    s_log_library,
-                    u_scale,
-                    s_scale,
+                    ut=ut,
+                    st=st,
+                    u_log_library=u_log_library,
+                    s_log_library=s_log_library,
+                    u_scale=u_scale,
+                    s_scale=s_scale,
                     u_read_depth=u_read_depth,
                     s_read_depth=s_read_depth,
                     u_cell_size_coef=u_cell_size_coef,
diff --git a/src/pyrovelocity/plots/__init__.py b/src/pyrovelocity/plots/__init__.py
index 71358b98c..4021a376b 100644
--- a/src/pyrovelocity/plots/__init__.py
+++ b/src/pyrovelocity/plots/__init__.py
@@ -7,6 +7,9 @@
 )
 from pyrovelocity.plots._experimental import plot_t0_selection
 from pyrovelocity.plots._genes import plot_gene_ranking
+from pyrovelocity.plots._lineage_fate_correlation import (
+    plot_lineage_fate_correlation,
+)
 from pyrovelocity.plots._parameters import (
     plot_parameter_posterior_distributions,
 )
@@ -15,6 +18,7 @@
     posterior_curve,
 )
 from pyrovelocity.plots._rainbow import rainbowplot
+from pyrovelocity.plots._report import plot_report, save_subfigures
 from pyrovelocity.plots._summary import plot_gene_selection_summary
 from pyrovelocity.plots._time import (
     plot_posterior_time,
@@ -34,8 +38,10 @@
     "plot_deterministic_simulation_phase_portrait",
     "plot_deterministic_simulation_trajectories",
     "plot_gene_ranking",
+    "plot_lineage_fate_correlation",
     "plot_parameter_posterior_distributions",
     "plot_posterior_time",
+    "plot_report",
     "plot_shared_time_uncertainty",
     "plot_spliced_unspliced_histogram",
     "plot_state_uncertainty",
@@ -44,4 +50,5 @@
     "rainbowplot",
     "plot_vector_field_summary",
     "plot_gene_selection_summary",
+    "save_subfigures",
 ]
diff --git a/src/pyrovelocity/plots/_common.py b/src/pyrovelocity/plots/_common.py
index 2ecb5dc0c..d8c39e5a3 100644
--- a/src/pyrovelocity/plots/_common.py
+++ b/src/pyrovelocity/plots/_common.py
@@ -5,7 +5,6 @@
 
 from pyrovelocity.logging import configure_logging
 
-
 __all__ = ["set_colorbar", "set_font_size"]
 
 logger = configure_logging(__name__)
@@ -23,6 +22,7 @@ def set_colorbar(
     fig=None,
     position="right",
     rainbow=False,
+    axes_label=None,
 ):
     if position == "right" and (not rainbow):
         cax = inset_axes(ax, width="2%", height="30%", loc=4, borderpad=0)
@@ -31,6 +31,8 @@ def set_colorbar(
         divider = make_axes_locatable(ax)
         cax = divider.append_axes(position, size="8%", pad=0.08)
         cb = fig.colorbar(smp, cax=cax, orientation=orientation, shrink=0.4)
+    if axes_label:
+        cax.set_label(axes_label)
 
     cb.ax.tick_params(labelsize=labelsize)
 
diff --git a/src/pyrovelocity/plots/_genes.py b/src/pyrovelocity/plots/_genes.py
index bc0202020..e3de4ffab 100644
--- a/src/pyrovelocity/plots/_genes.py
+++ b/src/pyrovelocity/plots/_genes.py
@@ -1,17 +1,21 @@
 from pathlib import Path
 from typing import Dict, List, Optional, Tuple
 
+import adjustText
 import matplotlib.pyplot as plt
+import matplotlib.ticker as ticker
 import numpy as np
+import pandas
 import seaborn as sns
 from adjustText import adjust_text
 from anndata import AnnData
 from beartype import beartype
-from matplotlib import gridspec
+from beartype.typing import Any, Callable
 from matplotlib.axes import Axes
 from matplotlib.figure import FigureBase
+from matplotlib.gridspec import GridSpec, SubplotSpec
 from matplotlib.patches import ArrowStyle, ConnectionStyle
-from numpy import ndarray
+from numpy.typing import NDArray
 from pandas import DataFrame
 
 from pyrovelocity.analysis.analyze import compute_volcano_data
@@ -22,58 +26,100 @@
 logger = configure_logging(__name__)
 
 
+if hasattr(adjustText, "logger"):
+    ajusttext_warn = adjustText.logger.warn
+
+    def filter_adjusttext_matplotlib_warn(message, *args, **kwargs):
+        if "Looks like you are using an old matplotlib version" not in message:
+            ajusttext_warn(message, *args, **kwargs)
+
+    adjustText.logger.warn = filter_adjusttext_matplotlib_warn
+
+
 @beartype
 def plot_gene_ranking(
-    posterior_samples: List[Dict[str, ndarray]],
-    adata: List[AnnData],
+    posterior_samples: Dict[str, NDArray[Any] | DataFrame],
+    adata: AnnData,
+    fig: Optional[FigureBase] = None,
     ax: Optional[Axes] = None,
+    gs: Optional[SubplotSpec] = None,
     time_correlation_with: str = "s",
-    selected_genes: Optional[List[str]] = None,
+    putative_marker_genes: Optional[List[str]] = None,
+    selected_genes: List[str] = [""],
     assemble: bool = False,
     negative: bool = False,
     show_marginal_histograms: bool = False,
     save_volcano_plot: bool = False,
     volcano_plot_path: str | Path = "volcano.pdf",
+    defaultfontsize=7,
+    show_xy_labels: bool = False,
+    truncate_lower_mae_percentile: float = 0.0,
+    recompute_volcano_data: bool = False,
 ) -> Tuple[DataFrame, Optional[FigureBase]]:
-    if selected_genes is not None:
-        assert isinstance(selected_genes, (tuple, list))
-        assert isinstance(selected_genes[0], str)
-        volcano_data = posterior_samples[0]["gene_ranking"]
-        genes = selected_genes
-    elif "u" in posterior_samples[0]:
+    if putative_marker_genes is not None:
+        volcano_data: DataFrame = posterior_samples["gene_ranking"]
+        genes = putative_marker_genes
+    elif "u" in posterior_samples or recompute_volcano_data:
         volcano_data, genes = compute_volcano_data(
-            posterior_samples,
-            adata,
-            time_correlation_with,
-            selected_genes,
-            negative,
+            posterior_samples=posterior_samples,
+            adata=adata,
+            time_correlation_with=time_correlation_with,
+            selected_genes=putative_marker_genes,
+            negative=negative,
         )
     else:
-        volcano_data = posterior_samples[0]["gene_ranking"]
-        genes = posterior_samples[0]["genes"]
+        volcano_data: DataFrame = posterior_samples["gene_ranking"]
+        genes = posterior_samples["genes"]
 
+    volcano_data["percentile"] = volcano_data["mean_mae"].rank(
+        pct=True, ascending=False
+    )
+    volcano_data["inverted_percentile"] = 1 - volcano_data["percentile"]
+    volcano_mask = (
+        volcano_data["inverted_percentile"] >= truncate_lower_mae_percentile
+    )
     adjust_text_compatible = is_adjust_text_compatible()
-    fig = None
 
-    defaultfontsize = 7
     defaultdotsize = 3
-    plot_title = "Pyro-Velocity genes"
+    plot_title = (
+        r"Mean absolute error vs spliced correlation with shared time"
+        # r"$-$MAE vs $\rho(\hat{s},t)$"
+        # if matplotlib.rcParams["text.usetex"]
+        # else "-MAE vs ρ(s,t)"
+    )
 
     if show_marginal_histograms:
         time_corr_hist, time_corr_bins = np.histogram(
             volcano_data["time_correlation"], bins="auto", density=False
         )
         mean_mae_hist, mean_mae_bins = np.histogram(
-            volcano_data["mean_mae"], bins="auto", density=False
+            a=volcano_data["mean_mae"],
+            # a=volcano_data[volcano_mask]["inverted_percentile"],
+            bins="auto",
+            density=False,
         )
 
-        fig = plt.figure(figsize=(10, 10))
-        gs = gridspec.GridSpec(
-            3, 3, width_ratios=[2, 2, 1], height_ratios=[1, 2, 2]
-        )
-        ax_scatter = plt.subplot(gs[1:, :2])
-        ax_hist_x = plt.subplot(gs[0, :2])
-        ax_hist_y = plt.subplot(gs[1:, 2])
+        if gs is None:
+            fig = plt.figure(figsize=(10, 10))
+            gsi = GridSpec(
+                nrows=3,
+                ncols=3,
+                width_ratios=[2, 2, 1],
+                height_ratios=[1, 2, 2],
+            )
+        else:
+            gsi = gs.subgridspec(
+                nrows=3,
+                ncols=3,
+                width_ratios=[2, 2, 1],
+                height_ratios=[1, 2, 2],
+            )
+        ax_scatter = plt.subplot(gsi[1:, :2])
+        ax_scatter.set_label("gene_selection")
+        ax_hist_x = plt.subplot(gsi[0, :2])
+        ax_hist_x.set_label("gene_selection")
+        ax_hist_y = plt.subplot(gsi[1:, 2])
+        ax_hist_y.set_label("gene_selection")
 
         # time histogram
         ax_hist_x.bar(
@@ -97,12 +143,44 @@ def plot_gene_ranking(
         defaultdotsize = 12
         plot_title = ""
         ax = ax_scatter
+    else:
+        if gs is not None:
+            gsi = gs.subgridspec(
+                nrows=2,
+                ncols=1,
+                height_ratios=[0.05, 1],
+                hspace=0.05,
+                wspace=0.0,
+            )
+            title_ax = fig.add_subplot(gsi[0, :])
+            title_ax.axis("off")
+            title_ax.set_xticklabels([])
+            title_ax.set_label("gene_selection")
+            title_ax.text(
+                0.5,
+                0.5,
+                plot_title,
+                ha="center",
+                va="center",
+                fontsize=defaultfontsize + 1,
+                fontweight="bold",
+                transform=title_ax.transAxes,
+            )
+            ax = fig.add_subplot(gsi[1, :])
+            ax.set_label("gene_selection")
+        elif ax is None:
+            fig, ax = plt.subplots(figsize=(10, 10))
+            ax.set_label("gene_selection")
+        else:
+            ax.set_label("gene_selection")
 
+    ax.set_label("gene_selection")
     sns.scatterplot(
         x="time_correlation",
         y="mean_mae",
+        # y="inverted_percentile",
         hue="selected genes",
-        data=volcano_data,
+        data=volcano_data[volcano_mask],
         s=defaultdotsize,
         linewidth=0,
         ax=ax,
@@ -118,6 +196,7 @@ def plot_gene_ranking(
         volcano_data["mean_mae"].min(),
         volcano_data["mean_mae"].max(),
     )
+    # y_min, y_max = truncate_lower_mae_percentile, 1.0
 
     padding = 0.1
     x_range = (x_max - x_min) * padding
@@ -126,26 +205,52 @@ def plot_gene_ranking(
     ax.set_xlim(x_min - x_range, x_max + x_range)
     ax.set_ylim(y_min - y_range, y_max + y_range)
 
-    ax.set_title(plot_title, fontsize=defaultfontsize)
-    ax.set_xlabel(
-        "shared time correlation\nwith spliced expression",
-        fontsize=defaultfontsize,
-    )
-    ax.set_ylabel("negative mean\nabsolute error", fontsize=defaultfontsize)
-    sns.despine()
+    ax.set_xlabel("")
+    ax.set_ylabel("")
+
     ax.tick_params(labelsize=defaultfontsize - 1)
+    ax.tick_params(axis="x", top=False, which="both")
+    ax.tick_params(axis="y", right=False, which="both")
+
+    if show_xy_labels:
+        ax.set_xlabel(
+            "shared time correlation with spliced expression",
+            fontsize=defaultfontsize,
+        )
+        ax.set_ylabel("negative mean absolute error", fontsize=defaultfontsize)
+        # ax.set_ylabel(
+        #     "mean absolute error percentile", fontsize=defaultfontsize
+        # )
+    else:
+        yticks = ax.get_yticks()
+        logger.info(
+            f"\nyticks: {yticks}\n",
+        )
+        ax.set_yticklabels([], fontsize=defaultfontsize)
+        ax.tick_params(axis="y", direction="in", pad=-20, labelsize=4)
+        # ax.yaxis.set_major_formatter(
+        #     ticker.FuncFormatter(
+        #         percentile_formatter(truncate_lower_mae_percentile)
+        #     )
+        # )
+
+    sns.despine()
 
     texts = []
+    light_orange = "#ffb343"
+    dark_orange = "#ff6a14"
     for i, g in enumerate(genes):
         ax.scatter(
             volcano_data.loc[g, :].time_correlation,
+            # volcano_data.loc[g, :].inverted_percentile,
             volcano_data.loc[g, :].mean_mae,
             s=15,
-            color="red",
+            color=dark_orange if g in selected_genes else light_orange,
             marker="*",
         )
         new_text = ax.text(
             volcano_data.loc[g, :].time_correlation,
+            # volcano_data.loc[g, :].inverted_percentile,
             volcano_data.loc[g, :].mean_mae,
             g,
             fontsize=defaultfontsize - 1,
@@ -191,6 +296,19 @@ def plot_gene_ranking(
     return volcano_data, fig
 
 
+@beartype
+def percentile_formatter(
+    lower_percentile: float, upper_percentile: float = 1.0
+) -> Callable:
+    def custom_formatter(x, pos):
+        if abs(x - lower_percentile) < 1e-6 or abs(x - upper_percentile) < 1e-6:
+            return f"{round(abs(1-x),2):.1f}"
+        else:
+            return ""
+
+    return custom_formatter
+
+
 def is_adjust_text_compatible():
     """Check if the current backend supports adjust_text."""
     try:
diff --git a/src/pyrovelocity/plots/_lineage_fate_correlation.py b/src/pyrovelocity/plots/_lineage_fate_correlation.py
new file mode 100644
index 000000000..2a2e646a6
--- /dev/null
+++ b/src/pyrovelocity/plots/_lineage_fate_correlation.py
@@ -0,0 +1,514 @@
+from pathlib import Path
+
+import matplotlib
+import numpy as np
+import pandas as pd
+import scvelo as scv
+import seaborn as sns
+from anndata import AnnData
+from beartype import beartype
+from beartype.typing import Dict, List, Tuple
+from matplotlib.axes import Axes
+from matplotlib.figure import Figure
+from scipy.spatial import distance
+from scipy.stats import spearmanr
+
+from pyrovelocity.io.compressedpickle import CompressedPickle
+from pyrovelocity.logging import configure_logging
+from pyrovelocity.plots._time import plot_posterior_time
+from pyrovelocity.plots._trajectory import (
+    align_trajectory_diff,
+    get_clone_trajectory,
+)
+from pyrovelocity.plots._uncertainty import (
+    get_posterior_sample_angle_uncertainty,
+)
+from pyrovelocity.plots._vector_fields import plot_vector_field_uncertainty
+from pyrovelocity.utils import load_anndata_from_path
+
+__all__ = ["plot_lineage_fate_correlation"]
+
+logger = configure_logging(__name__)
+
+
+@beartype
+def plot_lineage_fate_correlation(
+    posterior_samples_path: str | Path | AnnData,
+    adata_pyrovelocity: str | Path | AnnData,
+    adata_cospar: str | Path | AnnData,
+    all_axes: List[Axes] | np.ndarray,
+    fig: Figure,
+    state_color_dict: Dict,
+    ylabel: str = "Monocyte lineage",
+    dotsize: int = 3,
+    scale: float = 0.35,
+    arrow: float = 3.5,
+    lineage_fate_correlation_path: str | Path = "lineage_fate_correlation.pdf",
+    save_plot: bool = True,
+    show_colorbars: bool = False,
+    show_titles: bool = False,
+    default_fontsize: int = 7,
+    default_title_padding: int = 2,
+    include_uncertainty_measures: bool = False,
+) -> List[Axes] | np.ndarray:
+    """
+    Plot lineage fate correlation with shared latent time estimates.
+
+    Args:
+        posterior_samples_path (str | Path): Path to the posterior samples.
+        adata_pyrovelocity (str | Path): Path to the Pyro-Velocity AnnData object.
+        adata_scvelo (str | Path): Path to the scVelo AnnData object.
+        adata_cospar (AnnData): AnnData object with COSPAR results.
+        ax (Axes): Matplotlib axes.
+        fig (Figure): Matplotlib figure.
+        state_color_dict (Dict): Dictionary with cell state colors.
+        ylabel (str, optional): Label for y axis. Defaults to "Unipotent Monocyte lineage".
+        dotsize (int, optional): Size of plotted points. Defaults to 3.
+        scale (float, optional): Plot scale. Defaults to 0.35.
+        arrow (float, optional): Arrow size. Defaults to 3.5.
+
+    Examples:
+        >>> # xdoctest: +SKIP
+        >>> import matplotlib.pyplot as plt
+        >>> import scanpy as sc
+        >>> from pyrovelocity.io.datasets import larry_cospar, larry_mono
+        >>> from pyrovelocity.utils import load_anndata_from_path
+        >>> from pyrovelocity.plots import plot_lineage_fate_correlation
+        ...
+        >>> fig, ax = plt.subplots(1, 9)
+        >>> fig.set_size_inches(17, 2.75)
+        >>> fig.subplots_adjust(
+        ...     hspace=0.4, wspace=0.2, left=0.01, right=0.99, top=0.95, bottom=0.3
+        >>> )
+        ...
+        >>> data_set_name = "larry_mono"
+        >>> model_name = "model2"
+        >>> data_set_model_pairing = f"{data_set_name}_{model_name}"
+        >>> model_path = f"models/{data_set_model_pairing}"
+        ...
+        >>> adata_pyrovelocity = load_anndata_from_path(f"{model_path}/postprocessed.h5ad")
+        >>> # color_dict = dict(
+        ... #     zip(
+        ... #         adata_pyrovelocity.obs.state_info.cat.categories,
+        ... #         adata_pyrovelocity.uns["state_info_colors"],
+        ... #     )
+        ... # )
+        >>> adata_dynamical = load_anndata_from_path(f"data/processed/larry_mono_processed.h5ad")
+        >>> adata_cospar = load_anndata_from_path(f"data/external/larry_cospar.h5ad")
+        >>> plot_lineage_fate_correlation(
+        ...     posterior_samples_path=f"{model_path}/pyrovelocity.pkl.zst",
+        ...     adata_pyrovelocity=adata_pyrovelocity,
+        ...     adata_scvelo=adata_dynamical,
+        ...     adata_cospar=adata_cospar,
+        ...     ax=ax,
+        ...     fig=fig,
+        ... )
+    """
+    posterior_samples = CompressedPickle.load(posterior_samples_path)
+    embed_mean = posterior_samples["vector_field_posterior_mean"]
+
+    if isinstance(adata_pyrovelocity, str | Path):
+        adata_pyrovelocity = load_anndata_from_path(adata_pyrovelocity)
+    if isinstance(adata_cospar, str | Path):
+        adata_cospar = load_anndata_from_path(adata_cospar)
+
+    adata_scvelo = adata_pyrovelocity.copy()
+    adata_input_clone = get_clone_trajectory(adata_scvelo)
+    adata_input_clone.obsm["clone_vector_emb"][
+        np.isnan(adata_input_clone.obsm["clone_vector_emb"])
+    ] = 0
+    density = 0.35
+    diff = align_trajectory_diff(
+        [adata_input_clone, adata_scvelo, adata_scvelo],
+        [
+            adata_input_clone.obsm["clone_vector_emb"],
+            adata_scvelo.obsm["velocity_emb"],
+            embed_mean,
+        ],
+        embed="emb",
+        density=density,
+    )
+    scvelo_cos = pd.DataFrame(diff).apply(
+        lambda x: 1 - distance.cosine(x[2:4], x[4:6]), axis=1
+    )
+    pyro_cos = pd.DataFrame(diff).apply(
+        lambda x: 1 - distance.cosine(x[2:4], x[6:8]), axis=1
+    )
+    scvelo_cos_mean = scvelo_cos.mean()
+    pyro_cos_mean = pyro_cos.mean()
+    logger.info(
+        f"\nscVelo cosine similarity: {scvelo_cos_mean:.2f}\n"
+        f"Pyro-Velocity cosine similarity: {pyro_cos_mean:.2f}\n\n"
+    )
+
+    current_axis_index = 0
+
+    # SHIFT AXIS INDEX
+    ax, current_axis_index = get_next_axis(all_axes, current_axis_index)
+
+    res = pd.DataFrame(
+        {
+            "X": adata_pyrovelocity.obsm["X_emb"][:, 0],
+            "Y": adata_pyrovelocity.obsm["X_emb"][:, 1],
+            "cell_type": adata_pyrovelocity.obs.state_info,
+        }
+    )
+    sns.scatterplot(
+        x="X",
+        y="Y",
+        data=res,
+        alpha=0.90,
+        s=dotsize,
+        linewidth=0,
+        edgecolor="none",
+        hue="cell_type",
+        palette=state_color_dict,
+        ax=ax,
+        legend="brief",
+    )
+    ax.get_legend().remove()
+    ax.axis("off")
+    if show_titles:
+        ax.set_title(
+            "Cell types",
+            fontsize=default_fontsize,
+            pad=default_title_padding,
+        )
+    else:
+        ax.set_title(
+            "",
+            fontsize=default_fontsize,
+            pad=default_title_padding,
+        )
+    ax.set_ylabel(ylabel, fontsize=default_fontsize)
+
+    # SHIFT AXIS INDEX
+    ax, current_axis_index = get_next_axis(all_axes, current_axis_index)
+    scv.pl.velocity_embedding_grid(
+        adata=adata_input_clone,
+        scale=scale,
+        autoscale=True,
+        show=False,
+        s=dotsize,
+        density=density,
+        arrow_size=arrow,
+        linewidth=1,
+        vkey="clone_vector",
+        basis="emb",
+        ax=ax,
+        title="",
+        color="gray",
+        arrow_color="black",
+        fontsize=default_fontsize,
+    )
+    ax.axis("off")
+    if show_titles:
+        ax.set_title(
+            "Clonal progression",
+            fontsize=default_fontsize,
+            pad=default_title_padding,
+        )
+
+    # SHIFT AXIS INDEX
+    ax, current_axis_index = get_next_axis(all_axes, current_axis_index)
+    scv.pl.velocity_embedding_grid(
+        adata=adata_scvelo,
+        show=False,
+        s=dotsize,
+        density=density,
+        scale=scale,
+        autoscale=True,
+        arrow_size=arrow,
+        linewidth=1,
+        basis="emb",
+        ax=ax,
+        title="",
+        fontsize=default_fontsize,
+        color="gray",
+        arrow_color="black",
+    )
+    ax.axis("off")
+    if show_titles:
+        ax.set_title(
+            # "scVelo cosine similarity: %.2f" % scvelo_cos_mean, fontsize=default_fontsize
+            f"scVelo ({scvelo_cos_mean:.2f})",
+            fontsize=default_fontsize,
+            pad=default_title_padding,
+        )
+    else:
+        ax.set_title(
+            # f""
+            f"({scvelo_cos_mean:.2f})",
+            fontsize=default_fontsize,
+            pad=default_title_padding,
+        )
+
+    # SHIFT AXIS INDEX
+    ax, current_axis_index = get_next_axis(all_axes, current_axis_index)
+    scv.pl.velocity_embedding_grid(
+        adata=adata_pyrovelocity,
+        basis="emb",
+        vkey="velocity_pyro",
+        show=False,
+        s=dotsize,
+        density=density,
+        scale=scale,
+        autoscale=True,
+        arrow_size=arrow,
+        linewidth=1,
+        ax=ax,
+        title="",
+        fontsize=default_fontsize,
+        color="gray",
+        arrow_color="black",
+    )
+    ax.axis("off")
+    if show_titles:
+        ax.set_title(
+            # "Pyro-Velocity cosine similarity: %.2f" % pyro_cos_mean, fontsize=default_fontsize
+            rf"Pyro\thinspace-Velocity ({pyro_cos_mean:.2f})"
+            if matplotlib.rcParams["text.usetex"]
+            else f"Pyro\u2009-Velocity ({pyro_cos_mean:.2f})",
+            fontsize=default_fontsize,
+            pad=default_title_padding,
+        )
+    else:
+        ax.set_title(
+            # f""
+            f"({pyro_cos_mean:.2f})",
+            fontsize=default_fontsize,
+            pad=default_title_padding,
+        )
+
+    if include_uncertainty_measures:
+        pca_angles = posterior_samples["pca_embeds_angle"]
+        pca_cell_angles = pca_angles / np.pi * 180
+        pca_angles_std = get_posterior_sample_angle_uncertainty(pca_cell_angles)
+        logger.info(
+            f"\nPCA angle uncertainty: {pca_angles_std.mean():.2f}"
+            f"± {pca_angles_std.std():.2f}\n\n"
+        )
+
+        # SHIFT AXIS INDEX
+        ax, current_axis_index = get_next_axis(all_axes, current_axis_index)
+        plot_vector_field_uncertainty(
+            adata_pyrovelocity,
+            embed_mean,
+            pca_angles_std,
+            ax=ax,
+            cbar=show_colorbars,
+            fig=fig,
+            basis="emb",
+            scale=scale,
+            arrow_size=arrow,
+            p_mass_min=1,
+            autoscale=True,
+            density=density,
+            only_grid=False,
+            uncertain_measure="PCA angle",
+            cmap="winter",
+            cmax=None,
+            color_vector_field_by_measure=False,
+            show_titles=show_titles,
+        )
+        if show_titles:
+            ax.set_title(
+                r"Pyro\thinspace-Velocity angle $\sigma$"
+                if matplotlib.rcParams["text.usetex"]
+                else "Pyro\u2009-Velocity angle σ",
+                fontsize=default_fontsize,
+                pad=default_title_padding,
+            )
+
+    # The obs names in adata_pyrovelocity have a "-N" suffix that
+    # is not present in the adata_cospar obs Index.
+    patched_adata_pyrovelocity_obs_names = (
+        adata_pyrovelocity.obs_names.str.replace(
+            r"-\d",
+            "",
+            regex=True,
+        )
+    )
+    adata_cospar_obs_subset = adata_cospar[
+        patched_adata_pyrovelocity_obs_names, :
+    ]
+    scatter_dotsize_factor = 3
+    # SHIFT AXIS INDEX
+    ax, current_axis_index = get_next_axis(all_axes, current_axis_index)
+    scv.pl.scatter(
+        adata=adata_cospar_obs_subset,
+        basis="emb",
+        fontsize=default_fontsize,
+        color="fate_potency_transition_map",
+        cmap="inferno_r",
+        show=False,
+        ax=ax,
+        s=dotsize * scatter_dotsize_factor,
+        colorbar=show_colorbars,
+        title="",
+    )
+    ax.axis("off")
+    if show_titles:
+        # ax.set_title("Clonal fate potency", fontsize=default_fontsize)
+        ax.set_title(
+            "Fate potency",
+            fontsize=default_fontsize,
+            pad=default_title_padding,
+        )
+    gold_standard = adata_cospar_obs_subset.obs.fate_potency_transition_map
+    select = ~np.isnan(gold_standard)
+    scvelo_latent_time_correlation = spearmanr(
+        -gold_standard[select],
+        adata_scvelo.obs.latent_time.values[select],
+    )[0]
+
+    # SHIFT AXIS INDEX
+    ax, current_axis_index = get_next_axis(all_axes, current_axis_index)
+    scv.pl.scatter(
+        adata=adata_scvelo,
+        c="latent_time",
+        basis="emb",
+        s=dotsize * scatter_dotsize_factor,
+        cmap="inferno",
+        ax=ax,
+        show=False,
+        fontsize=default_fontsize,
+        colorbar=show_colorbars,
+        title="",
+    )
+    ax.axis("off")
+    if show_titles:
+        ax.set_title(
+            # f"scVelo latent time\ncorrelation: {scvelo_latent_time_correlation:.2f}"
+            # f"scVelo latent time ({scvelo_latent_time_correlation:.2f})",
+            f"scVelo time ({scvelo_latent_time_correlation:.2f})",
+            fontsize=default_fontsize,
+            pad=default_title_padding,
+        )
+    else:
+        ax.set_title(
+            # f"scVelo latent time\ncorrelation: {scvelo_latent_time_correlation:.2f}"
+            f"({scvelo_latent_time_correlation:.2f})",
+            fontsize=default_fontsize,
+            pad=default_title_padding,
+        )
+
+    pyrovelocity_shared_time_correlation = spearmanr(
+        -gold_standard[select],
+        posterior_samples["cell_time"].mean(0).flatten()[select],
+    )[0]
+    # SHIFT AXIS INDEX
+    ax, current_axis_index = get_next_axis(all_axes, current_axis_index)
+    plot_posterior_time(
+        posterior_samples,
+        adata_pyrovelocity,
+        ax=ax,
+        basis="emb",
+        fig=fig,
+        addition=False,
+        position="right",
+        cmap="winter",
+        s=dotsize,
+        show_colorbar=show_colorbars,
+        show_titles=show_titles,
+    )
+    if show_titles:
+        ax.set_title(
+            # f"Pyro-Velocity shared time\ncorrelation: {pyrovelocity_shared_time_correlation:.2f}"
+            # f"Pyro-Velocity shared time ({pyrovelocity_shared_time_correlation:.2f})",
+            rf"Pyro\thinspace-Velocity time ({pyrovelocity_shared_time_correlation:.2f})"
+            if matplotlib.rcParams["text.usetex"]
+            else f"Pyro\u2009-Velocity time ({pyrovelocity_shared_time_correlation:.2f})",
+            fontsize=default_fontsize,
+            pad=default_title_padding,
+        )
+    else:
+        ax.set_title(
+            # f""
+            f"({pyrovelocity_shared_time_correlation:.2f})",
+            fontsize=default_fontsize,
+            pad=default_title_padding,
+        )
+
+    if save_plot:
+        for ext in ["", ".png"]:
+            fig.savefig(
+                fname=f"{lineage_fate_correlation_path}{ext}",
+                facecolor=fig.get_facecolor(),
+                bbox_inches="tight",
+                edgecolor="none",
+                dpi=300,
+                transparent=False,
+            )
+
+    return all_axes
+
+
+@beartype
+def get_next_axis(
+    axes: List[Axes] | np.ndarray,
+    current_index: int = 0,
+) -> Tuple[Axes, int]:
+    return axes[current_index], current_index + 1
+
+    # import ipdb
+
+    # ipdb.set_trace()
+    # colorbar_axes = [
+    #     ax[5].images[0],
+    #     ax[6].collections[0],
+    #     ax[7].collections[0],
+    # ]
+    # return colorbar_axes
+
+    # cell_time_mean = posterior_samples["cell_time"].mean(0).flatten()
+    # cell_time_std = posterior_samples["cell_time"].std(0).flatten()
+    # cell_time_cov = cell_time_std / cell_time_mean
+    # print(cell_time_cov)
+
+    # plot_vector_field_uncertainty(
+    #     adata_pyrovelocity,
+    #     embed_mean,
+    #     cell_time_std,
+    #     ax=ax[3],
+    #     cbar=True,
+    #     fig=fig,
+    #     basis="emb",
+    #     scale=scale,
+    #     arrow_size=arrow,
+    #     p_mass_min=1,
+    #     autoscale=True,
+    #     density=density,
+    #     only_grid=False,
+    #     uncertain_measure="shared time",
+    #     cmap="winter",
+    #     cmax=None,
+    # )
+    # ax[3].set_title("Shared time uncertainty", fontsize=default_fontsize)
+
+    # cell_magnitudes = posterior_samples["original_spaces_embeds_magnitude"]
+    # cell_magnitudes_mean = cell_magnitudes.mean(axis=-2)
+    # cell_magnitudes_std = cell_magnitudes.std(axis=-2)
+    # cell_magnitudes_cov = cell_magnitudes_std / cell_magnitudes_mean
+    # print(cell_magnitudes_cov)
+
+    # plot_vector_field_uncertainty(
+    #     adata_pyrovelocity,
+    #     embed_mean,
+    #     cell_magnitudes_cov,
+    #     ax=ax[4],
+    #     cbar=True,
+    #     fig=fig,
+    #     basis="emb",
+    #     scale=scale,
+    #     p_mass_min=1,
+    #     density=density,
+    #     arrow_size=arrow,
+    #     only_grid=False,
+    #     autoscale=True,
+    #     uncertain_measure="base magnitude",
+    #     cmap="summer",
+    # )
+    # ax[4].set_title(
+    #     "Pyro-Velocity cosine similarity: %.2f" % pyro_cos_mean, fontsize=default_fontsize
+    # )
diff --git a/src/pyrovelocity/plots/_parameters.py b/src/pyrovelocity/plots/_parameters.py
index 9de3e5fbf..9e82cdf82 100644
--- a/src/pyrovelocity/plots/_parameters.py
+++ b/src/pyrovelocity/plots/_parameters.py
@@ -1,13 +1,17 @@
 from os import PathLike
 from typing import Dict, List
 
+import matplotlib
 import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
 import seaborn as sns
 from anndata import AnnData
 from beartype import beartype
+from beartype.typing import Optional
+from matplotlib import ticker
 from matplotlib.figure import FigureBase
+from matplotlib.gridspec import SubplotSpec
 
 from pyrovelocity.logging import configure_logging
 
@@ -16,38 +20,114 @@
 logger = configure_logging(__name__)
 
 
+@beartype
+def tex_or_plain(
+    tex_str: str,
+    plain_str: str,
+) -> str:
+    """
+    Returns the TeX-formatted string if text.usetex is True,
+    otherwise returns the plain string.
+    """
+    return tex_str if matplotlib.rcParams["text.usetex"] else plain_str
+
+
+DEFAULT_PARAMETER_LABEL_MAPPINGS = {
+    "alpha": tex_or_plain(r"$\alpha$", "α"),
+    "beta": tex_or_plain(r"$\beta$", "β"),
+    "gamma": tex_or_plain(r"$\gamma$", "γ"),
+    "u_offset": tex_or_plain(r"$u_0$", "u₀"),
+    "s_offset": tex_or_plain(r"$s_0$", "s₀"),
+    "t0": tex_or_plain(r"$t_0$", "t₀"),
+}
+
+
+def construct_log_string(x, base):
+    if x <= 0:
+        return "0"
+    # print(x)
+    log_val = int(np.round(np.log(x) / np.log(base)))
+    if base == np.e:
+        return rf"$e^{{{log_val}}}$"
+    else:
+        return rf"${base}^{{{log_val}}}$"
+
+
 @beartype
 def plot_parameter_posterior_distributions(
     posterior_samples: Dict[str, np.ndarray],
     adata: AnnData,
     geneset: List[str],
-    parameter_uncertainty_plot: PathLike | str,
-    parameter_names: List[str] = [
-        "alpha",
-        "beta",
-        "gamma",
-        "u_offset",
-        "s_offset",
-        "t0",
-    ],
-) -> FigureBase:
+    parameter_names: List[str]
+    | Dict[str, str] = DEFAULT_PARAMETER_LABEL_MAPPINGS,
+    fig: Optional[FigureBase] = None,
+    gs: Optional[SubplotSpec] = None,
+    save_plot: bool = False,
+    parameter_uncertainty_plot: PathLike | str = "parameter_uncertainty.pdf",
+    default_fontsize: int = 7,
+    log_base=10,
+    boxplot: bool = False,
+) -> Optional[FigureBase]:
+    if isinstance(parameter_names, list):
+        parameter_names = {param: param for param in parameter_names}
+
     parameters = [
         parameter
-        for parameter in parameter_names
+        for parameter in parameter_names.keys()
         if parameter in posterior_samples.keys()
     ]
-    fig, ax = plt.subplots(len(parameters), 1)
-    fig.set_size_inches(18, len(parameters) * 4)
+    main_title = (
+        r"Parameter density estimates from $\log_{10}$-posterior samples"
+    )
+    if "s_offset" in parameters:
+        nrows = (len(parameters) + 1) // 2
+        ncols = 2
+    else:
+        "t0" in parameters and parameters.remove("t0")
+        nrows = len(parameters)
+        ncols = 1
+    if gs is None:
+        fig, ax = plt.subplots(len(parameters), 1)
+        fig.set_size_inches(18, len(parameters) * 4)
+    else:
+        sgs = gs.subgridspec(
+            nrows=nrows + 1,
+            ncols=ncols,
+            height_ratios=[0.1] + [1] * nrows,
+            # hspace=0.0,
+            # wspace=0.0,
+        )
+        title_ax = fig.add_subplot(sgs[0, :])
+        title_ax.axis("off")
+        title_ax.set_label("parameter_posteriors")
+        title_ax.text(
+            0.5,
+            0.5,
+            main_title,
+            ha="center",
+            va="center",
+            fontsize=default_fontsize + 1,
+            fontweight="bold",
+            transform=title_ax.transAxes,
+        )
+
     for index, parameter in enumerate(parameters):
+        if gs is not None:
+            col = index // nrows
+            row = index % nrows + 1  # +1 because the first row is for the title
+            ax1 = fig.add_subplot(sgs[row, col])
+        else:
+            row = index
+            ax1 = ax[index]
+        ax1.set_label("parameter_posteriors")
         df = pd.DataFrame(
-            np.log(
-                posterior_samples[parameter].squeeze()[
-                    :, np.isin(adata.var_names, list(geneset))
-                ],
-            ),
+            # np.log(
+            posterior_samples[parameter].squeeze()[
+                :, np.isin(adata.var_names, list(geneset))
+            ],
+            # ),
             columns=adata.var_names[np.isin(adata.var_names, list(geneset))],
         )
-        # df = df.apply(lambda x: x - x.mean())
         df_long = df.melt(var_name="index", value_name="value")
         logger.debug(df_long.head())
         df_long["index"] = pd.Categorical(
@@ -67,26 +147,82 @@ def plot_parameter_posterior_distributions(
         else:
             pass
 
-        ax1 = sns.violinplot(
-            x="index",
-            y="value",
-            data=df_long,
-            ax=ax[index],
+        dark_orange = "#ff6a14"
+        light_orange = "#ffb343"
+        df_long_medians = df_long.groupby("index")["value"].median()
+        for i, median in enumerate(df_long_medians):
+            ax1.hlines(
+                median, i - 0.4, i + 0.4, color=dark_orange, linewidth=1.5
+            )
+        if boxplot:
+            sns.boxenplot(
+                data=df_long,
+                x="index",
+                y="value",
+                color=dark_orange,
+                linecolor=light_orange,
+                linewidth=0,
+                ax=ax1,
+                width_method="exponential",
+                k_depth="full",
+                showfliers=False,
+                log_scale=log_base,
+            )
+        else:
+            sns.violinplot(
+                x="index",
+                y="value",
+                color=dark_orange,
+                linewidth=0,
+                data=df_long,
+                ax=ax1,
+                inner="box",
+                inner_kws=dict(
+                    box_width=1.5,
+                    whis_width=0.75,
+                    color="0",
+                ),
+                log_scale=log_base,
+            )
+        ax1.yaxis.set_major_formatter(
+            ticker.FuncFormatter(lambda x, _: construct_log_string(x, log_base))
         )
-        ax1.set_xticklabels(ax1.get_xticklabels(), rotation=30, ha="right")
-        ax1.set_ylabel(parameter)
-        ax1.set_xlabel("")
-
-    fig.subplots_adjust(
-        hspace=0.4, wspace=0.45, left=0.08, right=0.95, top=0.9, bottom=0.15
-    )
-    for ext in ["", ".png"]:
-        fig.savefig(
-            f"{parameter_uncertainty_plot}{ext}",
-            facecolor=fig.get_facecolor(),
-            bbox_inches="tight",
-            edgecolor="none",
-            dpi=300,
+        ax1.yaxis.set_tick_params(labelsize=default_fontsize)
+        ax1.yaxis.set_minor_locator(
+            ticker.LogLocator(base=log_base, subs="all", numticks=20)
+        )
+        ax1.yaxis.set_minor_formatter(ticker.NullFormatter())
+        ax1.tick_params(
+            axis="both", which="major", labelsize=default_fontsize - 3
         )
-    plt.close(fig)
+        ax1.tick_params(axis="x", which="minor", bottom=False, top=False)
+
+        if row < nrows:
+            ax1.set_xticklabels([])
+            ax1.set_xlabel("")
+        else:
+            ax1.set_xlabel("")
+            truncated_labels = [
+                label.get_text()[:7] for label in ax1.get_xticklabels()
+            ]
+            ax1.set_xticklabels(
+                labels=truncated_labels,
+                rotation=0,
+                ha="center",
+                fontdict={"fontsize": 5},
+            )
+
+        ax1.set_ylabel(parameter_names[parameter], fontsize=default_fontsize)
+
+    if save_plot and fig is not None:
+        fig.tight_layout()
+        for ext in ["", ".png"]:
+            fig.savefig(
+                f"{parameter_uncertainty_plot}{ext}",
+                facecolor=fig.get_facecolor(),
+                bbox_inches="tight",
+                edgecolor="none",
+                dpi=300,
+            )
+        plt.close(fig)
     return fig
diff --git a/src/pyrovelocity/plots/_predictive.py b/src/pyrovelocity/plots/_predictive.py
index 7ad0adef8..3e5f32494 100644
--- a/src/pyrovelocity/plots/_predictive.py
+++ b/src/pyrovelocity/plots/_predictive.py
@@ -60,7 +60,6 @@ def posterior_curve(
     output_fig_objects = []
     for figi, gene in enumerate(gene_set):
         (index,) = np.where(adata.var_names == gene)
-        # print(adata.shape, index, posterior_samples["st_mean"].shape)
 
         fig, ax = plt.subplots(3, 4)
         fig.set_size_inches(15, 10)
@@ -90,7 +89,6 @@ def posterior_curve(
                 grid_cell_time.mean(0).flatten() < t0_sample
             ] = 0
             grid_cell_colors = colors[grid_mask_t0_sample]
-            # print(grid_time_samples_st.shape)
 
             im = ax[sample].scatter(
                 posterior_samples["st_mean"][:, index[0]],
@@ -163,13 +161,6 @@ def posterior_curve(
                 :, index[0]
             ].flatten()
 
-            ##u0 = posterior_samples['u_offset'][sample][:, index[0]].flatten()
-            ##s0 = posterior_samples['s_offset'][sample][:, index[0]].flatten()
-            ##u_inf = posterior_samples['u_inf'][sample][:, index[0]].flatten()
-            ##s_inf = posterior_samples['s_inf'][sample][:, index[0]].flatten()
-            ##switching = posterior_samples['switching'][sample][:, index[0]].flatten()
-            ##dt_switching = posterior_samples['dt_switching'][sample][:, index[0]].flatten()
-
             ax[sample + 4].scatter(
                 t0_sample,
                 u0 * uscale,
@@ -219,20 +210,6 @@ def posterior_curve(
                 linewidth=0.5,
                 c="black",
             )
-            # ax[sample].plot(grid_time_samples_st[sample][:, index[0]],
-            #                grid_time_samples_ut[sample][:, index[0]],
-            #                linestyle="--", linewidth=3, color='g')
-            # if sample == 0:
-            #     print(gene, u0 * uscale, s0)
-            #     print(gene, u_inf * uscale, s_inf)
-            #     print(
-            #         t0_sample,
-            #         dt_switching_sample,
-            #         cell_time_sample_min,
-            #         cell_time_sample_max,
-            #         (cell_time_sample <= t0_sample).sum(),
-            #     )
-            #     print(cell_time_sample.shape)
 
             switching = t0_sample + dt_switching_sample
             state0 = (cell_gene_state_grid == 0) & (
@@ -311,7 +288,6 @@ def extrapolate_prediction_sample_predictive(
 
     posterior_samples_list = []
     for tensor_dict in scdl:
-        # print("--------------------")
         u_obs = tensor_dict["U"]
         s_obs = tensor_dict["X"]
         u_log_library = tensor_dict["u_lib_size"]
@@ -321,19 +297,6 @@ def extrapolate_prediction_sample_predictive(
         u_log_library_scale = tensor_dict["u_lib_size_scale"]
         s_log_library_scale = tensor_dict["s_lib_size_scale"]
         ind_x = tensor_dict["ind_x"].long().squeeze()
-        # dummy_obs = (
-        #     torch.tensor(u_obs).to("cuda:0"),
-        #     torch.tensor(s_obs).to("cuda:0"),
-        #     torch.tensor(u_log_library).to("cuda:0"),
-        #     torch.tensor(s_log_library).to("cuda:0"),
-        #     torch.tensor(u_log_library_mean).to("cuda:0"),
-        #     torch.tensor(s_log_library_mean).to("cuda:0"),
-        #     torch.tensor(u_log_library_scale).to("cuda:0"),
-        #     torch.tensor(s_log_library_scale).to("cuda:0"),
-        #     torch.tensor(ind_x).to("cuda:0"),
-        #     None,
-        #     None,
-        # )
         dummy_obs = (
             torch.tensor(u_obs),
             torch.tensor(s_obs),
@@ -409,7 +372,6 @@ def extrapolate_prediction_sample_predictive(
                     axis=-3,
                 )
             )
-            # ).to("cuda:0")
 
         posterior_samples_new_tmp = Predictive(
             pyro.poutine.uncondition(
@@ -421,7 +383,6 @@ def extrapolate_prediction_sample_predictive(
             posterior_samples_new_tmp[key] = posterior_samples[key]
         posterior_samples_list.append(posterior_samples_new_tmp)
 
-    # print(len(posterior_samples_list))
     posterior_samples_new = {}
     for key in posterior_samples_list[0].keys():
         if posterior_samples_list[0][key].shape[-2] == 1:
@@ -430,12 +391,6 @@ def extrapolate_prediction_sample_predictive(
             posterior_samples_new[key] = torch.concat(
                 [element[key] for element in posterior_samples_list], axis=-2
             )
-    # posterior_samples_new = model.generate_posterior_samples(
-    #    adata=adata, batch_size=512, num_samples=8
-    # )
-
-    # for key in posterior_samples_new.keys():
-    #     print(posterior_samples_new[key].shape)
 
     grid_time_samples_ut = posterior_samples_new["ut"]
     grid_time_samples_st = posterior_samples_new["st"]
@@ -456,10 +411,6 @@ def extrapolate_prediction_sample_predictive(
         grid_time_samples_uscale = np.ones(grid_time_samples_uinf.shape)
 
     grid_time_samples_state = posterior_samples_new["cell_gene_state"]
-    # print(grid_time_samples_state.shape)
-    # print(grid_time_samples_uscale.shape)
-    # print(grid_time_samples_ut.shape)
-    # print(grid_time_samples_st.shape)
     if isinstance(grid_time_samples_state, np.ndarray):
         return (
             grid_time_samples_ut,
diff --git a/src/pyrovelocity/plots/_rainbow.py b/src/pyrovelocity/plots/_rainbow.py
index fe6cfc303..97e5bc24c 100644
--- a/src/pyrovelocity/plots/_rainbow.py
+++ b/src/pyrovelocity/plots/_rainbow.py
@@ -1,6 +1,7 @@
 from pathlib import Path
 from typing import Dict, List, Optional
 
+import matplotlib
 import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
@@ -8,17 +9,492 @@
 import seaborn as sns
 from anndata import AnnData
 from beartype import beartype
+from beartype.typing import Any, Tuple
 from matplotlib import ticker
+from matplotlib.axes import Axes
 from matplotlib.figure import Figure, FigureBase
+from matplotlib.gridspec import GridSpec, SubplotSpec
 from numpy import ndarray
+from numpy.typing import NDArray
 from pandas import DataFrame, Index
 
 from pyrovelocity.plots._common import set_colorbar, set_font_size
-from pyrovelocity.utils import ensure_numpy_array
+from pyrovelocity.utils import ensure_numpy_array, setup_colors
 
 __all__ = ["rainbowplot", "us_rainbowplot"]
 
 
+def rainbowplot_module(
+    volcano_data: pd.DataFrame,
+    adata: AnnData,
+    posterior_samples: Dict[str, NDArray[Any]],
+    fig: Optional[Figure] = None,
+    ax: Optional[Axes] = None,
+    gs: Optional[SubplotSpec] = None,
+    genes: Optional[List[str]] = None,
+    data: List[str] = ["st", "ut"],
+    cell_state: str = "clusters",
+    basis: str = "umap",
+    num_genes: int = 5,
+    add_line: bool = True,
+    negative_correlation: bool = False,
+    state_info_colors: bool = False,
+    save_plot: bool = False,
+    show_data: bool = True,
+    rainbow_plot_path: str = "rainbow.pdf",
+    dotsize: int = 1,
+    default_fontsize: int = 7,
+) -> Tuple[Figure, Dict[str, Axes]]:
+    set_font_size(default_fontsize)
+
+    if genes is None:
+        genes = get_genes(volcano_data, num_genes, negative_correlation)
+    number_of_genes = len(genes)
+
+    if gs is None:
+        fig, axes_dict = create_rainbow_figure(number_of_genes, show_data)
+    else:
+        axes_dict = create_rainbow_axes(gs, number_of_genes, show_data)
+
+    if state_info_colors:
+        colors = setup_state_info_colors(adata, cell_state, basis)
+    else:
+        colors = setup_colors(adata, cell_state)
+
+    st, ut = get_posterior_samples_mean(data, posterior_samples)
+    if "st_std" in posterior_samples:
+        st_std = posterior_samples["st_std"]
+    else:
+        st_std = None
+
+    for n, gene in enumerate(genes):
+        ress = get_data(gene, st, ut, adata, cell_state, posterior_samples)
+        plot_gene_data_module(axes_dict, n, ress, colors, add_line, show_data)
+        plot_gene_on_embedding(
+            axes_dict=axes_dict,
+            n=n,
+            adata=adata,
+            st=st,
+            gene=gene,
+            basis=basis,
+            show_data=show_data,
+            st_std=st_std,
+            dotsize=dotsize,
+        )
+        set_labels_module(axes_dict, n, gene, number_of_genes, ress)
+
+    # sns.despine()
+    _set_axes_aspect(axes_dict)
+    fig.tight_layout()
+
+    if save_plot:
+        for ext in ["", ".png"]:
+            fig.savefig(
+                f"{rainbow_plot_path}{ext}",
+                facecolor=fig.get_facecolor(),
+                bbox_inches="tight",
+                edgecolor="none",
+                dpi=300,
+            )
+
+    return fig, axes_dict
+
+
+def create_rainbow_figure(
+    number_of_genes: int,
+    show_data: bool,
+    st_std: bool = True,
+) -> Tuple[Figure, Dict[str, Axes]]:
+    subplot_height = 0.9
+    horizontal_panels = 6 if show_data else 5
+    subplot_width = 1.5 * subplot_height * horizontal_panels
+
+    fig = plt.figure(figsize=(subplot_width, subplot_height * number_of_genes))
+    gs = GridSpec(
+        nrows=number_of_genes,
+        ncols=horizontal_panels,
+        figure=fig,
+        width_ratios=[
+            0.21,
+            *([1] * (horizontal_panels - 1)),
+        ],
+        wspace=0.2,
+        hspace=0.2,
+    )
+
+    axes_dict = {}
+    for n in range(number_of_genes):
+        axes_dict[f"gene_{n}"] = fig.add_subplot(gs[n, 0])
+        axes_dict[f"gene_{n}"].axis("off")
+        axes_dict[f"phase_{n}"] = fig.add_subplot(gs[n, 1])
+        axes_dict[f"dynamics_{n}"] = fig.add_subplot(gs[n, 2])
+        axes_dict[f"predictive_{n}"] = fig.add_subplot(gs[n, 3])
+        if show_data:
+            axes_dict[f"data_{n}"] = fig.add_subplot(gs[n, 4])
+        if st_std:
+            axes_dict[f"cv_{n}"] = fig.add_subplot(gs[n, 5])
+
+    return fig, axes_dict
+
+
+def create_rainbow_axes(
+    # fig: Figure,
+    # ax: Axes,
+    gs_top: SubplotSpec,
+    number_of_genes: int,
+    show_data: bool,
+) -> Dict[str, Axes]:
+    # gs = GridSpec(
+    #     number_of_genes,
+    #     4 if show_data else 3,
+    #     figure=fig,
+    #     # subplot_spec=ax,
+    # )
+    n_cols = 6 if show_data else 5
+    boundary_column_width = 0.001
+    gene_label_column_width = 0.21
+    gs = gs_top.subgridspec(
+        nrows=number_of_genes,
+        ncols=n_cols + 2,
+        # figure=fig,
+        # subplot_spec=ax,
+        # width_ratios=[0.1] + [0.21] + [1] * (n_cols - 1) + [0.1],
+        width_ratios=[
+            boundary_column_width,
+            gene_label_column_width,
+            *([1] * (n_cols - 1)),
+            boundary_column_width,
+        ],
+        height_ratios=[1] * number_of_genes,
+        wspace=0.2,
+        hspace=0.2,
+    )
+    axs = gs.subplots()
+
+    axes_dict = {}
+    plot_label = "rainbow"
+    for n in range(number_of_genes):
+        # axes_dict[f"phase_{n}"] = fig.add_subplot(gs[n, 0])
+        # axes_dict[f"dynamics_{n}"] = fig.add_subplot(gs[n, 1])
+        # axes_dict[f"predictive_{n}"] = fig.add_subplot(gs[n, 2])
+        # if show_data:
+        #     axes_dict[f"data_{n}"] = fig.add_subplot(gs[n, 3])
+        axs[n, 0].set_label("buffer_column_left")
+        axs[n, 0].axis("off")
+        axes_dict[f"gene_{n}"] = axs[n, 1]
+        axs[n, 1].set_label(plot_label)
+        axs[n, 1].axis("off")
+        axes_dict[f"phase_{n}"] = axs[n, 2]
+        axs[n, 2].set_label(plot_label)
+        axes_dict[f"dynamics_{n}"] = axs[n, 3]
+        axs[n, 3].set_label(plot_label)
+        axes_dict[f"predictive_{n}"] = axs[n, 4]
+        axs[n, 4].set_label(plot_label)
+        if show_data:
+            axes_dict[f"data_{n}"] = axs[n, 5]
+            axs[n, 5].set_label(plot_label)
+        axes_dict[f"cv_{n}"] = axs[n, 6]
+        axs[n, 6].set_label(plot_label)
+        axs[n, -1].set_label("buffer_column_right")
+        axs[n, -1].axis("off")
+
+    return axes_dict
+
+
+def plot_gene_data_module(
+    axes_dict: Dict[str, Axes],
+    n: int,
+    ress: pd.DataFrame,
+    colors: Dict,
+    add_line: bool,
+    show_data: bool,
+):
+    scatterplot(axes_dict[f"phase_{n}"], ress, colors)
+    plot_gene(axes_dict[f"dynamics_{n}"], ress, colors, add_line)
+
+
+def plot_gene_on_embedding(
+    axes_dict: Dict[str, Axes],
+    n: int,
+    adata: AnnData,
+    st: NDArray[Any],
+    gene: str,
+    basis: str,
+    show_data: bool,
+    st_std: Optional[NDArray[Any]] = None,
+    dotsize: int = 1,
+    plot_individual_obs: bool = False,
+    gridsize: int = 100,
+):
+    (index,) = np.where(adata.var_names == gene)
+
+    if plot_individual_obs:
+        im = axes_dict[f"predictive_{n}"].scatter(
+            adata.obsm[f"X_{basis}"][:, 0],
+            adata.obsm[f"X_{basis}"][:, 1],
+            s=dotsize,
+            c=st[:, index].flatten(),
+            cmap="cividis",
+            edgecolors="none",
+        )
+    else:
+        im = axes_dict[f"predictive_{n}"].hexbin(
+            adata.obsm[f"X_{basis}"][:, 0],
+            adata.obsm[f"X_{basis}"][:, 1],
+            C=st[:, index].flatten(),
+            gridsize=gridsize,
+            cmap="cividis",
+            edgecolors="none",
+            reduce_C_function=np.mean,
+        )
+
+    set_colorbar(
+        im,
+        axes_dict[f"predictive_{n}"],
+        labelsize=5,
+        fig=axes_dict[f"predictive_{n}"].figure,
+        rainbow=True,
+        axes_label="rainbow",
+    )
+    axes_dict[f"predictive_{n}"].axis("off")
+
+    if st_std is not None:
+        if plot_individual_obs:
+            im = axes_dict[f"cv_{n}"].scatter(
+                adata.obsm[f"X_{basis}"][:, 0],
+                adata.obsm[f"X_{basis}"][:, 1],
+                s=dotsize,
+                c=st_std[:, index].flatten() / st[:, index].flatten(),
+                cmap="cividis",
+                edgecolors="none",
+            )
+        else:
+            im = axes_dict[f"cv_{n}"].hexbin(
+                adata.obsm[f"X_{basis}"][:, 0],
+                adata.obsm[f"X_{basis}"][:, 1],
+                C=st_std[:, index].flatten() / st[:, index].flatten(),
+                gridsize=gridsize,
+                cmap="cividis",
+                edgecolors="none",
+                reduce_C_function=np.mean,
+            )
+
+        set_colorbar(
+            im,
+            axes_dict[f"cv_{n}"],
+            labelsize=5,
+            fig=axes_dict[f"cv_{n}"].figure,
+            rainbow=True,
+            axes_label="rainbow",
+        )
+        axes_dict[f"cv_{n}"].axis("off")
+
+    if show_data:
+        if plot_individual_obs:
+            im = axes_dict[f"data_{n}"].scatter(
+                adata.obsm[f"X_{basis}"][:, 0],
+                adata.obsm[f"X_{basis}"][:, 1],
+                s=dotsize,
+                c=ensure_numpy_array(adata[:, index].X).flatten(),
+                cmap="cividis",
+                edgecolors="none",
+            )
+        else:
+            im = axes_dict[f"data_{n}"].hexbin(
+                adata.obsm[f"X_{basis}"][:, 0],
+                adata.obsm[f"X_{basis}"][:, 1],
+                C=ensure_numpy_array(adata[:, index].X).flatten(),
+                gridsize=gridsize,
+                cmap="cividis",
+                edgecolors="none",
+                reduce_C_function=np.mean,
+            )
+
+        set_colorbar(
+            im,
+            axes_dict[f"data_{n}"],
+            labelsize=5,
+            fig=axes_dict[f"data_{n}"].figure,
+            rainbow=True,
+            axes_label="rainbow",
+        )
+        axes_dict[f"data_{n}"].axis("off")
+
+
+def set_labels_module(
+    axes_dict: Dict[str, Axes],
+    n: int,
+    gene: str,
+    number_of_genes: int,
+    ress: pd.DataFrame,
+    default_font_size: int | float = 7,
+    small_labelpad: int | float = 0.7,
+    title_background_color: str = "#F0F0F0",
+):
+    if n == 0:
+        axes_dict[f"phase_{n}"].set_title(
+            # label=r"Predictive ($\hat{\mu}(\hat{s}), \hat{\mu}(\hat{u}))$"
+            # if matplotlib.rcParams["text.usetex"]
+            # else "Predictive samples (μ(s), μ(u))",
+            label=r"$(u, s)$ phase space",
+            fontsize=default_font_size + 1,
+        )
+        axes_dict[f"dynamics_{n}"].set_title(
+            # label=r"Predictive ($ \hat{\mu}(t), \hat{\mu}(\hat{s}))$"
+            # if matplotlib.rcParams["text.usetex"]
+            # else "Predictive samples (μ(t), μ(s))",
+            label="Spliced dynamics",
+            fontsize=default_font_size + 1,
+        )
+        axes_dict[f"predictive_{n}"].set_title(
+            # label=r"Predictive $\hat{\mu}(\hat{s})$"
+            # if matplotlib.rcParams["text.usetex"]
+            # else "Predictive samples μ(s)",
+            label="Predictive spliced",
+            fontsize=default_font_size + 1,
+            backgroundcolor=title_background_color,
+        )
+        axes_dict[f"cv_{n}"].set_title(
+            # label=r"Predictive $\left.\hat{\sigma}(\hat{s}) \right/ \hat{\mu}(\hat{s})$"
+            # if matplotlib.rcParams["text.usetex"]
+            # else "Predictive samples σ/μ(s)",
+            label=r"Predictive uncertainty",
+            fontsize=default_font_size + 1,
+            backgroundcolor=title_background_color,
+        )
+        if f"data_{n}" in axes_dict:
+            axes_dict[f"data_{n}"].set_title(
+                # label=r"$\log \hat{s}$"
+                # if matplotlib.rcParams["text.usetex"]
+                # else "log observed s",
+                label=r"Observed $\log_{e}$ spliced"
+                if matplotlib.rcParams["text.usetex"]
+                else "Observed log spliced",
+                fontsize=default_font_size + 1,
+            )
+
+    if n == number_of_genes - 1:
+        axes_dict[f"dynamics_{n}"].set_xlabel(
+            xlabel=r"shared time, $\hat{\mu}(t)$",
+            loc="left",
+            labelpad=small_labelpad,
+            fontsize=default_font_size,
+        )
+        axes_dict[f"dynamics_{n}"].set_ylabel(
+            ylabel=r"spliced, $\hat{\mu}(s)$",
+            loc="bottom",
+            labelpad=small_labelpad,
+            fontsize=default_font_size,
+        )
+        axes_dict[f"dynamics_{n}"].yaxis.set_label_position("right")
+        axes_dict[f"phase_{n}"].set_xlabel(
+            xlabel=r"spliced, $\hat{\mu}(s)$",
+            loc="left",
+            labelpad=small_labelpad,
+            fontsize=default_font_size,
+        )
+        axes_dict[f"phase_{n}"].set_ylabel(
+            ylabel=r"unspliced, $\hat{\mu}(u)$",
+            loc="bottom",
+            labelpad=small_labelpad,
+            fontsize=default_font_size,
+        )
+        axes_dict[f"phase_{n}"].yaxis.set_label_position("right")
+    else:
+        axes_dict[f"dynamics_{n}"].set_xlabel("")
+        axes_dict[f"phase_{n}"].set_xlabel("")
+        axes_dict[f"dynamics_{n}"].set_ylabel("")
+        axes_dict[f"phase_{n}"].set_ylabel("")
+
+    axes_dict[f"gene_{n}"].text(
+        x=0.0,
+        y=0.5,
+        s=gene[:7],
+        fontsize=8,
+        weight="normal",
+        rotation=0,
+        va="center",
+        ha="center",
+        # transform=axes_dict[f"phase_{n}"].transAxes,
+    )
+    # axes_dict[f"phase_{n}"].set_title(gene, fontsize=7)
+    # axes_dict[f"phase_{n}"].text(
+    #     x=-0.4,
+    #     y=0.5,
+    #     s=gene,
+    #     fontsize=8,
+    #     weight="normal",
+    #     rotation=90,
+    #     va="center",
+    #     ha="right",
+    #     transform=axes_dict[f"phase_{n}"].transAxes,
+    # )
+
+    set_axis_limits_and_ticks(
+        ax_dynamics=axes_dict[f"dynamics_{n}"],
+        ax_phase=axes_dict[f"phase_{n}"],
+        ress=ress,
+        default_font_size=default_font_size,
+    )
+
+
+def set_axis_limits_and_ticks(ax_dynamics, ax_phase, ress, default_font_size=7):
+    major_tick_labels_scale_factor = 0.6
+    x_ticks = [0, np.power(10, get_closest_pow_of_10(ress["spliced"].max()))]
+    y_ticks = [0, np.power(10, get_closest_pow_of_10(ress["unspliced"].max()))]
+    ax_phase.set_xticks(x_ticks)
+    ax_phase.set_yticks(y_ticks)
+    ax_phase.get_xaxis().set_major_formatter(
+        ticker.FuncFormatter(
+            lambda x, pos: "" if pos == 0 else construct_log_string(x)
+        )
+    )
+    ax_phase.get_yaxis().set_major_formatter(
+        ticker.FuncFormatter(lambda x, _: construct_log_string(x))
+    )
+    ax_phase.tick_params(
+        axis="both",
+        which="major",
+        labelsize=default_font_size * major_tick_labels_scale_factor,
+    )
+
+    x_ticks = [0, 1]
+    y_ticks = [0, np.power(10, get_closest_pow_of_10(ress["spliced"].max()))]
+    ax_dynamics.set_xticks(x_ticks)
+    ax_dynamics.set_yticks(y_ticks)
+    ax_dynamics.get_xaxis().set_major_formatter(
+        ticker.FuncFormatter(lambda x, pos: "" if pos == 0 else x)
+    )
+    ax_dynamics.get_yaxis().set_major_formatter(
+        ticker.FuncFormatter(lambda x, _: construct_log_string(x))
+    )
+    ax_dynamics.tick_params(
+        axis="both",
+        which="major",
+        labelsize=default_font_size * major_tick_labels_scale_factor,
+    )
+
+
+def _set_axes_aspect(all_axes: Dict[str, Axes]) -> None:
+    for k, ax in all_axes.items():
+        if "predictive" in k or "data" in k or "cv" in k:
+            ax.set_aspect("equal", adjustable="box")
+        else:
+            x_min, x_max = ax.get_xlim()
+            y_min, y_max = ax.get_ylim()
+
+            x_range = x_max - x_min
+            y_range = y_max - y_min
+
+            square_aspect_fraction = 0.65
+
+            ax.set_aspect(
+                aspect=(x_range / y_range) * square_aspect_fraction,
+                adjustable="box",
+            )
+
+
+# TODO: merge with rainbowplot_module
 @beartype
 def rainbowplot(
     volcano_data: DataFrame,
@@ -32,10 +508,11 @@ def rainbowplot(
     num_genes: int = 5,
     add_line: bool = True,
     negative_correlation: bool = False,
-    scvelo_colors: bool = False,
+    state_info_colors: bool = False,
     save_plot: bool = False,
     show_data: bool = True,
     rainbow_plot_path: str | Path = "rainbow.pdf",
+    default_font_size: int = 7,
 ) -> FigureBase:
     set_font_size(7)
 
@@ -62,8 +539,8 @@ def rainbowplot(
     else:
         ax = fig.subplots(number_of_genes, horizontal_panels)
 
-    if scvelo_colors:
-        colors = setup_scvelo_colors(adata, cell_state)
+    if state_info_colors:
+        colors = setup_state_info_colors(adata, cell_state)
     else:
         colors = setup_colors(adata, cell_state)
 
@@ -78,11 +555,11 @@ def rainbowplot(
             ax4 = ax[n, 3]
 
         if n == 0:
-            ax1.set_title("Predictive dynamics", fontsize=7)
-            ax2.set_title("Phase portrait", fontsize=7)
-            ax3.set_title("Predictive spliced", fontsize=7)
+            ax1.set_title("Predictive dynamics", fontsize=default_font_size)
+            ax2.set_title("Phase portrait", fontsize=default_font_size)
+            ax3.set_title("Predictive spliced", fontsize=default_font_size)
             if show_data:
-                ax4.set_title("Log spliced data", fontsize=7)
+                ax4.set_title("Log spliced data", fontsize=default_font_size)
 
         plot_gene(ax1, ress, colors, add_line)
         scatterplot(ax2, ress, colors)
@@ -93,6 +570,7 @@ def rainbowplot(
             s=3,
             c=st[:, index].flatten(),
             cmap="cividis",
+            edgecolors="none",
         )
         set_colorbar(im, ax3, labelsize=5, fig=fig, rainbow=True)
         ax3.axis("off")
@@ -103,6 +581,7 @@ def rainbowplot(
                 s=3,
                 c=ensure_numpy_array(adata.X[:, index]).flatten(),
                 cmap="cividis",
+                edgecolors="none",
             )
             set_colorbar(im, ax4, labelsize=5, fig=fig, rainbow=True)
             ax4.axis("off")
@@ -127,7 +606,7 @@ def rainbowplot(
     return fig
 
 
-def set_subfigure_titles(ax, n):
+def set_subfigure_titles(ax, n, default_font_size):
     if n == 0:
         ax[0].set_title("Rainbow plot", fontsize=7)
         ax[1].set_title("Phase portrait", fontsize=7)
@@ -146,10 +625,11 @@ def scatter_gene(ax, adata, st, gene, basis):
         s=3,
         c=st[:, index].flatten(),
         cmap="cividis",
+        edgecolors="none",
     )
 
 
-def adjust_subfigure(subfig):
+def adjust_subfigure(subfig, default_font_size):
     subfig[0].subplots_adjust(
         hspace=0.8, wspace=1.4, left=0.32, right=0.94, top=0.92, bottom=0.12
     )
@@ -192,7 +672,12 @@ def get_genes(
     )
 
 
-def setup_scvelo_colors(adata, cell_state, basis):
+@beartype
+def setup_state_info_colors(
+    adata: AnnData,
+    cell_state: str,
+    basis: str,
+):
     scv.pl.scatter(
         adata,
         basis=basis,
@@ -207,17 +692,18 @@ def setup_scvelo_colors(adata, cell_state, basis):
     )
 
 
-def setup_colors(adata, cell_state):
-    clusters = adata.obs.loc[:, cell_state]
-    return dict(
-        zip(
-            clusters.cat.categories,
-            sns.color_palette("deep", clusters.cat.categories.shape[0]),
-        )
-    )
+# TODO: remove following refactor to utils
+# def setup_colors(adata, cell_state):
+#     clusters = adata.obs.loc[:, cell_state]
+#     return dict(
+#         zip(
+#             clusters.cat.categories,
+#             sns.color_palette("deep", clusters.cat.categories.shape[0]),
+#         )
+#     )
 
 
-def get_posterior_samples_mean(data, posterior_samples):
+def get_posterior_samples_mean(data, posterior_samples) -> NDArray[Any]:
     if (data[0] in posterior_samples) and (data[1] in posterior_samples):
         st = posterior_samples[data[0]].mean(0).squeeze()
         ut = posterior_samples[data[1]].mean(0).squeeze()
@@ -267,7 +753,7 @@ def plot_gene(ax1, ress, colors, add_line):
             )
 
 
-def set_labels(ax1, ax2, ax3, gene, ngenes, ress, n):
+def set_labels(ax1, ax2, ax3, gene, ngenes, ress, n, default_font_size=7):
     if n == 0:
         ax3.set_title("Predictive spliced", fontsize=7)
     if n == ngenes - 1:
@@ -326,7 +812,7 @@ def construct_log_string(x):
     return rf"{str_val}"
 
 
-def scatterplot(ax2, ress, colors):
+def scatterplot(ax, ress, colors):
     sns.scatterplot(
         x="spliced",
         y="unspliced",
@@ -336,7 +822,7 @@ def scatterplot(ax2, ress, colors):
         edgecolor="none",
         hue="cell_type",
         palette=colors,
-        ax=ax2,
+        ax=ax,
         marker="o",
         legend=False,
         s=3,
diff --git a/src/pyrovelocity/plots/_report.py b/src/pyrovelocity/plots/_report.py
new file mode 100644
index 000000000..74a08178f
--- /dev/null
+++ b/src/pyrovelocity/plots/_report.py
@@ -0,0 +1,375 @@
+from pathlib import Path
+
+import dill
+import matplotlib.pyplot as plt
+from anndata import AnnData
+from beartype.typing import Any, Dict, List, Optional, Tuple
+from matplotlib.axes import Axes
+from matplotlib.figure import FigureBase
+from matplotlib.gridspec import GridSpec
+from numpy.typing import NDArray
+from pandas import DataFrame
+
+from pyrovelocity.io.compressedpickle import CompressedPickle
+from pyrovelocity.plots._genes import plot_gene_ranking
+from pyrovelocity.plots._parameters import (
+    plot_parameter_posterior_distributions,
+)
+from pyrovelocity.plots._rainbow import rainbowplot_module as rainbowplot
+from pyrovelocity.plots._vector_fields import plot_vector_field_summary
+from pyrovelocity.styles import configure_matplotlib_style
+from pyrovelocity.styles.colors import LARRY_CELL_TYPE_COLORS
+
+configure_matplotlib_style()
+
+__all__ = [
+    "plot_report",
+    "save_subfigures",
+]
+
+
+def create_main_figure(
+    width: float,
+    height: float,
+    layout: Dict[str, List[float]],
+) -> Tuple[FigureBase, Dict[str, Axes]]:
+    """Create the main figure with all subplots."""
+    fig = plt.figure(figsize=(width, height))
+    gs = GridSpec(
+        figure=fig,
+        nrows=len(layout["height_ratios"]),
+        ncols=len(layout["width_ratios"]),
+        height_ratios=layout["height_ratios"],
+        width_ratios=layout["width_ratios"],
+    )
+
+    axes = {}
+
+    for key, ax in axes.items():
+        ax.set_label(key)
+
+    return fig, axes, gs
+
+
+def plot_report(
+    adata: AnnData,
+    posterior_samples: Dict[str, NDArray[Any]],
+    volcano_data: DataFrame,
+    putative_marker_genes: List[str],
+    selected_genes: List[str],
+    vector_field_basis: str = "umap",
+    cell_state: str = "clusters",
+    state_color_dict: Optional[Dict[str, str]] = None,
+    boxplot: bool = False,
+    report_file_path: Path | str = "example_plot_report.pdf",
+    figure_file_path: Path | str = "example_report_figure.dill.zst",
+) -> FigureBase:
+    """
+    Plot a report figure with multiple subplots and serialize the Figure object.
+
+    Args:
+        figure_file_path (Path | str, optional):
+            Figure object file. Defaults to "example_report_figure.dill.zst".
+        adata (AnnData, optional):
+            AnnData object. Defaults to adata.
+        posterior_samples (Dict[str, NDArray[Any]], optional):
+            Posterior samples dictionary. Defaults to posterior_samples.
+        volcano_data (DataFrame, optional):
+            Volcano data DataFrame. Defaults to volcano_data.
+        vector_field_basis (str, optional):
+            Vector field basis identifier. Defaults to "emb".
+        cell_state (str, optional):
+            Cell state identifier. Defaults to "state_info".
+        putative_marker_genes (List[str], optional):
+            List of putative marker genes. Defaults to putative_marker_genes.
+        selected_genes (List[str], optional):
+            List of genes to be included in report figure. Defaults to selected_genes.
+        report_file_path (Path | str, optional):
+            File to save report figure. Defaults to "example_plot_report.pdf".
+
+    Returns:
+        FigureBase: Figure object containing the report figure.
+
+    Examples:
+    >>> # xdoctest: +SKIP
+    >>> from pyrovelocity.analysis.analyze import top_mae_genes
+    >>> from pyrovelocity.plots import plot_report
+    >>> from pyrovelocity.utils import load_anndata_from_path
+    >>> from pyrovelocity.io.compressedpickle import CompressedPickle
+    >>> from pyrovelocity.styles.colors import LARRY_CELL_TYPE_COLORS
+    ...
+    >>> adata = load_anndata_from_path("models/larry_model2/postprocessed.h5ad")
+    >>> posterior_samples = CompressedPickle.load(
+    ...     "models/larry_model2/pyrovelocity.pkl.zst"
+    ... )
+    >>> volcano_data = posterior_samples["gene_ranking"]
+    >>> vector_field_basis = "emb"
+    >>> cell_state = "state_info"
+    >>> putative_marker_genes = top_mae_genes(
+    ...     volcano_data=volcano_data,
+    ...     mae_top_percentile=3,
+    ...     min_genes_per_bin=3,
+    ... )
+    >>> selected_genes = putative_marker_genes[:6]
+    >>> selected_genes = ["Cyp11a1", "Csf2rb", "Osbpl8", "Lgals1", "Cmtm7", "Runx1"]
+    >>> putative_marker_genes = list(set(putative_marker_genes + selected_genes))
+    ...
+    >>> plot_report(
+    ...     adata=adata,
+    ...     posterior_samples=posterior_samples,
+    ...     volcano_data=volcano_data,
+    ...     putative_marker_genes=putative_marker_genes,
+    ...     selected_genes=selected_genes,
+    ...     vector_field_basis=vector_field_basis,
+    ...     cell_state=cell_state,
+    ...     state_color_dict=LARRY_CELL_TYPE_COLORS,
+    ...     report_file_path="example_plot_report.pdf",
+    ... )
+    """
+    width = 8.5 - 1
+    height = (11 - 1) * 0.9
+    layout = {
+        "height_ratios": [0.12, 0.28, 0.6],
+        "width_ratios": [0.5, 0.25, 0.25],
+    }
+
+    selected_genes_in_adata = [
+        gene for gene in selected_genes if gene in adata.var.index
+    ]
+
+    additional_genes = [
+        gene
+        for gene in putative_marker_genes
+        if gene not in selected_genes_in_adata and gene in adata.var.index
+    ]
+
+    while len(selected_genes_in_adata) < 6 and additional_genes:
+        selected_genes_in_adata.append(additional_genes.pop(0))
+
+    extended_putative_marker_genes = selected_genes_in_adata.copy()
+    for gene in putative_marker_genes:
+        if (
+            gene not in extended_putative_marker_genes
+            and gene in adata.var.index
+        ):
+            extended_putative_marker_genes.append(gene)
+
+    selected_genes = selected_genes_in_adata
+    putative_marker_genes = extended_putative_marker_genes
+
+    fig, axes, gs = create_main_figure(width, height, layout)
+    plot_vector_field_summary(
+        adata=adata,
+        posterior_samples=posterior_samples,
+        vector_field_basis=vector_field_basis,
+        cell_state=cell_state,
+        state_color_dict=state_color_dict,
+        fig=fig,
+        gs=gs[0, :],
+        default_fontsize=7,
+    )
+
+    plot_gene_ranking(
+        posterior_samples=posterior_samples,
+        adata=adata,
+        fig=fig,
+        gs=gs[1, 0],
+        putative_marker_genes=putative_marker_genes,
+        selected_genes=selected_genes,
+        time_correlation_with="st",
+        show_marginal_histograms=False,
+    )
+
+    plot_parameter_posterior_distributions(
+        posterior_samples=posterior_samples,
+        adata=adata,
+        geneset=selected_genes,
+        fig=fig,
+        gs=gs[1, 1:],
+        boxplot=boxplot,
+    )
+
+    rainbowplot(
+        volcano_data=volcano_data,
+        adata=adata,
+        posterior_samples=posterior_samples,
+        genes=selected_genes,
+        data=["st", "ut"],
+        basis=vector_field_basis,
+        cell_state=cell_state,
+        fig=fig,
+        gs=gs[2, :],
+    )
+
+    fig.tight_layout()
+
+    x_col1 = -0.005
+    y_row2 = 0.84
+    add_panel_label(fig, "a", x_col1, 1.00)
+    add_panel_label(fig, "b", x_col1, y_row2)
+    add_panel_label(fig, "c", 0.47, y_row2)
+    add_panel_label(fig, "d", x_col1, 0.57)
+
+    fig.savefig(report_file_path, format="pdf")
+    fig.savefig(
+        f"{report_file_path}.png",
+        facecolor=fig.get_facecolor(),
+        bbox_inches="tight",
+        edgecolor="none",
+        dpi=300,
+        format="png",
+    )
+
+    CompressedPickle.save(figure_file_path, fig)
+
+    return fig
+
+
+def extract_subfigures(
+    buffer: Optional[bytes] = None,
+    axes_to_keep: List[str] = [],
+    main_fig: Optional[FigureBase] = None,
+    figure_file_path: Optional[str | Path] = None,
+) -> FigureBase:
+    """
+    Extract a subset of axes from the main figure using dill for serialization.
+
+    Args:
+        main_fig: The main Figure object
+        axes_to_keep: List of axes keys to keep in the new figure
+
+    Returns:
+        A new Figure object with only the specified axes
+    """
+
+    if buffer:
+        subfig = dill.loads(buffer)
+    elif main_fig:
+        buffer = dill.dumps(main_fig)
+        subfig = dill.loads(buffer)
+    else:
+        raise ValueError("Either buffer or main_fig must be provided.")
+
+    for text in subfig.texts[:]:
+        subfig.texts.remove(text)
+
+    axes_to_remove = [
+        ax for ax in subfig.axes if ax.get_label() not in axes_to_keep
+    ]
+
+    for ax in axes_to_remove:
+        subfig.delaxes(ax)
+
+    if figure_file_path:
+        with Path(figure_file_path).open("wb") as f:
+            dill.dump(subfig, f)
+
+    return subfig
+
+
+def save_subfigures(
+    figure_file_path: Path | str = "main_figure.dill.zst",
+    vector_field_summary_file_path: Path
+    | str = "extracted_vector_field_summary.pdf",
+    gene_selection_file_path: Path | str = "extracted_gene_selection.pdf",
+    parameter_posteriors_file_path: Path
+    | str = "extracted_parameter_posteriors.pdf",
+    rainbow_file_path: Path | str = "extracted_rainbow.pdf",
+):
+    fig = CompressedPickle.load(figure_file_path)
+    buffer = dill.dumps(fig)
+
+    subfig_vector_field_summary = extract_subfigures(
+        buffer=buffer,
+        axes_to_keep=["vector_field"],
+    )
+    subfig_vector_field_summary.savefig(
+        fname=vector_field_summary_file_path,
+        format="pdf",
+    )
+
+    subfig_gene_selection = extract_subfigures(
+        buffer=buffer,
+        axes_to_keep=["gene_selection"],
+    )
+    subfig_gene_selection.savefig(
+        fname=gene_selection_file_path,
+        format="pdf",
+    )
+
+    subfig_parameter_posteriors = extract_subfigures(
+        buffer=buffer,
+        axes_to_keep=["parameter_posteriors"],
+    )
+    subfig_parameter_posteriors.savefig(
+        fname=parameter_posteriors_file_path,
+        format="pdf",
+    )
+
+    subfig_rainbow = extract_subfigures(
+        buffer=buffer,
+        axes_to_keep=["rainbow"],
+    )
+    subfig_rainbow.savefig(
+        fname=rainbow_file_path,
+        format="pdf",
+    )
+
+
+def add_panel_label(
+    fig: FigureBase,
+    label: str,
+    x: float,
+    y: float,
+    fontsize: int = 14,
+    fontweight: str = "bold",
+    va: str = "top",
+    ha: str = "left",
+):
+    """
+    Add a panel label to the figure using global figure coordinates.
+
+    Args:
+        fig: matplotlib figure object
+        label: string, the label to add (e.g., 'a', 'b', 'c')
+        x: float, x-coordinate in figure coordinates (0-1)
+        y: float, y-coordinate in figure coordinates (0-1)
+        fontsize: int, font size for the label
+        fontweight: str, font weight for the label
+        va: str, vertical alignment for the label
+        ha: str, horizontal alignment for the label
+    """
+    fig.text(
+        x=x,
+        y=y,
+        s=label,
+        fontsize=fontsize,
+        fontweight=fontweight,
+        va=va,
+        ha=ha,
+    )
+
+
+def add_axis_label(
+    ax: Axes,
+    label: str,
+    x: float = -0.1,
+    y: float = 1.1,
+    fontsize: int = 14,
+    fontweight: str = "bold",
+    va: str = "top",
+    ha: str = "right",
+):
+    """
+    Add a label to the given axes.
+    """
+    ax.text(
+        x=x,
+        y=y,
+        s=label,
+        transform=ax.transAxes,
+        fontsize=fontsize,
+        fontweight=fontweight,
+        va=va,
+        ha=ha,
+    )
diff --git a/src/pyrovelocity/plots/_summary.py b/src/pyrovelocity/plots/_summary.py
index e597f02ef..346a37798 100644
--- a/src/pyrovelocity/plots/_summary.py
+++ b/src/pyrovelocity/plots/_summary.py
@@ -42,8 +42,8 @@ def plot_gene_selection_summary(
     )
 
     volcano_data, _ = plot_gene_ranking(
-        posterior_samples=[posterior_samples],
-        adata=[adata],
+        posterior_samples=posterior_samples,
+        adata=adata,
         ax=ax[1],
         time_correlation_with="st",
         selected_genes=selected_genes,
diff --git a/src/pyrovelocity/plots/_time.py b/src/pyrovelocity/plots/_time.py
index a00bb9086..50e5f1be2 100644
--- a/src/pyrovelocity/plots/_time.py
+++ b/src/pyrovelocity/plots/_time.py
@@ -8,10 +8,14 @@
 import seaborn as sns
 from anndata import AnnData
 from beartype import beartype
-from matplotlib.figure import FigureBase
+from beartype.typing import Optional
+from matplotlib.axes import Axes
+from matplotlib.figure import Figure, FigureBase
+from matplotlib.ticker import MaxNLocator
 from scipy.stats import spearmanr
 
 from pyrovelocity.plots._common import set_colorbar, set_font_size
+from pyrovelocity.utils import quartile_coefficient_of_dispersion
 
 __all__ = [
     "plot_posterior_time",
@@ -27,7 +31,12 @@ def plot_posterior_time(
     basis="umap",
     addition=True,
     position="left",
+    cmap="cividis",
     s=3,
+    show_colorbar=True,
+    show_titles=True,
+    alpha=1,
+    plot_individual_obs=False,
 ):
     if addition:
         sns.set_style("white")
@@ -37,7 +46,8 @@ def plot_posterior_time(
         plt.hist(posterior_samples["cell_time"].mean(0), bins=100, label="test")
         plt.xlabel("mean of cell time")
         plt.ylabel("frequency")
-        plt.title("Histogram of cell time posterior samples")
+        if show_titles:
+            plt.title("Histogram of cell time posterior samples")
         plt.legend()
     pos_mean_time = posterior_samples["cell_time"].mean(0)
     adata.obs["cell_time"] = pos_mean_time / pos_mean_time.max()
@@ -45,82 +55,179 @@ def plot_posterior_time(
     if ax is None:
         fig, ax = plt.subplots(1, 1)
         fig.set_size_inches(2.36, 2)
-    im = ax.scatter(
-        adata.obsm[f"X_{basis}"][:, 0],
-        adata.obsm[f"X_{basis}"][:, 1],
-        s=s,
-        alpha=0.4,
-        c=adata.obs["cell_time"],
-        cmap="cividis",
-        linewidth=0,
-    )
-    set_colorbar(im, ax, labelsize=5, fig=fig, position=position)
-    ax.axis("off")
-    if "cytotrace" in adata.obs.columns:
-        ax.set_title(
-            "Pyro-Velocity shared time\ncorrelation with Cytotrace: %.2f"
-            % (
-                spearmanr(
-                    adata.obs["cell_time"].values,
-                    1 - adata.obs.cytotrace.values,
-                )[0]
-            ),
-            fontsize=7,
+    if plot_individual_obs:
+        im = ax.scatter(
+            adata.obsm[f"X_{basis}"][:, 0],
+            adata.obsm[f"X_{basis}"][:, 1],
+            s=s,
+            alpha=alpha,
+            c=adata.obs["cell_time"],
+            cmap=cmap,
+            linewidth=0,
         )
     else:
-        ax.set_title("Pyro-Velocity shared time\n", fontsize=7)
+        im = ax.hexbin(
+            x=adata.obsm[f"X_{basis}"][:, 0],
+            y=adata.obsm[f"X_{basis}"][:, 1],
+            C=adata.obs["cell_time"],
+            gridsize=100,
+            cmap=cmap,
+            alpha=alpha,
+            linewidths=0,
+            edgecolors="none",
+            reduce_C_function=np.mean,  # This will average cell_time values in each hexagon
+        )
+    if show_colorbar:
+        set_colorbar(im, ax, labelsize=5, fig=fig, position=position)
+    ax.axis("off")
+    if show_titles:
+        if "cytotrace" in adata.obs.columns:
+            ax.set_title(
+                "Pyro-Velocity shared time\ncorrelation with Cytotrace: %.2f"
+                % (
+                    spearmanr(
+                        adata.obs["cell_time"].values,
+                        1 - adata.obs.cytotrace.values,
+                    )[0]
+                ),
+                fontsize=7,
+            )
+        else:
+            ax.set_title("Pyro-Velocity shared time\n", fontsize=7)
 
 
 @beartype
 def plot_shared_time_uncertainty(
-    posterior_samples: Dict[str, np.ndarray],
     adata: AnnData,
+    posterior_samples: Dict[str, np.ndarray],
     vector_field_basis: str,
     shared_time_plot: PathLike | str,
+    dotsize: Optional[int] = None,
+    default_font_size: int = 12,
+    duplicate_title: bool = False,
+    plot_individual_obs: bool = False,
 ) -> FigureBase:
     cell_time_mean = posterior_samples["cell_time"].mean(0).flatten()
-    cell_time_std = posterior_samples["cell_time"].std(0).flatten()
+    cell_time_mean_max = cell_time_mean.max()
+    cell_times = posterior_samples["cell_time"] / cell_time_mean_max
+    cell_time_mean = cell_times.mean(0).flatten()
+    cell_time_std = cell_times.std(0).flatten()
+    cell_time_cv = cell_time_std / cell_time_mean
+    cell_time_qcd = quartile_coefficient_of_dispersion(cell_times).flatten()
     adata.obs["shared_time_std"] = cell_time_std
     adata.obs["shared_time_mean"] = cell_time_mean
+    adata.obs["shared_time_cv"] = cell_time_cv
+    adata.obs["shared_time_qcd"] = cell_time_qcd
 
-    set_font_size(7)
-    fig, ax = plt.subplots(1, 3)
-    fig.set_size_inches(10, 3)
-    ax = ax.flatten()
-
-    ax[0].hist(cell_time_std / cell_time_mean, bins=100)
-    ax[0].set_title("histogram of shared time CoV")
-    ax_st = scv.pl.scatter(
-        adata,
-        c="shared_time_mean",
-        ax=ax[1],
-        show=False,
-        cmap="inferno",
-        fontsize=12,
-        colorbar=True,
+    mean_string = (
+        r"shared time $\mu$"
+        if matplotlib.rcParams["text.usetex"]
+        else "shared time μ"
     )
-    ax_cv = scv.pl.scatter(
-        adata,
-        c="shared_time_std",
-        ax=ax[2],
-        show=False,
-        cmap="winter",
-        fontsize=12,
-        colorbar=True,
-        title="shared time standard deviation",
+    cv_string = (
+        r"shared time $\left.\sigma \right/ \mu$"
+        if matplotlib.rcParams["text.usetex"]
+        else "shared time σ/μ"
     )
-    ax_cv.set_xlabel("density estimate over 90th %")
-    select = adata.obs["shared_time_std"] > np.quantile(
-        adata.obs["shared_time_std"], 0.9
-    )
-    sns.kdeplot(
-        x=adata.obsm[f"X_{vector_field_basis}"][:, 0][select],
-        y=adata.obsm[f"X_{vector_field_basis}"][:, 1][select],
-        ax=ax[2],
-        levels=3,
-        fill=False,
+    qcd_string = f"shared time QCD"
+
+    fig = plt.figure(figsize=(6, 6))
+    gs = fig.add_gridspec(
+        nrows=3,
+        ncols=2,
+        width_ratios=[0.5, 0.5],
+        height_ratios=[0.45, 0.45, 0.05],
     )
+
+    ax1 = fig.add_subplot(gs[0, 0])
+    ax1.hist(cell_time_mean, bins=100)
+    ax1.set_title(mean_string)
+
+    ax2 = fig.add_subplot(gs[0, 1])
+    ax2.hist(cell_time_qcd, bins=100)
+    ax2.set_title(qcd_string)
+
+    ax3 = fig.add_subplot(gs[1, 0])
+    if plot_individual_obs:
+        scv.pl.scatter(
+            adata=adata,
+            basis=vector_field_basis,
+            c="shared_time_mean",
+            ax=ax3,
+            show=False,
+            cmap="winter",
+            fontsize=default_font_size,
+            colorbar=False,
+            s=dotsize,
+            title="",
+        )
+    else:
+        im3 = ax3.hexbin(
+            x=adata.obsm[f"X_{vector_field_basis}"][:, 0],
+            y=adata.obsm[f"X_{vector_field_basis}"][:, 1],
+            C=adata.obs["shared_time_mean"],
+            gridsize=100,
+            cmap="winter",
+            linewidths=0,
+            edgecolors="none",
+            vmin=min(cell_time_mean),
+            vmax=max(cell_time_mean),
+            reduce_C_function=np.mean,
+        )
+    ax3.axis("off")
+    if duplicate_title:
+        ax3.set_title(mean_string)
+
+    ax4 = fig.add_subplot(gs[1, 1])
+    if plot_individual_obs:
+        scv.pl.scatter(
+            adata=adata,
+            basis=vector_field_basis,
+            c="shared_time_cv",
+            ax=ax4,
+            show=False,
+            cmap="winter",
+            fontsize=default_font_size,
+            colorbar=False,
+            s=dotsize,
+            title="",
+        )
+    else:
+        im4 = ax4.hexbin(
+            x=adata.obsm[f"X_{vector_field_basis}"][:, 0],
+            y=adata.obsm[f"X_{vector_field_basis}"][:, 1],
+            # C=adata.obs["shared_time_cv"],
+            C=adata.obs["shared_time_qcd"],
+            gridsize=100,
+            cmap="winter",
+            linewidths=0,
+            edgecolors="none",
+            vmin=min(cell_time_qcd),
+            vmax=max(cell_time_qcd),
+            reduce_C_function=np.mean,
+        )
+    ax4.axis("off")
+    if duplicate_title:
+        ax4.set_title(qcd_string)
+    # select = adata.obs["shared_time_cv"] > np.quantile(
+    #     adata.obs["shared_time_cv"], 0.9
+    # )
+    # sns.kdeplot(
+    #     x=adata.obsm[f"X_{vector_field_basis}"][:, 0][select],
+    #     y=adata.obsm[f"X_{vector_field_basis}"][:, 1][select],
+    #     ax=ax[2],
+    #     levels=3,
+    #     fill=False,
+    # )
+    # for ax in [ax1, ax2, ax3, ax4]:
+    #     ax.set_aspect("equal", adjustable="box")
     fig.tight_layout()
+
+    cbar_ax3 = fig.add_subplot(gs[2, 0])
+    _add_colorbar(fig=fig, ax=ax3, cbar_ax=cbar_ax3)
+    cbar_ax4 = fig.add_subplot(gs[2, 1])
+    _add_colorbar(fig=fig, ax=ax4, cbar_ax=cbar_ax4)
+
     for ext in ["", ".png"]:
         fig.savefig(
             f"{shared_time_plot}{ext}",
@@ -133,3 +240,30 @@ def plot_shared_time_uncertainty(
     plt.close(fig)
 
     return fig
+
+
+@beartype
+def _add_colorbar(
+    fig: Figure,
+    ax: Axes,
+    cbar_ax: Axes,
+    cbar_width_fraction: float = 0.6,
+    cbar_height: float = 0.02,
+) -> Axes:
+    im = ax.collections[0]
+    cbar = fig.colorbar(mappable=im, cax=cbar_ax, orientation="horizontal")
+    cbar.locator = MaxNLocator(nbins=2)
+    cbar.update_ticks()
+    cbar_ax.xaxis.set_ticks_position("bottom")
+    cbar_ax.xaxis.set_label_position("bottom")
+    ax3_pos = ax.get_position()
+    cbar_width = ax3_pos.width * cbar_width_fraction
+    cbar_ax.set_position(
+        [
+            ax3_pos.x0 + (ax3_pos.width - cbar_width),
+            ax3_pos.y0 - cbar_height,
+            cbar_width,
+            cbar_height,
+        ]
+    )
+    return cbar_ax
diff --git a/src/pyrovelocity/plots/_trajectory.py b/src/pyrovelocity/plots/_trajectory.py
index 0fc163478..7be71cfec 100644
--- a/src/pyrovelocity/plots/_trajectory.py
+++ b/src/pyrovelocity/plots/_trajectory.py
@@ -1,423 +1,96 @@
-import anndata
 import numpy as np
+from anndata import AnnData
+from beartype import beartype
+from beartype.typing import List, Optional
 from scipy.sparse import issparse
 
 
+@beartype
 def get_clone_trajectory(
-    adata,
-    average_start_point=True,
-    global_traj=True,
-    times=[2, 4, 6],
-    clone_num=None,
-):
+    adata: AnnData,
+    average_start_point: bool = True,
+    times: List[int] = [2, 4, 6],
+    clone_num: Optional[int] = None,
+) -> AnnData:
+    adata = adata.copy()
     if not average_start_point:
         adata.obsm["clone_vector_emb"] = np.zeros((adata.shape[0], 2))
 
-    adatas = []
-    clones = []
     centroids = []
-    cen_clones = []
-    print(adata.shape)
     adata.obs["clones"] = 0
-    if "noWell" in adata.obs.columns:
-        for w in adata.obs.Well.unique():
-            adata_w = adata[adata.obs.Well == w]
-            clone_adata_w = clone_adata[clone_adata.obs.Well == w]
-            for j in range(clone_adata_w.shape[1]):
-                adata_w.obs["clonei"] = 0
-                # belongs to same clone
-                adata_w.obs.loc[
-                    clone_adata_w[:, j].X.toarray()[:, 0] >= 1, "clonei"
-                ] = 1
-
-                if not average_start_point:
-                    for i in np.where(
-                        (adata_w.obs.time == 2) & (adata_w.obs.clonei == 1)
-                    )[0]:
-                        next_time = np.where(
-                            (adata_w.obs.time == 4) & (adata_w.obs.clonei == 1)
-                        )[0]
-                        adata_w.obsm["velocity_umap"][i] = (
-                            adata_w.obsm["X_umap"][next_time].mean(axis=0)
-                            - adata_w.obsm["X_umap"][i]
-                        )
-                    for i in np.where(
-                        (adata_w.obs.time == 4) & (adata_w.obs.clonei == 1)
-                    )[0]:
-                        next_time = np.where(
-                            (adata_w.obs.time == 6) & (adata_w.obs.clonei == 1)
-                        )[0]
-                        adata_w.obsm["velocity_umap"][i] = (
-                            adata_w.obsm["X_umap"][next_time].mean(axis=0)
-                            - adata_w.obsm["X_umap"][i]
-                        )
-                else:
-                    time2 = np.where(
-                        (adata_w.obs.time == 2) & (adata_w.obs.clonei == 1)
-                    )[0]
-                    time4 = np.where(
-                        (adata_w.obs.time == 4) & (adata_w.obs.clonei == 1)
-                    )[0]
-                    time6 = np.where(
-                        (adata_w.obs.time == 6) & (adata_w.obs.clonei == 1)
-                    )[0]
-                    if (
-                        time2.shape[0] == 0
-                        and time4.shape[0] == 0
-                        and time6.shape[0] == 0
-                    ):
-                        continue
-                    if (
-                        time2.shape[0] > 0
-                        and time4.shape[0] == 0
-                        and time6.shape[0] > 0
-                    ):
-                        continue
-                    adata_new = anndata.AnnData(
-                        np.vstack(
-                            [
-                                adata_w[time2].X.toarray().mean(axis=0),
-                                adata_w[time4].X.toarray().mean(axis=0),
-                                adata_w[time6].X.toarray().mean(axis=0),
-                            ]
-                        ),
-                        layers={
-                            "spliced": np.vstack(
-                                [
-                                    adata_w[time2]
-                                    .layers["spliced"]
-                                    .toarray()
-                                    .mean(axis=0),
-                                    adata_w[time4]
-                                    .layers["spliced"]
-                                    .toarray()
-                                    .mean(axis=0),
-                                    adata_w[time6]
-                                    .layers["spliced"]
-                                    .toarray()
-                                    .mean(axis=0),
-                                ]
-                            ),
-                            "unspliced": np.vstack(
-                                [
-                                    adata_w[time2]
-                                    .layers["unspliced"]
-                                    .toarray()
-                                    .mean(axis=0),
-                                    adata_w[time4]
-                                    .layers["unspliced"]
-                                    .toarray()
-                                    .mean(axis=0),
-                                    adata_w[time6]
-                                    .layers["unspliced"]
-                                    .toarray()
-                                    .mean(axis=0),
-                                ]
-                            ),
-                        },
-                        var=adata_w.var,
-                    )
-
-                    adata_new.obs.loc[:, "time"] = [2, 4, 6]
-                    adata_new.obs.loc[:, "Cell type annotation"] = "Centroid"
-                    print(adata_w[time6].obs.clonetype.unique())
-                    print(adata_w[time6].obs)
-
-                    adata_new.obs.loc[:, "clonetype"] = (
-                        adata_w[time6].obs.clonetype.unique()
-                    )  # use cell fate from last time point
-                    adata_new.obs.loc[:, "clones"] = int(j)
-                    if "Well" in adata_w[time6].obs.columns:
-                        adata_new.obs.loc[:, "Well"] = adata_w[
-                            time6
-                        ].obs.Well.unique()
-
-                    adata_new.obsm["X_umap"] = np.vstack(
-                        [
-                            adata_w[time2].obsm["X_umap"].mean(axis=0),
-                            adata_w[time4].obsm["X_umap"].mean(axis=0),
-                            adata_w[time6].obsm["X_umap"].mean(axis=0),
-                        ]
-                    )
-                    adata_new.obsm["velocity_umap"] = np.vstack(
-                        [
-                            adata_w.obsm["X_umap"][time4].mean(axis=0)
-                            - adata_w.obsm["X_umap"][time2].mean(axis=0),
-                            adata_w.obsm["X_umap"][time6].mean(axis=0)
-                            - adata_w.obsm["X_umap"][time4].mean(axis=0),
-                            np.zeros(2),
-                        ]
-                    )
-                    centroids.append(adata_new)
-                    clone_new = anndata.AnnData(
-                        np.vstack(
-                            [
-                                clone_adata_w[time2].X.toarray().mean(axis=0),
-                                clone_adata_w[time4].X.toarray().mean(axis=0),
-                                clone_adata_w[time6].X.toarray().mean(axis=0),
-                            ]
-                        ),
-                        obs=adata_new.obs,
-                    )
-                    clone_new.var_names = clone_adata.var_names
-                    clone_new.var = clone_adata.var
-                    # print(clone_new.shape)
-                    cen_clones.append(clone_new)
-
-            adata_new = adata_w.concatenate(
-                centroids[0].concatenate(centroids[1:]), join="outer"
-            )
-            clone_new = clone_adata_w.concatenate(
-                cen_clones[0].concatenate(cen_clones[1:]), join="outer"
+
+    if clone_num is None:
+        clone_num = adata.obsm["X_clone"].shape[1]
+    for j in range(clone_num):
+        adata.obs["clonei"] = 0
+
+        if issparse(adata.obsm["X_clone"]):
+            adata.obs.loc[
+                adata.obsm["X_clone"].toarray()[:, j] >= 1, "clonei"
+            ] = 1
+        else:
+            adata.obs.loc[adata.obsm["X_clone"][:, j] >= 1, "clonei"] = 1
+
+        times_index = []
+        for t in times:
+            times_index.append(
+                np.where((adata.obs.time_info == t) & (adata.obs.clonei == 1))[
+                    0
+                ]
             )
-            adatas.append(adata_new)
-            clones.append(clone_new)
-        return adatas[0].concatenate(adatas[1]), clones[0].concatenate(
-            clones[1]
-        )
-    else:
-        if clone_num is None:
-            clone_num = adata.obsm["X_clone"].shape[1]
-        for j in range(clone_num):
-            print(j)
-            adata.obs["clonei"] = 0
-            # print('----------aa------')
-            if issparse(adata.obsm["X_clone"]):
-                adata.obs.loc[
-                    adata.obsm["X_clone"].toarray()[:, j] >= 1, "clonei"
-                ] = 1
-            else:
-                adata.obs.loc[adata.obsm["X_clone"][:, j] >= 1, "clonei"] = 1
-            # print('----------bb------')
-
-            if not average_start_point:
-                for i in np.where(
-                    (adata.obs.time == 2) & (adata.obs.clonei == 1)
-                )[0]:
-                    next_time = np.where(
-                        (adata.obs.time == 4) & (adata.obs.clonei == 1)
-                    )[0]
-                    adata.obsm["velocity_umap"][i] = (
-                        adata.obsm["X_umap"][next_time].mean(axis=0)
-                        - adata.obsm["X_umap"][i]
-                    )
-                for i in np.where(
-                    (adata.obs.time == 4) & (adata.obs.clonei == 1)
-                )[0]:
-                    next_time = np.where(
-                        (adata.obs.time == 6) & (adata.obs.clonei == 1)
-                    )[0]
-                    adata.obsm["velocity_umap"][i] = (
-                        adata.obsm["X_umap"][next_time].mean(axis=0)
-                        - adata.obsm["X_umap"][i]
-                    )
-            else:
-                if global_traj:
-                    times_index = []
-                    for t in times:
-                        times_index.append(
-                            np.where(
-                                (adata.obs.time_info == t)
-                                & (adata.obs.clonei == 1)
-                            )[0]
-                        )
-
-                    consecutive_flag = np.array(
-                        [int(time.shape[0] > 0) for time in times_index]
-                    )
-                    consecutive = np.diff(consecutive_flag)
-                    if np.sum(consecutive_flag == 1) >= 2 and np.any(
-                        consecutive == 0
-                    ):  # Must be consecutive time points
-                        # print('centroid:', consecutive, times_index)
-                        adata_new = anndata.AnnData(
-                            np.vstack(
-                                [
-                                    np.array(
-                                        adata[time].X.mean(axis=0)
-                                    ).squeeze()
-                                    for time in times_index
-                                    if time.shape[0] > 0
-                                ]
-                            ),
-                            # layers={
-                            #     "spliced": np.vstack(
-                            #         [
-                            #             np.array(
-                            #                 adata[time]
-                            #                 .layers["spliced"]
-                            #                 .mean(axis=0)
-                            #             )
-                            #             for time in times_index
-                            #             if time.shape[0] > 0
-                            #         ]
-                            #     ),
-                            #     "unspliced": np.vstack(
-                            #         [
-                            #             np.array(
-                            #                 adata[time]
-                            #                 .layers["unspliced"]
-                            #                 .mean(axis=0)
-                            #             )
-                            #             for time in times_index
-                            #             if time.shape[0] > 0
-                            #         ]
-                            #     ),
-                            # },
-                            var=adata.var,
-                        )
-                        # print('----------cc------')
-                        adata.obs.iloc[
-                            np.hstack(
-                                [
-                                    time
-                                    for time in times_index
-                                    if time.shape[0] > 0
-                                ]
-                            ),
-                            adata.obs.columns.get_loc("clones"),
-                        ] = int(j)
-                        adata_new.obs.loc[:, "time"] = [
-                            t
-                            for t, time in zip([2, 4, 6], times_index)
-                            if time.shape[0] > 0
-                        ]
-                        adata_new.obs.loc[:, "clones"] = int(j)
-                        adata_new.obs.loc[:, "state_info"] = "Centroid"
-                        adata_new.obsm["X_emb"] = np.vstack(
-                            [
-                                adata[time].obsm["X_emb"].mean(axis=0)
-                                for time in times_index
-                                if time.shape[0] > 0
-                            ]
-                        )
-                        # print('----------dd------')
-
-                        # print(adata_new.shape)
-                        # print(adata_new.obsm['X_umap'])
-                        adata_new.obsm["clone_vector_emb"] = np.vstack(
-                            [
-                                adata_new.obsm["X_emb"][i + 1]
-                                - adata_new.obsm["X_emb"][i]
-                                for i in range(
-                                    adata_new.obsm["X_emb"].shape[0] - 1
-                                )
-                            ]
-                            + [np.zeros(2)]
-                        )
-                        # print('----------ee------')
-                        # print(adata_new.obsm['clone_vector_emb'])
-                    else:
-                        # print('pass-------')
-                        continue
-
-                else:
-                    time2 = np.where(
-                        (adata.obs.time == t) & (adata.obs.clonei == 1)
-                    )[0]
-                    time4 = np.where(
-                        (adata.obs.time == 4) & (adata.obs.clonei == 1)
-                    )[0]
-                    time6 = np.where(
-                        (adata.obs.time == 6) & (adata.obs.clonei == 1)
-                    )[0]
-                    adata_new = anndata.AnnData(
-                        np.vstack(
-                            [
-                                adata[time2].X.toarray().mean(axis=0),
-                                adata[time4].X.toarray().mean(axis=0),
-                                adata[time6].X.toarray().mean(axis=0),
-                            ]
-                        ),
-                        layers={
-                            "spliced": np.vstack(
-                                [
-                                    adata[time2]
-                                    .layers["spliced"]
-                                    .toarray()
-                                    .mean(axis=0),
-                                    adata[time4]
-                                    .layers["spliced"]
-                                    .toarray()
-                                    .mean(axis=0),
-                                    adata[time6]
-                                    .layers["spliced"]
-                                    .toarray()
-                                    .mean(axis=0),
-                                ]
-                            ),
-                            "unspliced": np.vstack(
-                                [
-                                    adata[time2]
-                                    .layers["unspliced"]
-                                    .toarray()
-                                    .mean(axis=0),
-                                    adata[time4]
-                                    .layers["unspliced"]
-                                    .toarray()
-                                    .mean(axis=0),
-                                    adata[time6]
-                                    .layers["unspliced"]
-                                    .toarray()
-                                    .mean(axis=0),
-                                ]
-                            ),
-                        },
-                        var=adata.var,
-                    )
-
-                    print(adata_new.X.sum(axis=1))
-                    adata_new.obs.loc[:, "time"] = [2, 4, 6]
-                    adata_new.obs.loc[:, "Cell type annotation"] = "Centroid"
-                    if not global_traj:
-                        adata_new.obs.loc[:, "clonetype"] = (
-                            adata[time6].obs.clonetype.unique()
-                        )  # use cell fate from last time point
-                    adata_new.obs.loc[:, "clones"] = j
-
-                    if "noWell" in adata[time6].obs.columns:
-                        adata_new.obs.loc[:, "Well"] = adata[
-                            time6
-                        ].obs.Well.unique()
-
-                    adata_new.obsm["X_umap"] = np.vstack(
-                        [
-                            adata[time2].obsm["X_umap"].mean(axis=0),
-                            adata[time4].obsm["X_umap"].mean(axis=0),
-                            adata[time6].obsm["X_umap"].mean(axis=0),
-                        ]
-                    )
-                    adata_new.obsm["velocity_umap"] = np.vstack(
-                        [
-                            adata.obsm["X_umap"][time4].mean(axis=0)
-                            - adata.obsm["X_umap"][time2].mean(axis=0),
-                            adata.obsm["X_umap"][time6].mean(axis=0)
-                            - adata.obsm["X_umap"][time4].mean(axis=0),
-                            np.zeros(2),
-                        ]
-                    )
-
-                    # print(adata_new.obsm['velocity_umap'])
-                    clone_new = anndata.AnnData(
-                        np.vstack(
-                            [
-                                clone_adata[time2].X.toarray().mean(axis=0),
-                                clone_adata[time4].X.toarray().mean(axis=0),
-                                clone_adata[time6].X.toarray().mean(axis=0),
-                            ]
-                        ),
-                        obs=adata_new.obs,
-                    )
-                    clone_new.var_names = clone_adata.var_names
-                    clone_new.var = clone_adata.var
-                    cen_clones.append(clone_new)
-                centroids.append(adata_new)
-        print(adata.shape)
-        print(len(centroids))
-        adata_new = adata.concatenate(
-            centroids[0].concatenate(centroids[1:]), join="outer"
+
+        consecutive_flag = np.array(
+            [int(time.shape[0] > 0) for time in times_index]
         )
-        return adata_new
+        consecutive = np.diff(consecutive_flag)
+
+        if np.sum(consecutive_flag == 1) >= 2 and np.any(consecutive == 0):
+            adata_new = AnnData(
+                np.vstack(
+                    [
+                        np.array(adata[time].X.mean(axis=0)).squeeze()
+                        for time in times_index
+                        if time.shape[0] > 0
+                    ]
+                ),
+                var=adata.var,
+            )
+
+            adata.obs.iloc[
+                np.hstack([time for time in times_index if time.shape[0] > 0]),
+                adata.obs.columns.get_loc("clones"),
+            ] = int(j)
+            adata_new.obs.loc[:, "time"] = [
+                t
+                for t, time in zip([2, 4, 6], times_index)
+                if time.shape[0] > 0
+            ]
+            adata_new.obs.loc[:, "clones"] = int(j)
+            adata_new.obs.loc[:, "state_info"] = "Centroid"
+            adata_new.obsm["X_emb"] = np.vstack(
+                [
+                    adata[time].obsm["X_emb"].mean(axis=0)
+                    for time in times_index
+                    if time.shape[0] > 0
+                ]
+            )
+
+            adata_new.obsm["clone_vector_emb"] = np.vstack(
+                [
+                    adata_new.obsm["X_emb"][i + 1] - adata_new.obsm["X_emb"][i]
+                    for i in range(adata_new.obsm["X_emb"].shape[0] - 1)
+                ]
+                + [np.zeros(2)]
+            )
+        else:
+            continue
+
+        centroids.append(adata_new)
+
+    adata_new = adata.concatenate(
+        centroids[0].concatenate(centroids[1:]), join="outer"
+    )
+    return adata_new
 
 
 def align_trajectory_diff(
@@ -487,3 +160,422 @@ def align_trajectory_diff(
     print(results.shape)
     length_filter = np.sqrt((results[:, 2:4] ** 2).sum(1)) > length_cutoff
     return results[length_filter]
+
+
+# TODO: remove unused code
+# def get_clone_trajectory2(
+#     adata: AnnData,
+#     # clone_adata: AnnData,
+#     average_start_point: bool = True,
+#     global_traj: bool = True,
+#     times: List[int] = [2, 4, 6],
+#     clone_num: Optional[int] = None,
+# ) -> AnnData:
+#     if not average_start_point:
+#         adata.obsm["clone_vector_emb"] = np.zeros((adata.shape[0], 2))
+
+#     adatas = []
+#     clones = []
+#     centroids = []
+#     cen_clones = []
+#     print(adata.shape)
+#     adata.obs["clones"] = 0
+#     if "noWell" in adata.obs.columns:
+#         for w in adata.obs.Well.unique():
+#             adata_w = adata[adata.obs.Well == w]
+#             clone_adata_w = clone_adata[clone_adata.obs.Well == w]
+#             for j in range(clone_adata_w.shape[1]):
+#                 adata_w.obs["clonei"] = 0
+#                 # belongs to same clone
+#                 adata_w.obs.loc[
+#                     clone_adata_w[:, j].X.toarray()[:, 0] >= 1, "clonei"
+#                 ] = 1
+
+#                 if not average_start_point:
+#                     for i in np.where(
+#                         (adata_w.obs.time == 2) & (adata_w.obs.clonei == 1)
+#                     )[0]:
+#                         next_time = np.where(
+#                             (adata_w.obs.time == 4) & (adata_w.obs.clonei == 1)
+#                         )[0]
+#                         adata_w.obsm["velocity_umap"][i] = (
+#                             adata_w.obsm["X_umap"][next_time].mean(axis=0)
+#                             - adata_w.obsm["X_umap"][i]
+#                         )
+#                     for i in np.where(
+#                         (adata_w.obs.time == 4) & (adata_w.obs.clonei == 1)
+#                     )[0]:
+#                         next_time = np.where(
+#                             (adata_w.obs.time == 6) & (adata_w.obs.clonei == 1)
+#                         )[0]
+#                         adata_w.obsm["velocity_umap"][i] = (
+#                             adata_w.obsm["X_umap"][next_time].mean(axis=0)
+#                             - adata_w.obsm["X_umap"][i]
+#                         )
+#                 else:
+#                     time2 = np.where(
+#                         (adata_w.obs.time == 2) & (adata_w.obs.clonei == 1)
+#                     )[0]
+#                     time4 = np.where(
+#                         (adata_w.obs.time == 4) & (adata_w.obs.clonei == 1)
+#                     )[0]
+#                     time6 = np.where(
+#                         (adata_w.obs.time == 6) & (adata_w.obs.clonei == 1)
+#                     )[0]
+#                     if (
+#                         time2.shape[0] == 0
+#                         and time4.shape[0] == 0
+#                         and time6.shape[0] == 0
+#                     ):
+#                         continue
+#                     if (
+#                         time2.shape[0] > 0
+#                         and time4.shape[0] == 0
+#                         and time6.shape[0] > 0
+#                     ):
+#                         continue
+#                     adata_new = AnnData(
+#                         np.vstack(
+#                             [
+#                                 adata_w[time2].X.toarray().mean(axis=0),
+#                                 adata_w[time4].X.toarray().mean(axis=0),
+#                                 adata_w[time6].X.toarray().mean(axis=0),
+#                             ]
+#                         ),
+#                         layers={
+#                             "spliced": np.vstack(
+#                                 [
+#                                     adata_w[time2]
+#                                     .layers["spliced"]
+#                                     .toarray()
+#                                     .mean(axis=0),
+#                                     adata_w[time4]
+#                                     .layers["spliced"]
+#                                     .toarray()
+#                                     .mean(axis=0),
+#                                     adata_w[time6]
+#                                     .layers["spliced"]
+#                                     .toarray()
+#                                     .mean(axis=0),
+#                                 ]
+#                             ),
+#                             "unspliced": np.vstack(
+#                                 [
+#                                     adata_w[time2]
+#                                     .layers["unspliced"]
+#                                     .toarray()
+#                                     .mean(axis=0),
+#                                     adata_w[time4]
+#                                     .layers["unspliced"]
+#                                     .toarray()
+#                                     .mean(axis=0),
+#                                     adata_w[time6]
+#                                     .layers["unspliced"]
+#                                     .toarray()
+#                                     .mean(axis=0),
+#                                 ]
+#                             ),
+#                         },
+#                         var=adata_w.var,
+#                     )
+
+#                     adata_new.obs.loc[:, "time"] = [2, 4, 6]
+#                     adata_new.obs.loc[:, "Cell type annotation"] = "Centroid"
+#                     print(adata_w[time6].obs.clonetype.unique())
+#                     print(adata_w[time6].obs)
+
+#                     adata_new.obs.loc[:, "clonetype"] = (
+#                         adata_w[time6].obs.clonetype.unique()
+#                     )  # use cell fate from last time point
+#                     adata_new.obs.loc[:, "clones"] = int(j)
+#                     if "Well" in adata_w[time6].obs.columns:
+#                         adata_new.obs.loc[:, "Well"] = adata_w[
+#                             time6
+#                         ].obs.Well.unique()
+
+#                     adata_new.obsm["X_umap"] = np.vstack(
+#                         [
+#                             adata_w[time2].obsm["X_umap"].mean(axis=0),
+#                             adata_w[time4].obsm["X_umap"].mean(axis=0),
+#                             adata_w[time6].obsm["X_umap"].mean(axis=0),
+#                         ]
+#                     )
+#                     adata_new.obsm["velocity_umap"] = np.vstack(
+#                         [
+#                             adata_w.obsm["X_umap"][time4].mean(axis=0)
+#                             - adata_w.obsm["X_umap"][time2].mean(axis=0),
+#                             adata_w.obsm["X_umap"][time6].mean(axis=0)
+#                             - adata_w.obsm["X_umap"][time4].mean(axis=0),
+#                             np.zeros(2),
+#                         ]
+#                     )
+#                     centroids.append(adata_new)
+#                     clone_new = AnnData(
+#                         np.vstack(
+#                             [
+#                                 clone_adata_w[time2].X.toarray().mean(axis=0),
+#                                 clone_adata_w[time4].X.toarray().mean(axis=0),
+#                                 clone_adata_w[time6].X.toarray().mean(axis=0),
+#                             ]
+#                         ),
+#                         obs=adata_new.obs,
+#                     )
+#                     clone_new.var_names = clone_adata.var_names
+#                     clone_new.var = clone_adata.var
+#                     # print(clone_new.shape)
+#                     cen_clones.append(clone_new)
+
+#             adata_new = adata_w.concatenate(
+#                 centroids[0].concatenate(centroids[1:]), join="outer"
+#             )
+#             clone_new = clone_adata_w.concatenate(
+#                 cen_clones[0].concatenate(cen_clones[1:]), join="outer"
+#             )
+#             adatas.append(adata_new)
+#             clones.append(clone_new)
+#         return adatas[0].concatenate(adatas[1]), clones[0].concatenate(
+#             clones[1]
+#         )
+#     else:
+#         if clone_num is None:
+#             clone_num = adata.obsm["X_clone"].shape[1]
+#         for j in range(clone_num):
+#             # print(j)
+#             adata.obs["clonei"] = 0
+#             # print('----------aa------')
+#             if issparse(adata.obsm["X_clone"]):
+#                 adata.obs.loc[
+#                     adata.obsm["X_clone"].toarray()[:, j] >= 1, "clonei"
+#                 ] = 1
+#             else:
+#                 adata.obs.loc[adata.obsm["X_clone"][:, j] >= 1, "clonei"] = 1
+#             # print('----------bb------')
+
+#             if not average_start_point:
+#                 for i in np.where(
+#                     (adata.obs.time == 2) & (adata.obs.clonei == 1)
+#                 )[0]:
+#                     next_time = np.where(
+#                         (adata.obs.time == 4) & (adata.obs.clonei == 1)
+#                     )[0]
+#                     adata.obsm["velocity_umap"][i] = (
+#                         adata.obsm["X_umap"][next_time].mean(axis=0)
+#                         - adata.obsm["X_umap"][i]
+#                     )
+#                 for i in np.where(
+#                     (adata.obs.time == 4) & (adata.obs.clonei == 1)
+#                 )[0]:
+#                     next_time = np.where(
+#                         (adata.obs.time == 6) & (adata.obs.clonei == 1)
+#                     )[0]
+#                     adata.obsm["velocity_umap"][i] = (
+#                         adata.obsm["X_umap"][next_time].mean(axis=0)
+#                         - adata.obsm["X_umap"][i]
+#                     )
+#             else:
+#                 if global_traj:
+#                     times_index = []
+#                     for t in times:
+#                         times_index.append(
+#                             np.where(
+#                                 (adata.obs.time_info == t)
+#                                 & (adata.obs.clonei == 1)
+#                             )[0]
+#                         )
+
+#                     consecutive_flag = np.array(
+#                         [int(time.shape[0] > 0) for time in times_index]
+#                     )
+#                     consecutive = np.diff(consecutive_flag)
+#                     if np.sum(consecutive_flag == 1) >= 2 and np.any(
+#                         consecutive == 0
+#                     ):  # Must be consecutive time points
+#                         # print('centroid:', consecutive, times_index)
+#                         adata_new = AnnData(
+#                             np.vstack(
+#                                 [
+#                                     np.array(
+#                                         adata[time].X.mean(axis=0)
+#                                     ).squeeze()
+#                                     for time in times_index
+#                                     if time.shape[0] > 0
+#                                 ]
+#                             ),
+#                             # layers={
+#                             #     "spliced": np.vstack(
+#                             #         [
+#                             #             np.array(
+#                             #                 adata[time]
+#                             #                 .layers["spliced"]
+#                             #                 .mean(axis=0)
+#                             #             )
+#                             #             for time in times_index
+#                             #             if time.shape[0] > 0
+#                             #         ]
+#                             #     ),
+#                             #     "unspliced": np.vstack(
+#                             #         [
+#                             #             np.array(
+#                             #                 adata[time]
+#                             #                 .layers["unspliced"]
+#                             #                 .mean(axis=0)
+#                             #             )
+#                             #             for time in times_index
+#                             #             if time.shape[0] > 0
+#                             #         ]
+#                             #     ),
+#                             # },
+#                             var=adata.var,
+#                         )
+#                         # print('----------cc------')
+#                         adata.obs.iloc[
+#                             np.hstack(
+#                                 [
+#                                     time
+#                                     for time in times_index
+#                                     if time.shape[0] > 0
+#                                 ]
+#                             ),
+#                             adata.obs.columns.get_loc("clones"),
+#                         ] = int(j)
+#                         adata_new.obs.loc[:, "time"] = [
+#                             t
+#                             for t, time in zip([2, 4, 6], times_index)
+#                             if time.shape[0] > 0
+#                         ]
+#                         adata_new.obs.loc[:, "clones"] = int(j)
+#                         adata_new.obs.loc[:, "state_info"] = "Centroid"
+#                         adata_new.obsm["X_emb"] = np.vstack(
+#                             [
+#                                 adata[time].obsm["X_emb"].mean(axis=0)
+#                                 for time in times_index
+#                                 if time.shape[0] > 0
+#                             ]
+#                         )
+#                         # print('----------dd------')
+
+#                         # print(adata_new.shape)
+#                         # print(adata_new.obsm['X_umap'])
+#                         adata_new.obsm["clone_vector_emb"] = np.vstack(
+#                             [
+#                                 adata_new.obsm["X_emb"][i + 1]
+#                                 - adata_new.obsm["X_emb"][i]
+#                                 for i in range(
+#                                     adata_new.obsm["X_emb"].shape[0] - 1
+#                                 )
+#                             ]
+#                             + [np.zeros(2)]
+#                         )
+#                         # print('----------ee------')
+#                         # print(adata_new.obsm['clone_vector_emb'])
+#                     else:
+#                         # print('pass-------')
+#                         continue
+
+#                 else:
+#                     time2 = np.where(
+#                         (adata.obs.time == t) & (adata.obs.clonei == 1)
+#                     )[0]
+#                     time4 = np.where(
+#                         (adata.obs.time == 4) & (adata.obs.clonei == 1)
+#                     )[0]
+#                     time6 = np.where(
+#                         (adata.obs.time == 6) & (adata.obs.clonei == 1)
+#                     )[0]
+#                     adata_new = AnnData(
+#                         np.vstack(
+#                             [
+#                                 adata[time2].X.toarray().mean(axis=0),
+#                                 adata[time4].X.toarray().mean(axis=0),
+#                                 adata[time6].X.toarray().mean(axis=0),
+#                             ]
+#                         ),
+#                         layers={
+#                             "spliced": np.vstack(
+#                                 [
+#                                     adata[time2]
+#                                     .layers["spliced"]
+#                                     .toarray()
+#                                     .mean(axis=0),
+#                                     adata[time4]
+#                                     .layers["spliced"]
+#                                     .toarray()
+#                                     .mean(axis=0),
+#                                     adata[time6]
+#                                     .layers["spliced"]
+#                                     .toarray()
+#                                     .mean(axis=0),
+#                                 ]
+#                             ),
+#                             "unspliced": np.vstack(
+#                                 [
+#                                     adata[time2]
+#                                     .layers["unspliced"]
+#                                     .toarray()
+#                                     .mean(axis=0),
+#                                     adata[time4]
+#                                     .layers["unspliced"]
+#                                     .toarray()
+#                                     .mean(axis=0),
+#                                     adata[time6]
+#                                     .layers["unspliced"]
+#                                     .toarray()
+#                                     .mean(axis=0),
+#                                 ]
+#                             ),
+#                         },
+#                         var=adata.var,
+#                     )
+
+#                     print(adata_new.X.sum(axis=1))
+#                     adata_new.obs.loc[:, "time"] = [2, 4, 6]
+#                     adata_new.obs.loc[:, "Cell type annotation"] = "Centroid"
+#                     if not global_traj:
+#                         adata_new.obs.loc[:, "clonetype"] = (
+#                             adata[time6].obs.clonetype.unique()
+#                         )  # use cell fate from last time point
+#                     adata_new.obs.loc[:, "clones"] = j
+
+#                     if "noWell" in adata[time6].obs.columns:
+#                         adata_new.obs.loc[:, "Well"] = adata[
+#                             time6
+#                         ].obs.Well.unique()
+
+#                     adata_new.obsm["X_umap"] = np.vstack(
+#                         [
+#                             adata[time2].obsm["X_umap"].mean(axis=0),
+#                             adata[time4].obsm["X_umap"].mean(axis=0),
+#                             adata[time6].obsm["X_umap"].mean(axis=0),
+#                         ]
+#                     )
+#                     adata_new.obsm["velocity_umap"] = np.vstack(
+#                         [
+#                             adata.obsm["X_umap"][time4].mean(axis=0)
+#                             - adata.obsm["X_umap"][time2].mean(axis=0),
+#                             adata.obsm["X_umap"][time6].mean(axis=0)
+#                             - adata.obsm["X_umap"][time4].mean(axis=0),
+#                             np.zeros(2),
+#                         ]
+#                     )
+
+#                     # print(adata_new.obsm['velocity_umap'])
+#                     clone_new = AnnData(
+#                         np.vstack(
+#                             [
+#                                 clone_adata[time2].X.toarray().mean(axis=0),
+#                                 clone_adata[time4].X.toarray().mean(axis=0),
+#                                 clone_adata[time6].X.toarray().mean(axis=0),
+#                             ]
+#                         ),
+#                         obs=adata_new.obs,
+#                     )
+#                     clone_new.var_names = clone_adata.var_names
+#                     clone_new.var = clone_adata.var
+#                     cen_clones.append(clone_new)
+#                 centroids.append(adata_new)
+#         print(adata.shape)
+#         print(len(centroids))
+#         adata_new = adata.concatenate(
+#             centroids[0].concatenate(centroids[1:]), join="outer"
+#         )
+#         return adata_new
diff --git a/src/pyrovelocity/plots/_vector_fields.py b/src/pyrovelocity/plots/_vector_fields.py
index 3f53deb02..125988a19 100644
--- a/src/pyrovelocity/plots/_vector_fields.py
+++ b/src/pyrovelocity/plots/_vector_fields.py
@@ -1,6 +1,6 @@
 from os import PathLike
-from typing import Dict
 
+import matplotlib
 import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
@@ -8,53 +8,105 @@
 import seaborn as sns
 from anndata import AnnData
 from beartype import beartype
-from matplotlib import cm
+from beartype.typing import Any, Dict, List, Optional, Tuple
+from matplotlib.axes import Axes
 from matplotlib.colors import Normalize
 from matplotlib.figure import FigureBase
+from matplotlib.gridspec import SubplotSpec
 from matplotlib.ticker import MaxNLocator
 from scvelo.plotting.velocity_embedding_grid import default_arrow
 
 from pyrovelocity.analysis.analyze import compute_mean_vector_field
 from pyrovelocity.logging import configure_logging
+from pyrovelocity.plots._time import plot_posterior_time
 from pyrovelocity.plots._uncertainty import (
     get_posterior_sample_angle_uncertainty,
 )
-
-
-logger = configure_logging(__name__)
-
+from pyrovelocity.styles import configure_matplotlib_style
+from pyrovelocity.utils import quartile_coefficient_of_dispersion, setup_colors
 
 __all__ = [
     "plot_vector_field_summary",
-    "plot_vector_field_uncertain",
+    "plot_vector_field_uncertainty",
     "plot_mean_vector_field",
-    "plot_arrow_examples",
 ]
 
+logger = configure_logging(__name__)
+
+configure_matplotlib_style()
+
+
+@beartype
+def create_vector_field_summary_layout(
+    fig: Optional[FigureBase] = None,
+    gs: Optional[SubplotSpec] = None,
+    fig_width: int | float = 7.5,
+    fig_height: int | float = 1.08,
+) -> Tuple[FigureBase, List[Axes], Axes, List[Axes]]:
+    if fig is None:
+        fig = plt.figure(
+            figsize=(fig_width, fig_height),
+        )
+    if gs is None:
+        gsi = fig.add_gridspec(
+            nrows=2,
+            ncols=6,
+            width_ratios=[1] * 6,
+            height_ratios=[10, 1],
+            wspace=0.1,
+            hspace=0.0,
+        )
+    else:
+        gsi = gs.subgridspec(
+            nrows=2,
+            ncols=6,
+            width_ratios=[1] * 6,
+            height_ratios=[10, 1],
+            wspace=0.1,
+            hspace=0.0,
+        )
+    main_axes = [fig.add_subplot(gsi[0, i]) for i in range(6)]
+    legend_axes = fig.add_subplot(gsi[1, :3])
+    colorbar_axes = [fig.add_subplot(gsi[1, i]) for i in range(3, 6)]
+    all_axes = main_axes + [legend_axes] + colorbar_axes
+    for ax in all_axes:
+        ax.set_label("vector_field")
+
+    return fig, main_axes, legend_axes, colorbar_axes
+
 
 @beartype
 def plot_vector_field_summary(
     adata: AnnData,
     posterior_samples: Dict[str, np.ndarray],
     vector_field_basis: str,
-    plot_name: PathLike | str,
+    plot_name: Optional[PathLike | str] = None,
     cell_state: str = "cell_type",
+    state_color_dict: Optional[Dict[str, Any]] = None,
+    fig: Optional[FigureBase] = None,
+    gs: Optional[SubplotSpec] = None,
+    default_fontsize: int = 7 if matplotlib.rcParams["text.usetex"] else 6,
+    default_title_padding: int = 5,
+    dotsize: int | float = 1,
+    scale: float = 0.35,
+    arrow_size: float = 3,
+    density: float = 0.4,
+    save_fig: bool = False,
+    linewidth: float = 0.5,
+    title_background_color: str = "#F0F0F0",
+    force_complete_angular_scale: bool = False,
 ) -> FigureBase:
-    # posterior_vector_field = posterior_samples["vector_field_posterior_samples"]
     posterior_time = posterior_samples["cell_time"]
-    cell_magnitudes = posterior_samples["original_spaces_embeds_magnitude"]
     pca_embeds_angle = posterior_samples["pca_embeds_angle"]
-    # embed_radians = posterior_samples["embeds_angle"]
     embed_mean = posterior_samples["vector_field_posterior_mean"]
 
-    dot_size = 3.5
-    font_size = 6.5
-    scale = 0.35
-    scale_high = 7.8
-    scale_low = 7.8
+    (
+        fig,
+        ax,
+        legend_axes,
+        colorbar_axes,
+    ) = create_vector_field_summary_layout(fig=fig, gs=gs)
 
-    arrow = 3.6
-    density = 0.4
     ress = pd.DataFrame(
         {
             "cell_type": adata.obs[cell_state].values,
@@ -62,147 +114,266 @@ def plot_vector_field_summary(
             "X2": adata.obsm[f"X_{vector_field_basis}"][:, 1],
         }
     )
-    fig = plt.figure(figsize=(9.6, 2), constrained_layout=False)
-    fig.subplots_adjust(
-        hspace=0.2, wspace=0.1, left=0.01, right=0.99, top=0.99, bottom=0.45
-    )
-    ax = fig.subplots(1, 6)
-    pos = ax[0].get_position()
+
+    if state_color_dict is None:
+        state_color_dict = setup_colors(adata, cell_state)
 
     sns.scatterplot(
         x="X1",
         y="X2",
+        hue="cell_type",
+        s=dotsize,
+        palette=state_color_dict,
         data=ress,
         alpha=0.9,
-        s=dot_size,
         linewidth=0,
         edgecolor="none",
-        hue="cell_type",
         ax=ax[0],
         legend="brief",
     )
+    ax[0].get_legend().remove()
+    ax[0].set_xticklabels([])
+    ax[0].set_xlabel("")
     ax[0].axis("off")
-    ax[0].set_title("Cell types\n", fontsize=font_size)
-    ax[0].legend(
-        loc="lower left",
-        bbox_to_anchor=(0.5, -0.48),
-        ncol=5,
-        fancybox=True,
-        prop={"size": font_size},
-        fontsize=font_size,
-        frameon=False,
+    ax[0].set_title(
+        "Cell types",
+        fontsize=default_fontsize,
+        pad=default_title_padding,
     )
-    kwargs = dict(
+
+    scv.pl.velocity_embedding_grid(
+        adata,
+        basis=vector_field_basis,
+        fontsize=default_fontsize,
+        ax=ax[1],
+        title="",
         color="gray",
-        s=dot_size,
+        s=dotsize,
         show=False,
         alpha=0.25,
         min_mass=3.5,
         scale=scale,
         frameon=False,
         density=density,
-        arrow_size=3,
-        linewidth=1,
+        arrow_size=arrow_size,
+        linewidth=linewidth,
     )
-    scv.pl.velocity_embedding_grid(
-        adata,
-        basis=vector_field_basis,
-        fontsize=font_size,
-        ax=ax[1],
-        title="",
-        **kwargs,
+    ax[1].set_xticklabels([])
+    ax[1].set_xlabel("")
+    ax[1].axis("off")
+    ax[1].set_title(
+        "scVelo",
+        fontsize=default_fontsize,
+        pad=default_title_padding,
     )
-    ax[1].set_title("Scvelo\n", fontsize=7)
     scv.pl.velocity_embedding_grid(
         adata,
-        fontsize=font_size,
+        fontsize=default_fontsize,
         basis=vector_field_basis,
         title="",
         ax=ax[2],
         vkey="velocity_pyro",
-        **kwargs,
+        color="gray",
+        s=dotsize,
+        show=False,
+        alpha=0.25,
+        min_mass=3.5,
+        scale=scale,
+        frameon=False,
+        density=density,
+        arrow_size=arrow_size,
+        linewidth=linewidth,
+    )
+    ax[2].set_xticklabels([])
+    ax[2].set_xlabel("")
+    ax[2].axis("off")
+    ax[2].set_title(
+        rf"Pyro\thinspace-Velocity"
+        if matplotlib.rcParams["text.usetex"]
+        else f"Pyro\u2009-Velocity",
+        fontsize=default_fontsize,
+        pad=default_title_padding,
+        backgroundcolor=title_background_color,
     )
-    ax[2].set_title("Pyro-Velocity\n", fontsize=7)
 
-    pca_cell_angles = pca_embeds_angle / np.pi * 180  # degree
+    pca_cell_angles = pca_embeds_angle / np.pi * 180
     pca_angles_std = get_posterior_sample_angle_uncertainty(pca_cell_angles)
 
-    cell_time_mean = posterior_time.mean(0).flatten()
-    cell_time_std = posterior_time.std(0).flatten()
-    cell_time_cov = cell_time_std / cell_time_mean
-
-    plot_vector_field_uncertain(
-        adata,
-        embed_mean,
-        cell_time_std,
+    plot_vector_field_uncertainty(
+        adata=adata,
+        embed_mean=embed_mean,
+        embeds_radian_or_magnitude=pca_angles_std,
         ax=ax[3],
-        cbar=True,
+        cbar=False,
         fig=fig,
         basis=vector_field_basis,
         scale=scale,
-        arrow_size=arrow,
+        arrow_size=arrow_size,
         p_mass_min=1,
         autoscale=True,
         density=density,
         only_grid=False,
-        uncertain_measure="shared time",
-        cmap="winter",
+        uncertain_measure="PCA angle",
+        cmap="inferno",
         cmax=None,
+        show_titles=False,
+        force_complete_angular_scale=force_complete_angular_scale,
+    )
+    ax[3].set_title(
+        r"PCA angle uncertainty"
+        if matplotlib.rcParams["text.usetex"]
+        else "PCA angle σ",
+        fontsize=default_fontsize,
+        pad=default_title_padding,
+        backgroundcolor=title_background_color,
     )
 
-    cell_magnitudes_mean = cell_magnitudes.mean(axis=-2)
-    cell_magnitudes_std = cell_magnitudes.std(axis=-2)
-    cell_magnitudes_cov = cell_magnitudes_std / cell_magnitudes_mean
-    plot_vector_field_uncertain(
+    plot_posterior_time(
+        posterior_samples,
         adata,
-        embed_mean,
-        cell_magnitudes_cov,
         ax=ax[4],
-        cbar=True,
-        fig=fig,
         basis=vector_field_basis,
-        scale=scale,
-        arrow_size=arrow,
-        p_mass_min=1,
-        autoscale=True,
-        density=density,
-        only_grid=False,
-        uncertain_measure="base magnitude",
-        cmap="summer",
-        cmax=None,
+        fig=fig,
+        addition=False,
+        position="right",
+        cmap="winter",
+        s=dotsize,
+        show_colorbar=False,
+        show_titles=False,
+        alpha=1,
+    )
+    ax[4].set_title(
+        r"Shared time mean"
+        # r"shared time $\hat{\mu}(t)$"
+        if matplotlib.rcParams["text.usetex"]
+        else "Shared time μ",
+        fontsize=default_fontsize,
+        pad=default_title_padding,
+        backgroundcolor=title_background_color,
     )
 
-    plot_vector_field_uncertain(
-        adata,
-        embed_mean,
-        pca_angles_std,
+    cell_time_mean = posterior_time.mean(0).flatten()
+    cell_time_mean_max = cell_time_mean.max()
+    cell_times = posterior_time / cell_time_mean_max
+    # cell_time_mean = cell_times.mean(0).flatten()
+    # cell_time_std = cell_times.std(0).flatten()
+    # cell_time_cov = cell_time_std / cell_time_mean
+    cell_time_qcd = quartile_coefficient_of_dispersion(cell_times).flatten()
+
+    plot_vector_field_uncertainty(
+        adata=adata,
+        embed_mean=embed_mean,
+        # embeds_radian_or_magnitude=cell_time_cov,
+        embeds_radian_or_magnitude=cell_time_qcd,
         ax=ax[5],
-        cbar=True,
+        cbar=False,
         fig=fig,
         basis=vector_field_basis,
         scale=scale,
-        arrow_size=arrow,
+        arrow_size=arrow_size,
         p_mass_min=1,
         autoscale=True,
         density=density,
         only_grid=False,
-        uncertain_measure="PCA angle",
-        cmap="inferno",
+        uncertain_measure="shared time",
+        cmap="winter",
         cmax=None,
+        show_titles=False,
+    )
+    ax[5].set_title(
+        r"Shared time uncertainty"
+        if matplotlib.rcParams["text.usetex"]
+        else "shared time σ/μ",
+        fontsize=default_fontsize,
+        pad=default_title_padding,
+        backgroundcolor=title_background_color,
     )
-    for ext in ["", ".png"]:
-        fig.savefig(
-            f"{plot_name}{ext}",
-            facecolor=fig.get_facecolor(),
-            bbox_inches="tight",
-            edgecolor="none",
-            dpi=300,
+
+    handles, labels = ax[0].get_legend_handles_labels()
+    legend_axes.legend(
+        handles=handles,
+        labels=labels,
+        loc="upper left",
+        bbox_to_anchor=(-0.05, 2.0),
+        # bbox_transform=bottom_axes[0].transAxes,
+        bbox_transform=legend_axes.transAxes,
+        ncol=5,
+        frameon=False,
+        fancybox=True,
+        markerscale=3,
+        columnspacing=0.05,
+        handletextpad=-0.5,
+        fontsize=default_fontsize * 0.9,
+    )
+    legend_axes.axis("off")
+
+    for axi in ax:
+        axi.set_aspect("equal", adjustable="box")
+
+    colorbar_labels = [
+        r"$\hat{\sigma}$",
+        r"$\mu(t)$",
+        r"$\left.\hat{\sigma}(t) \right/ \hat{\mu}(t)$",
+    ]
+    colorbar_ticks = [
+        [0, 360] if force_complete_angular_scale else [],
+        [0, 1],
+        [],
+    ]
+    for axi, cax, clabel, cticks in zip(
+        ax[3:],
+        colorbar_axes,
+        colorbar_labels,
+        colorbar_ticks,
+    ):
+        ax_pos = axi.get_position()
+        cax.axis("on")
+        cbar = fig.colorbar(
+            mappable=axi.collections[0],
+            cax=cax,
+            orientation="horizontal",
         )
-    plt.close(fig)
+
+        if len(cticks) > 0:
+            vmin, vmax = axi.collections[0].get_clim()
+            cbar.set_ticks([vmin, vmax])
+            cbar.set_ticklabels([rf"{cticks[0]}", rf"{cticks[1]}"])
+        else:
+            cbar.locator = MaxNLocator(nbins=3)
+            cbar.update_ticks()
+
+        cax.xaxis.set_ticks_position("bottom")
+        cax.xaxis.set_label_position("bottom")
+        cax.xaxis.set_tick_params(labelsize=default_fontsize * 0.8)
+        # cbar.set_label(label=clabel, fontsize=default_fontsize, labelpad=0)
+        # TODO: support colorbar with width specified as a fraction of the axis width
+        # cbar_width = ax_pos.width * 0.6
+        # cbar_height = ax_pos.height * 0.10
+        # cax.set_position(
+        #     [
+        #         ax_pos.x0 + (ax_pos.width - cbar_width),
+        #         ax_pos.y0 - cbar_height * 1.05,
+        #         cbar_width,
+        #         cbar_height,
+        #     ]
+        # )
+
+    if save_fig:
+        fig.tight_layout()
+
+        for ext in ["", ".png"]:
+            fig.savefig(
+                f"{plot_name}{ext}",
+                facecolor=fig.get_facecolor(),
+                bbox_inches="tight",
+                edgecolor="none",
+                dpi=300,
+            )
+        plt.close(fig)
     return fig
 
 
-def plot_vector_field_uncertain(
+def plot_vector_field_uncertainty(
     adata,
     embed_mean,
     embeds_radian_or_magnitude,
@@ -220,115 +391,151 @@ def plot_vector_field_uncertain(
     uncertain_measure="angle",
     cmap="winter",
     cmax=0.305,
+    color_vector_field_by_measure=False,
+    dotsize=1,
+    show_titles: bool = True,
+    default_fontsize: int = 7,
+    plot_individual_obs: bool = False,
+    force_complete_angular_scale: bool = False,
 ):
-    if cmap == "inferno":
-        colormap = cm.inferno
-    elif cmap == "summer":
-        colormap = cm.summer
-    else:
-        colormap = cm.winter
-
-    # print(adata.shape)
-    # print(embeds_radian_or_magnitude.shape)
-
     if uncertain_measure == "angle":
         adata.obs["uncertain"] = get_posterior_sample_angle_uncertainty(
             embeds_radian_or_magnitude / np.pi * 180
         )
+    elif uncertain_measure in ["base magnitude", "shared time", "PCA angle"]:
+        adata.obs["uncertain"] = embeds_radian_or_magnitude
     else:
         adata.obs["uncertain"] = embeds_radian_or_magnitude.std(axis=0)
 
-    if uncertain_measure in ["base magnitude", "shared time", "PCA angle"]:
-        adata.obs["uncertain"] = embeds_radian_or_magnitude
-
-    dot_size = 1
-    # plt.rcParams["image.cmap"] = "winter"
     if ax is None:
         ax = fig.subplots(1, 2)
     if isinstance(ax, list) and len(ax) == 2:
         if not only_grid:
-            # norm = Normalize()
-            # norm.autoscale(adata.obs["uncertain"])
             order = np.argsort(adata.obs["uncertain"].values)
             im = ax[0].scatter(
                 adata.obsm[f"X_{basis}"][:, 0][order],
                 adata.obsm[f"X_{basis}"][:, 1][order],
-                # c=colormap(norm(adata.obs["uncertain"].values[order])),
                 c=adata.obs["uncertain"].values[order],
                 cmap=cmap,
                 norm=None,
                 vmin=np.percentile(uncertain, 5),
                 vmax=np.percentile(uncertain, 95),
-                s=dot_size,
+                s=dotsize,
                 linewidth=1,
-                edgecolors="face",
+                edgecolors="none",
             )
             ax[0].axis("off")
-            ax[0].set_title(
-                f"Single-cell\n {uncertain_measure} uncertainty ", fontsize=7
-            )
+            if show_titles:
+                ax[0].set_title(
+                    # f"Single-cell\n {uncertain_measure} uncertainty ",
+                    f"{uncertain_measure} uncertainty",
+                    fontsize=default_fontsize,
+                )
             ax = ax[1]
 
-    X_grid, V_grid, uncertain = project_grid_points(
-        adata.obsm[f"X_{basis}"],
-        embed_mean,
-        adata.obs["uncertain"].values,
-        p_mass_min=p_mass_min,
-        autoscale=autoscale,
-        density=density,
-    )
+    if color_vector_field_by_measure:
+        X_grid, V_grid, uncertain = project_grid_points(
+            adata.obsm[f"X_{basis}"],
+            embed_mean,
+            adata.obs["uncertain"].values,
+            p_mass_min=p_mass_min,
+            autoscale=autoscale,
+            density=density,
+        )
 
-    # scale = None
-    hl, hw, hal = default_arrow(arrow_size)
-    quiver_kwargs = {"angles": "xy", "scale_units": "xy"}
-    # quiver_kwargs = {"angles": "xy", "scale_units": "width"}
-    quiver_kwargs.update({"width": 0.001, "headlength": hl / 2})
-    quiver_kwargs.update({"headwidth": hw / 2, "headaxislength": hal / 2})
-    quiver_kwargs.update({"linewidth": 1, "zorder": 3})
-    norm = Normalize()
-    norm.autoscale(uncertain)
-    ax.scatter(
-        adata.obsm[f"X_{basis}"][:, 0],
-        adata.obsm[f"X_{basis}"][:, 1],
-        s=1,
-        linewidth=0,
-        color="gray",
-        alpha=0.22,
-    )
-    im = ax.quiver(
-        X_grid[:, 0],
-        X_grid[:, 1],
-        V_grid[:, 0],
-        V_grid[:, 1],
-        uncertain,
-        norm=None,
-        cmap=cmap,
-        edgecolors="face",
-        scale=scale,
-        clim=(
-            np.percentile(uncertain, 5),
-            np.percentile(uncertain, 95) if cmax is None else cmax,
-        ),
-        **quiver_kwargs,
-    )
-    ax.set_title(f"Averaged\n {uncertain_measure} uncertainty ", fontsize=7)
-    ax.axis("off")
+        hl, hw, hal = default_arrow(arrow_size)
+        quiver_kwargs = {"angles": "xy", "scale_units": "xy"}
+        quiver_kwargs.update({"width": 0.001, "headlength": hl / 2})
+        quiver_kwargs.update({"headwidth": hw / 2, "headaxislength": hal / 2})
+        quiver_kwargs.update({"linewidth": 1, "zorder": 3})
+        norm = Normalize()
+        norm.autoscale(uncertain)
+        ax.scatter(
+            adata.obsm[f"X_{basis}"][:, 0],
+            adata.obsm[f"X_{basis}"][:, 1],
+            s=1,
+            linewidth=0,
+            color="gray",
+            alpha=0.22,
+        )
+        im = ax.quiver(
+            X_grid[:, 0],
+            X_grid[:, 1],
+            V_grid[:, 0],
+            V_grid[:, 1],
+            uncertain,
+            norm=None,
+            cmap=cmap,
+            edgecolors="face",
+            scale=scale,
+            clim=(
+                np.percentile(uncertain, 5),
+                np.percentile(uncertain, 95) if cmax is None else cmax,
+            ),
+            **quiver_kwargs,
+        )
+        if show_titles:
+            ax.set_title(
+                f"Averaged\n {uncertain_measure} uncertainty ",
+                fontsize=default_fontsize,
+            )
+        ax.axis("off")
+    else:
+        order = np.argsort(adata.obs["uncertain"].values)
+        ordered_uncertainty_measure = adata.obs["uncertain"].values[order]
+        if plot_individual_obs:
+            im = ax.scatter(
+                adata.obsm[f"X_{basis}"][:, 0][order],
+                adata.obsm[f"X_{basis}"][:, 1][order],
+                c=ordered_uncertainty_measure,
+                cmap=cmap,
+                norm=None,
+                vmin=0
+                if "angle" in uncertain_measure
+                else min(ordered_uncertainty_measure),
+                vmax=360
+                if force_complete_angular_scale
+                else max(ordered_uncertainty_measure),
+                s=dotsize,
+                linewidth=1,
+                edgecolors="none",
+            )
+        else:
+            im = ax.hexbin(
+                adata.obsm[f"X_{basis}"][:, 0][order],
+                adata.obsm[f"X_{basis}"][:, 1][order],
+                C=ordered_uncertainty_measure,
+                gridsize=100,
+                cmap=cmap,
+                vmin=0
+                if "angle" in uncertain_measure
+                else min(ordered_uncertainty_measure),
+                vmax=360
+                if force_complete_angular_scale
+                else max(ordered_uncertainty_measure),
+                linewidths=0,
+                edgecolors="none",
+            )
+
+        ax.axis("off")
+        if show_titles:
+            ax.set_title(
+                # f"Single-cell\n {uncertain_measure} uncertainty ", fontsize=default_fontsize
+                f"{uncertain_measure} uncertainty",
+                fontsize=default_fontsize,
+            )
     if cbar:
-        # divider = make_axes_locatable(ax)
-        # cax = divider.append_axes('bottom', size='5%', pad=0.1)
-        # cbar = fig.colorbar(im, cax=cax, orientation="horizontal", shrink=0.6)
-        ### cbar.ax.set_xticks([0, 180, 360], [0, 180, 360])
-        ##fig.colorbar(im, ax=ax, shrink=0.6, location='bottom')
         pos = ax.get_position()
-        cbar_ax = fig.add_axes(
-            [pos.x0 + 0.05, pos.y0 - 0.02, pos.width * 0.6, pos.height / 17]
+        cax = fig.add_axes(
+            [pos.x0 + 0.05, pos.y0 - 0.05, pos.width * 0.6, pos.height / 17]
         )
+
         cbar = fig.colorbar(
-            im, cax=cbar_ax, orientation="horizontal"
+            im, cax=cax, orientation="horizontal"
         )  # fraction=0.046, pad=0.04
-        cbar.ax.tick_params(axis="x", labelsize=5.5)
+        cbar.ax.tick_params(axis="x", labelsize=default_fontsize * 0.8)
         cbar.ax.locator = MaxNLocator(nbins=2, integer=True)
-    # cbar.ax.set_xlabel(f"{uncertain_measure} uncertainty", fontsize=7)
+        # cbar.ax.set_xlabel(f"{uncertain_measure} uncertainty", fontsize=default_fontsize)
 
 
 def plot_mean_vector_field(
@@ -365,7 +572,6 @@ def plot_mean_vector_field(
     return adata.obsm[f"velocity_pyro_{basis}"]
 
 
-# def project_grid_points(emb, velocity_emb, uncertain=None, p_mass_min=3.5, density=0.3):
 def project_grid_points(
     emb,
     velocity_emb,
@@ -385,8 +591,6 @@ def project_grid_points(
 
     for dim_i in range(2):
         m, M = np.min(emb[:, dim_i]), np.max(emb[:, dim_i])
-        # m = m - .025 * np.abs(M - m)
-        # M = M + .025 * np.abs(M - m)
         m = m - 0.01 * np.abs(M - m)
         M = M + 0.01 * np.abs(M - m)
         gr = np.linspace(m, M, int(grid_num))
@@ -396,8 +600,6 @@ def project_grid_points(
     X_grid = np.vstack([i.flat for i in meshes_tuple]).T
 
     n_neighbors = int(emb.shape[0] / 50)
-    # print(n_neighbors)
-    # nn = NearestNeighbors(n_neighbors=30, n_jobs=-1)
     nn = NearestNeighbors(n_neighbors=n_neighbors, n_jobs=-1)
     nn.fit(emb)
     dists, neighs = nn.kneighbors(X_grid)
@@ -412,7 +614,6 @@ def project_grid_points(
         V_grid = (velocity_emb[:, :2][neighs] * weight[:, :, None, None]).sum(
             1
         ) / np.maximum(1, p_mass)[:, None, None]
-    # print(V_grid.shape)
 
     p_mass_min *= np.percentile(p_mass, 99) / 100
     if autoscale:
@@ -429,142 +630,175 @@ def project_grid_points(
         )
 
 
-def plot_arrow_examples(
-    adata,
-    v_maps,
-    embeds_radian,
-    embed_mean,
-    ax=None,
-    fig=None,
-    cbar=True,
-    basis="umap",
-    n_sample=30,
-    scale=0.0021,
-    alpha=0.02,
-    index=19,
-    index2=0,
-    scale2=0.04,
-    num_certain=3,
-    num_total=4,
-    p_mass_min=1.0,
-    density=0.3,
-    arrow_size=4,
-    customize_uncertain=None,
-):
-    X_grid, V_grid, uncertain = project_grid_points(
-        adata.obsm[f"X_{basis}"],
-        v_maps,
-        get_posterior_sample_angle_uncertainty(embeds_radian / np.pi * 180)
-        if customize_uncertain is None
-        else customize_uncertain,
-        p_mass_min=p_mass_min,
-        density=density,
-    )
-    # print(X_grid.shape, V_grid.shape, uncertain.shape)
-    norm = Normalize()
-    norm.autoscale(uncertain)
-    colormap = cm.inferno
-
-    indexes = np.argsort(uncertain)[::-1][
-        index : (index + num_total - num_certain)
-    ]
-    hl, hw, hal = default_arrow(arrow_size)
-    # print(hl, hw, hal)
-    quiver_kwargs = {"angles": "xy", "scale_units": "xy"}
-    # quiver_kwargs = {"angles": "xy", "scale_units": "width"}
-    quiver_kwargs = {"width": 0.002, "zorder": 0}
-    quiver_kwargs.update({"headlength": hl / 2})
-    quiver_kwargs.update({"headwidth": hw / 2, "headaxislength": hal / 2})
-
-    ax.scatter(
-        adata.obsm[f"X_{basis}"][:, 0],
-        adata.obsm[f"X_{basis}"][:, 1],
-        s=1,
-        linewidth=0,
-        color="gray",
-        alpha=alpha,
-    )
-
-    # normalize arrow size the constant
-    V_grid[:, 0] = V_grid[:, 0] / np.sqrt(V_grid[:, 0] ** 2 + V_grid[:, 1] ** 2)
-    V_grid[:, 1] = V_grid[:, 1] / np.sqrt(V_grid[:, 1] ** 2 + V_grid[:, 1] ** 2)
-
-    for i in range(n_sample):
-        for j in indexes:
-            # ax.quiver(
-            #    X_grid[j, 0],
-            #    X_grid[j, 1],
-            #    embed_mean[j, 0],
-            #    embed_mean[j, 1],
-            #    ec='black',
-            #    scale=scale,
-            #    color=colormap(norm(uncertain))[j],
-            #    **quiver_kwargs,
-            # )
-            ax.quiver(
-                X_grid[j, 0],
-                X_grid[j, 1],
-                V_grid[j][0][i],
-                V_grid[j][1][i],
-                ec="face",
-                norm=Normalize(vmin=0, vmax=360),
-                scale=scale,
-                color=colormap(norm(uncertain))[j],
-                linewidth=0,
-                alpha=0.3,
-                **quiver_kwargs,
-            )
-        ax.quiver(
-            X_grid[j, 0],
-            X_grid[j, 1],
-            V_grid[j][0].mean(),
-            V_grid[j][1].mean(),
-            ec="black",
-            alpha=1,
-            norm=Normalize(vmin=0, vmax=360),
-            scale=scale,
-            linewidth=0,
-            color=colormap(norm(uncertain))[j],
-            **quiver_kwargs,
-        )
-    indexes = np.argsort(uncertain)[index2 : (index2 + num_certain)]
-    for i in range(n_sample):
-        for j in indexes:
-            # ax.quiver(
-            #    X_grid[j, 0],
-            #    X_grid[j, 1],
-            #    embed_mean[j, 0],
-            #    embed_mean[j, 1],
-            #    ec='black',
-            #    scale=scale,
-            #    color=colormap(norm(uncertain))[j],
-            #    **quiver_kwargs,
-            # )
-            ax.quiver(
-                X_grid[j, 0],
-                X_grid[j, 1],
-                V_grid[j][0][i],
-                V_grid[j][1][i],
-                # ec=colormap(norm(uncertain))[j],
-                ec="face",
-                scale=scale2,
-                alpha=0.3,
-                linewidth=0,
-                color=colormap(norm(uncertain))[j],
-                norm=Normalize(vmin=0, vmax=360),
-                **quiver_kwargs,
-            )
-        ax.quiver(
-            X_grid[j, 0],
-            X_grid[j, 1],
-            V_grid[j][0].mean(),
-            V_grid[j][1].mean(),
-            ec="black",
-            alpha=1,
-            linewidth=0,
-            norm=Normalize(vmin=0, vmax=360),
-            scale=scale2,
-            color=colormap(norm(uncertain))[j],
-            **quiver_kwargs,
-        )
-    ax.axis("off")
+# TODO: remove unused code
+# cell_magnitudes = posterior_samples["original_spaces_embeds_magnitude"]
+# cell_magnitudes_mean = cell_magnitudes.mean(axis=-2)
+# cell_magnitudes_std = cell_magnitudes.std(axis=-2)
+# cell_magnitudes_cov = cell_magnitudes_std / cell_magnitudes_mean
+# plot_vector_field_uncertainty(
+#     adata,
+#     embed_mean,
+#     cell_magnitudes_cov,
+#     ax=ax[4],
+#     cbar=False,
+#     fig=fig,
+#     basis=vector_field_basis,
+#     scale=scale,
+#     arrow_size=arrow_size,
+#     p_mass_min=1,
+#     autoscale=True,
+#     density=density,
+#     only_grid=False,
+#     uncertain_measure="base magnitude",
+#     cmap="summer",
+#     cmax=None,
+#     show_titles=False,
+# )
+# ax[4].set_title(
+#     r"base magnitude $\sigma$"
+#     if matplotlib.rcParams["text.usetex"]
+#     else "base magnitude σ",
+#     fontsize=default_fontsize,
+#     pad=default_title_padding,
+# )
+
+
+# def plot_arrow_examples(
+#     adata,
+#     v_maps,
+#     embeds_radian,
+#     embed_mean,
+#     ax=None,
+#     fig=None,
+#     cbar=True,
+#     basis="umap",
+#     n_sample=30,
+#     scale=0.0021,
+#     alpha=0.02,
+#     index=19,
+#     index2=0,
+#     scale2=0.04,
+#     num_certain=3,
+#     num_total=4,
+#     p_mass_min=1.0,
+#     density=0.3,
+#     arrow_size=4,
+#     customize_uncertain=None,
+# ):
+#     X_grid, V_grid, uncertain = project_grid_points(
+#         adata.obsm[f"X_{basis}"],
+#         v_maps,
+#         get_posterior_sample_angle_uncertainty(embeds_radian / np.pi * 180)
+#         if customize_uncertain is None
+#         else customize_uncertain,
+#         p_mass_min=p_mass_min,
+#         density=density,
+#     )
+#     # print(X_grid.shape, V_grid.shape, uncertain.shape)
+#     norm = Normalize()
+#     norm.autoscale(uncertain)
+#     colormap = cm.inferno
+
+#     indexes = np.argsort(uncertain)[::-1][
+#         index : (index + num_total - num_certain)
+#     ]
+#     hl, hw, hal = default_arrow(arrow_size)
+#     # print(hl, hw, hal)
+#     quiver_kwargs = {"angles": "xy", "scale_units": "xy"}
+#     # quiver_kwargs = {"angles": "xy", "scale_units": "width"}
+#     quiver_kwargs = {"width": 0.002, "zorder": 0}
+#     quiver_kwargs.update({"headlength": hl / 2})
+#     quiver_kwargs.update({"headwidth": hw / 2, "headaxislength": hal / 2})
+
+#     ax.scatter(
+#         adata.obsm[f"X_{basis}"][:, 0],
+#         adata.obsm[f"X_{basis}"][:, 1],
+#         s=1,
+#         linewidth=0,
+#         color="gray",
+#         alpha=alpha,
+#     )
+
+#     # normalize arrow size the constant
+#     V_grid[:, 0] = V_grid[:, 0] / np.sqrt(V_grid[:, 0] ** 2 + V_grid[:, 1] ** 2)
+#     V_grid[:, 1] = V_grid[:, 1] / np.sqrt(V_grid[:, 1] ** 2 + V_grid[:, 1] ** 2)
+
+#     for i in range(n_sample):
+#         for j in indexes:
+#             # ax.quiver(
+#             #    X_grid[j, 0],
+#             #    X_grid[j, 1],
+#             #    embed_mean[j, 0],
+#             #    embed_mean[j, 1],
+#             #    ec='black',
+#             #    scale=scale,
+#             #    color=colormap(norm(uncertain))[j],
+#             #    **quiver_kwargs,
+#             # )
+#             ax.quiver(
+#                 X_grid[j, 0],
+#                 X_grid[j, 1],
+#                 V_grid[j][0][i],
+#                 V_grid[j][1][i],
+#                 ec="face",
+#                 norm=Normalize(vmin=0, vmax=360),
+#                 scale=scale,
+#                 color=colormap(norm(uncertain))[j],
+#                 linewidth=0,
+#                 alpha=0.3,
+#                 **quiver_kwargs,
+#             )
+#         ax.quiver(
+#             X_grid[j, 0],
+#             X_grid[j, 1],
+#             V_grid[j][0].mean(),
+#             V_grid[j][1].mean(),
+#             ec="black",
+#             alpha=1,
+#             norm=Normalize(vmin=0, vmax=360),
+#             scale=scale,
+#             linewidth=0,
+#             color=colormap(norm(uncertain))[j],
+#             **quiver_kwargs,
+#         )
+#     indexes = np.argsort(uncertain)[index2 : (index2 + num_certain)]
+#     for i in range(n_sample):
+#         for j in indexes:
+#             # ax.quiver(
+#             #    X_grid[j, 0],
+#             #    X_grid[j, 1],
+#             #    embed_mean[j, 0],
+#             #    embed_mean[j, 1],
+#             #    ec='black',
+#             #    scale=scale,
+#             #    color=colormap(norm(uncertain))[j],
+#             #    **quiver_kwargs,
+#             # )
+#             ax.quiver(
+#                 X_grid[j, 0],
+#                 X_grid[j, 1],
+#                 V_grid[j][0][i],
+#                 V_grid[j][1][i],
+#                 # ec=colormap(norm(uncertain))[j],
+#                 ec="face",
+#                 scale=scale2,
+#                 alpha=0.3,
+#                 linewidth=0,
+#                 color=colormap(norm(uncertain))[j],
+#                 norm=Normalize(vmin=0, vmax=360),
+#                 **quiver_kwargs,
+#             )
+#         ax.quiver(
+#             X_grid[j, 0],
+#             X_grid[j, 1],
+#             V_grid[j][0].mean(),
+#             V_grid[j][1].mean(),
+#             ec="black",
+#             alpha=1,
+#             linewidth=0,
+#             norm=Normalize(vmin=0, vmax=360),
+#             scale=scale2,
+#             color=colormap(norm(uncertain))[j],
+#             **quiver_kwargs,
+#         )
+#     ax.axis("off")
diff --git a/src/pyrovelocity/plots/layout.py b/src/pyrovelocity/plots/layout.py
new file mode 100644
index 000000000..f46a41bdf
--- /dev/null
+++ b/src/pyrovelocity/plots/layout.py
@@ -0,0 +1,321 @@
+from pathlib import Path
+
+import dill
+import matplotlib.pyplot as plt
+from beartype.typing import Dict, List, Optional, Tuple
+from matplotlib.axes import Axes
+from matplotlib.figure import FigureBase
+from matplotlib.gridspec import GridSpec
+
+from pyrovelocity.io.compressedpickle import CompressedPickle
+from pyrovelocity.styles import configure_matplotlib_style
+
+configure_matplotlib_style()
+
+__all__ = [
+    "plot_main",
+    "plot_subfigures",
+]
+
+
+def create_main_figure(
+    width: float,
+    height: float,
+    layout: Dict[str, List[float]],
+) -> Tuple[FigureBase, Dict[str, Axes]]:
+    """Create the main figure with all subplots."""
+    fig = plt.figure(figsize=(width, height))
+    gs = GridSpec(
+        figure=fig,
+        nrows=len(layout["height_ratios"]),
+        ncols=len(layout["width_ratios"]),
+        height_ratios=layout["height_ratios"],
+        width_ratios=layout["width_ratios"],
+    )
+
+    axes = {}
+    axes["ax1"] = fig.add_subplot(gs[0, :])
+    axes["ax2"] = fig.add_subplot(gs[1, 0])
+    axes["ax3"] = fig.add_subplot(gs[1, 1])
+    axes["ax4"] = fig.add_subplot(gs[1, 2])
+    axes["ax5"] = fig.add_subplot(gs[2, :])
+
+    for key, ax in axes.items():
+        ax.set_label(key)
+
+    return fig, axes
+
+
+def extract_subfigures(
+    buffer: Optional[bytes] = None,
+    axes_to_keep: List[str] = [],
+    main_fig: Optional[FigureBase] = None,
+    figure_file_path: Optional[str | Path] = None,
+) -> FigureBase:
+    """
+    Extract a subset of axes from the main figure using dill for serialization.
+
+    Args:
+        main_fig: The main Figure object
+        axes_to_keep: List of axes keys to keep in the new figure
+
+    Returns:
+        A new Figure object with only the specified axes
+    """
+
+    if buffer:
+        subfig = dill.loads(buffer)
+    elif main_fig:
+        buffer = dill.dumps(main_fig)
+        subfig = dill.loads(buffer)
+    else:
+        raise ValueError("Either buffer or main_fig must be provided.")
+
+    for text in subfig.texts[:]:
+        subfig.texts.remove(text)
+
+    axes_to_remove = [
+        ax for ax in subfig.axes if ax.get_label() not in axes_to_keep
+    ]
+
+    for ax in axes_to_remove:
+        subfig.delaxes(ax)
+
+    if figure_file_path:
+        with Path(figure_file_path).open("wb") as f:
+            dill.dump(subfig, f)
+
+    return subfig
+
+
+def plot_main(
+    figure_file_path: Path | str = "main_figure.dill.zst",
+) -> FigureBase:
+    """Create an example plot with a custom layout and demonstrate subplot extraction."""
+    width = 8.5 - 1
+    height = (11 - 1) * 0.9
+    layout = {
+        "height_ratios": [0.1, 0.3, 0.6],
+        "width_ratios": [0.5, 0.25, 0.25],
+    }
+
+    fig, axes = create_main_figure(width, height, layout)
+
+    plot_wide_row(axes["ax1"])
+    plot_small_cell(axes["ax2"])
+    plot_narrow_column(axes["ax3"])
+    plot_narrow_column(axes["ax4"])
+    plot_large_cell(axes["ax5"])
+
+    fig.tight_layout()
+
+    x_col1 = -0.015
+    y_row2 = 0.87
+    add_panel_label(fig, "a", x_col1, 1.00)
+    add_panel_label(fig, "b", x_col1, y_row2)
+    add_panel_label(fig, "c", 0.45, y_row2)
+    add_panel_label(fig, "d", 0.72, y_row2)
+    add_panel_label(fig, "e", x_col1, 0.57)
+
+    fig.savefig("example_plot_layout.pdf", format="pdf")
+    CompressedPickle.save(figure_file_path, fig)
+
+    return fig
+
+
+def plot_subfigures(figure_file_path: Path | str = "main_figure.dill.zst"):
+    fig = CompressedPickle.load(figure_file_path)
+    buffer = dill.dumps(fig)
+
+    subfig1 = extract_subfigures(buffer=buffer, axes_to_keep=["ax1"])
+    subfig1.savefig("extracted_ax1.pdf", format="pdf")
+
+    subfig23 = extract_subfigures(buffer=buffer, axes_to_keep=["ax2", "ax3"])
+    subfig23.savefig("extracted_ax2_ax3.pdf", format="pdf")
+
+    subfig34 = extract_subfigures(buffer=buffer, axes_to_keep=["ax3", "ax4"])
+    subfig34.savefig("extracted_ax3_ax4.pdf", format="pdf")
+
+    subfig15 = extract_subfigures(buffer=buffer, axes_to_keep=["ax1", "ax5"])
+    subfig15.savefig("extracted_ax1_ax5.pdf", format="pdf")
+
+    subfig5 = extract_subfigures(buffer=buffer, axes_to_keep=["ax5"])
+    subfig5.savefig("extracted_ax5.pdf", format="pdf")
+
+
+def example_plot_manual():
+    """
+    Create an example plot with a custom layout.
+
+    Each subplot in the gridspec grid may be labeled with a panel label
+    whose location is given in Figure-level coordinates.
+    """
+    n_rows = 3
+    n_cols = 3
+    width = 8.5 - 1
+    height = (11 - 1) * 0.9
+    row_1_fraction = 0.1
+    row_2_fraction = 0.3
+    row_3_fraction = 0.6
+
+    col_1_fraction = 0.5
+    col_2_fraction = 0.25
+    col_3_fraction = 0.25
+
+    fig = plt.figure(figsize=(width, height))
+    gs = GridSpec(
+        figure=fig,
+        nrows=n_rows,
+        height_ratios=[
+            row_1_fraction,
+            row_2_fraction,
+            row_3_fraction,
+        ],
+        ncols=n_cols,
+        width_ratios=[
+            col_1_fraction,
+            col_2_fraction,
+            col_3_fraction,
+        ],
+    )
+
+    fig_1 = plt.figure(
+        figsize=(
+            (col_2_fraction + col_3_fraction) * width,
+            (row_2_fraction) * height,
+        )
+    )
+    gs_1 = GridSpec(
+        figure=fig,
+        nrows=1,
+        height_ratios=[row_2_fraction],
+        ncols=2,
+        width_ratios=[
+            col_2_fraction,
+            col_3_fraction,
+        ],
+    )
+
+    ax1 = fig.add_subplot(gs[0, :])
+    plot_wide_row(ax1)
+    fig2 = plt.figure()
+    ax = fig2.add_subplot(111)
+    plot_wide_row(ax)
+
+    ax2 = fig.add_subplot(gs[1, 0])
+    plot_small_cell(ax2)
+
+    ax3 = fig.add_subplot(gs[1, 1])
+    plot_narrow_column(ax3)
+    ax3_1 = fig_1.add_subplot(gs_1[0, 0])
+    plot_narrow_column(ax3_1)
+
+    ax4 = fig.add_subplot(gs[1, 2])
+    plot_narrow_column(ax4)
+    ax4_1 = fig_1.add_subplot(gs_1[0, 1])
+    plot_narrow_column(ax4_1)
+
+    ax5 = fig.add_subplot(gs[2, :])
+    plot_large_cell(ax5)
+
+    fig.tight_layout()
+    fig_1.tight_layout()
+
+    x_col1 = -0.015
+    y_row2 = 0.87
+    add_panel_label(fig, "a", x_col1, 1.00)
+    add_panel_label(fig, "b", x_col1, y_row2)
+    add_panel_label(fig, "c", 0.45, y_row2)
+    add_panel_label(fig, "d", 0.72, y_row2)
+    add_panel_label(fig, "e", x_col1, 0.57)
+
+    fig.savefig("example_plot_layout.pdf", format="pdf")
+    fig_1.savefig("example_plot_layout_1.pdf", format="pdf")
+
+
+def plot_wide_row(
+    ax: Axes,
+    title: str = "Wide Row",
+):
+    ax.set_title(title)
+
+
+def plot_small_cell(
+    ax: Axes,
+    title: str = "Small Cell",
+):
+    ax.set_title(title)
+    ax.set_aspect("equal", adjustable="box")
+
+
+def plot_narrow_column(
+    ax: Axes,
+    title: str = "Narrow Column",
+):
+    ax.set_title(title)
+
+
+def plot_large_cell(
+    ax: Axes,
+    title: str = "Large Cell",
+):
+    ax.set_title(title)
+
+
+def add_panel_label(
+    fig: FigureBase,
+    label: str,
+    x: float,
+    y: float,
+    fontsize: int = 14,
+    fontweight: str = "bold",
+    va: str = "top",
+    ha: str = "left",
+):
+    """
+    Add a panel label to the figure using global figure coordinates.
+
+    Args:
+        fig: matplotlib figure object
+        label: string, the label to add (e.g., 'a', 'b', 'c')
+        x: float, x-coordinate in figure coordinates (0-1)
+        y: float, y-coordinate in figure coordinates (0-1)
+        fontsize: int, font size for the label
+        fontweight: str, font weight for the label
+        va: str, vertical alignment for the label
+        ha: str, horizontal alignment for the label
+    """
+    fig.text(
+        x=x,
+        y=y,
+        s=label,
+        fontsize=fontsize,
+        fontweight=fontweight,
+        va=va,
+        ha=ha,
+    )
+
+
+def add_axis_label(
+    ax: Axes,
+    label: str,
+    x: float = -0.1,
+    y: float = 1.1,
+    fontsize: int = 14,
+    fontweight: str = "bold",
+    va: str = "top",
+    ha: str = "right",
+):
+    """
+    Add a label to the given axes.
+    """
+    ax.text(
+        x=x,
+        y=y,
+        s=label,
+        transform=ax.transAxes,
+        fontsize=fontsize,
+        fontweight=fontweight,
+        va=va,
+        ha=ha,
+    )
diff --git a/src/pyrovelocity/plots/models.py b/src/pyrovelocity/plots/models.py
new file mode 100644
index 000000000..8c07bb22a
--- /dev/null
+++ b/src/pyrovelocity/plots/models.py
@@ -0,0 +1,275 @@
+from pathlib import Path
+
+import daft
+import matplotlib.pyplot as plt
+
+__all__ = [
+    "variable_initial_condition_model_plate_diagram",
+    "variable_initial_condition_multiple_timepoints_model_plate_diagram",
+]
+
+
+def variable_initial_condition_model_plate_diagram(
+    output_path: str
+    | Path = "variable_initial_condition_model_plate_diagram.pdf",
+):
+    plt.rcParams["font.family"] = "serif"
+    plt.rcParams["font.size"] = 16
+    plt.rcParams["text.usetex"] = True
+
+    pgm = daft.PGM(line_width=1.2)
+
+    optional_color = "#ff6a14"
+    optional_color_params = {"ec": optional_color}
+
+    # hyperparameters
+    pgm.add_node(
+        "mu_init",
+        r"$\mu_{0}$",
+        0.5,
+        6,
+        fixed=True,
+        plot_params=optional_color_params | {"fc": optional_color},
+    )
+    pgm.add_node(
+        "sigma_init",
+        r"$\sigma_{0}^2$",
+        1.5,
+        6,
+        fixed=True,
+        plot_params=optional_color_params | {"fc": optional_color},
+    )
+    pgm.add_node("mu_theta", r"$\mu_{\theta}$", 2.5, 6, fixed=True)
+    pgm.add_node("sigma_theta", r"$\sigma_{\theta}^2$", 3.5, 6, fixed=True)
+    pgm.add_node("mu_sigma", r"$\mu_{\sigma}$", 4.5, 6, fixed=True)
+    pgm.add_node("sigma_sigma", r"$\sigma_{\sigma}^2$", 5.5, 6, fixed=True)
+
+    # latent variables for gene-specific parameters
+    pgm.add_node(
+        "us_0i",
+        r"$(u,s)_{0i}$",
+        1,
+        5,
+        fontsize=7,
+        scale=1.0,
+        plot_params=optional_color_params,
+    )
+    pgm.add_node("t_0i", r"$t_{0i}$", 2, 5, plot_params=optional_color_params)
+    pgm.add_node("theta_i", r"$\theta_i$", 3, 5)
+    pgm.add_node("sigma_ui", r"$\sigma_{ui}$", 4, 5)
+    pgm.add_node("sigma_si", r"$\sigma_{si}$", 5, 5)
+
+    # latent variables for cell-specific outcomes
+    pgm.add_node(
+        "u_ij",
+        r"$u_{ij}$",
+        2,
+        4,
+        scale=1.0,
+        shape="rectangle",
+    )
+    pgm.add_node(
+        "s_ij",
+        r"$s_{ij}$",
+        4,
+        4,
+        scale=1.0,
+        shape="rectangle",
+    )
+
+    # observed data
+    pgm.add_node("t_j", r"$t_j$", 6.0, 3.25)
+    pgm.add_node(
+        "u_obs_ij",
+        r"$\hat{u}_{ij}$",
+        2,
+        2.5,
+        scale=1.0,
+        observed=True,
+    )
+    pgm.add_node(
+        "s_obs_ij",
+        r"$\hat{s}_{ij}$",
+        4,
+        2.5,
+        scale=1.0,
+        observed=True,
+    )
+
+    # edges
+    edge_params = {"head_length": 0.3, "head_width": 0.25, "lw": 0.7}
+    optional_color_params.update({"fc": optional_color})
+    optional_color_params.update(edge_params)
+    pgm.add_edge("mu_init", "us_0i", plot_params=optional_color_params)
+    pgm.add_edge("sigma_init", "us_0i", plot_params=optional_color_params)
+    pgm.add_edge("mu_init", "t_0i", plot_params=optional_color_params)
+    pgm.add_edge("sigma_init", "t_0i", plot_params=optional_color_params)
+    pgm.add_edge("mu_theta", "theta_i", plot_params=edge_params)
+    pgm.add_edge("sigma_theta", "theta_i", plot_params=edge_params)
+    pgm.add_edge("mu_sigma", "sigma_ui", plot_params=edge_params)
+    pgm.add_edge("sigma_sigma", "sigma_ui", plot_params=edge_params)
+    pgm.add_edge("mu_sigma", "sigma_si", plot_params=edge_params)
+    pgm.add_edge("sigma_sigma", "sigma_si", plot_params=edge_params)
+
+    pgm.add_edge("us_0i", "u_ij", plot_params=optional_color_params)
+    pgm.add_edge("t_0i", "u_ij", plot_params=optional_color_params)
+    pgm.add_edge("us_0i", "s_ij", plot_params=optional_color_params)
+    pgm.add_edge("t_0i", "s_ij", plot_params=optional_color_params)
+    pgm.add_edge("theta_i", "s_ij", plot_params=edge_params)
+    pgm.add_edge("theta_i", "u_ij", plot_params=edge_params)
+
+    pgm.add_edge("u_ij", "u_obs_ij", plot_params=edge_params)
+    pgm.add_edge("s_ij", "s_obs_ij", plot_params=edge_params)
+    pgm.add_edge("sigma_ui", "u_obs_ij", plot_params=edge_params)
+    pgm.add_edge("sigma_si", "s_obs_ij", plot_params=edge_params)
+
+    pgm.add_edge("t_j", "u_ij", plot_params=edge_params)
+    pgm.add_edge("t_j", "s_ij", plot_params=edge_params)
+
+    # plates
+    pgm.add_plate(
+        [0.5, 1.2, 5, 4.4],
+        label=r"Genes $i \in \{1, \ldots, G\}$",
+        shift=-0.1,
+        fontsize=12,
+    )
+    pgm.add_plate(
+        [1.0, 1.8, 5.5, 2.75],
+        label=r"Cells $j \in \{1, \ldots, N\}$",
+        shift=-0.1,
+        fontsize=12,
+    )
+
+    pgm.render()
+
+    for ext in ["", ".png"]:
+        pgm.savefig(
+            f"{output_path}{ext}",
+            bbox_inches="tight",
+            edgecolor="none",
+            dpi=300,
+            transparent=False,
+        )
+
+
+def variable_initial_condition_multiple_timepoints_model_plate_diagram(
+    output_path: str
+    | Path = "variable_initial_condition_multiple_timepoints_model_plate_diagram.pdf",
+):
+    plt.rcParams["font.family"] = "serif"
+    plt.rcParams["font.size"] = 16
+    plt.rcParams["text.usetex"] = True
+
+    pgm = daft.PGM(line_width=1.2)
+
+    # hyperparameters
+    pgm.add_node("mu_init", r"$\mu_{0}$", 0.5, 6, fixed=True)
+    pgm.add_node("sigma_init", r"$\sigma_{0}^2$", 1.5, 6, fixed=True)
+    pgm.add_node("mu_theta", r"$\mu_{\theta}$", 2.5, 6, fixed=True)
+    pgm.add_node("sigma_theta", r"$\sigma_{\theta}^2$", 3.5, 6, fixed=True)
+    pgm.add_node("mu_sigma", r"$\mu_{\sigma}$", 4.5, 6, fixed=True)
+    pgm.add_node("sigma_sigma", r"$\sigma_{\sigma}^2$", 5.5, 6, fixed=True)
+
+    # latent variables for gene-specific parameters
+    # pgm.add_node("us_0i", r"$\mathbf{x}_{0i}$", 1, 5, scale=1.0)
+    pgm.add_node("us_0i", r"$(u,s)_{0i}$", 1, 5, fontsize=7, scale=1.0)
+    pgm.add_node("t_0i", r"$t_{0i}$", 2, 5)
+    pgm.add_node("theta_i", r"$\theta_i$", 3, 5)
+    pgm.add_node("sigma_ui", r"$\sigma_{ui}$", 4, 5)
+    pgm.add_node("sigma_si", r"$\sigma_{si}$", 5, 5)
+
+    # latent variables for cell-specific outcomes
+    pgm.add_node(
+        "u_ij",
+        r"${u}^k_{ij}$",
+        2,
+        3.8,
+        scale=1.0,
+        shape="rectangle",
+    )
+    pgm.add_node(
+        "s_ij",
+        r"${s}^k_{ij}$",
+        4,
+        3.8,
+        scale=1.0,
+        shape="rectangle",
+    )
+
+    # observed data
+    pgm.add_node("t_j", r"${t}^k_j$", 5.9, 3.1)
+    pgm.add_node(
+        "u_obs_ij",
+        r"$\hat{u}{}^{k}_{ij}$",
+        2,
+        2.4,
+        scale=1.0,
+        observed=True,
+    )
+    pgm.add_node(
+        "s_obs_ij",
+        r"$\hat{s}{}^{k}_{ij}$",
+        4,
+        2.4,
+        scale=1.0,
+        observed=True,
+    )
+
+    # edges
+    edge_params = {"head_length": 0.3, "head_width": 0.25, "lw": 0.7}
+    pgm.add_edge("mu_init", "us_0i", plot_params=edge_params)
+    pgm.add_edge("sigma_init", "us_0i", plot_params=edge_params)
+    pgm.add_edge("mu_init", "t_0i", plot_params=edge_params)
+    pgm.add_edge("sigma_init", "t_0i", plot_params=edge_params)
+    pgm.add_edge("mu_theta", "theta_i", plot_params=edge_params)
+    pgm.add_edge("sigma_theta", "theta_i", plot_params=edge_params)
+    pgm.add_edge("mu_sigma", "sigma_ui", plot_params=edge_params)
+    pgm.add_edge("sigma_sigma", "sigma_ui", plot_params=edge_params)
+    pgm.add_edge("mu_sigma", "sigma_si", plot_params=edge_params)
+    pgm.add_edge("sigma_sigma", "sigma_si", plot_params=edge_params)
+
+    pgm.add_edge("us_0i", "u_ij", plot_params=edge_params)
+    pgm.add_edge("t_0i", "u_ij", plot_params=edge_params)
+    pgm.add_edge("us_0i", "s_ij", plot_params=edge_params)
+    pgm.add_edge("t_0i", "s_ij", plot_params=edge_params)
+    pgm.add_edge("theta_i", "s_ij", plot_params=edge_params)
+    pgm.add_edge("theta_i", "u_ij", plot_params=edge_params)
+
+    pgm.add_edge("u_ij", "u_obs_ij", plot_params=edge_params)
+    pgm.add_edge("s_ij", "s_obs_ij", plot_params=edge_params)
+    pgm.add_edge("sigma_ui", "u_obs_ij", plot_params=edge_params)
+    pgm.add_edge("sigma_si", "s_obs_ij", plot_params=edge_params)
+
+    pgm.add_edge("t_j", "u_ij", plot_params=edge_params)
+    pgm.add_edge("t_j", "s_ij", plot_params=edge_params)
+
+    # plates
+    pgm.add_plate(
+        [0.4, 1.0, 5, 4.5],
+        label=r"$i \in \{1, \ldots, G\}$",
+        shift=-0.1,
+        fontsize=12,
+    )
+    pgm.add_plate(
+        [0.8, 1.4, 5.9, 3.2],
+        label=r"$j \in \{1, \ldots, N\}$",
+        shift=-0.1,
+        fontsize=12,
+    )
+    pgm.add_plate(
+        [1.2, 1.8, 5.2, 2.5],
+        label=r"$k \in \{1, \ldots, K_j\}$",
+        shift=-0.1,
+        fontsize=12,
+    )
+
+    pgm.render()
+
+    for ext in ["", ".png"]:
+        pgm.savefig(
+            f"{output_path}{ext}",
+            bbox_inches="tight",
+            edgecolor="none",
+            dpi=300,
+            transparent=False,
+        )
diff --git a/src/pyrovelocity/styles/__init__.py b/src/pyrovelocity/styles/__init__.py
index e69de29bb..564314c38 100644
--- a/src/pyrovelocity/styles/__init__.py
+++ b/src/pyrovelocity/styles/__init__.py
@@ -0,0 +1,5 @@
+from pyrovelocity.styles.configure import configure_matplotlib_style
+
+__all__ = [
+    "configure_matplotlib_style",
+]
diff --git a/src/pyrovelocity/styles/colors.py b/src/pyrovelocity/styles/colors.py
new file mode 100644
index 000000000..ad269f17c
--- /dev/null
+++ b/src/pyrovelocity/styles/colors.py
@@ -0,0 +1,24 @@
+from dataclasses import asdict, dataclass
+
+from beartype import beartype
+
+__all__ = ["LARRY_CELL_TYPE_COLORS"]
+
+
+@beartype
+@dataclass(frozen=True)
+class LarryCellTypeColors:
+    Baso: str = "#1f77b4"  # Dark blue
+    Ccr7_DC: str = "#ff7f0e"  # Orange
+    Eos: str = "#2ca02c"  # Green
+    Erythroid: str = "#d62728"  # Red
+    Lymphoid: str = "#9467bd"  # Purple
+    Mast: str = "#8c564b"  # Brown
+    Meg: str = "#e377c2"  # Pink
+    Monocyte: str = "#bcbd22"  # Olive
+    Neutrophil: str = "#17becf"  # Teal
+    Undifferentiated: str = "#aec7e8"  # Light blue
+    pDC: str = "#ffbb78"  # Light orange
+
+
+LARRY_CELL_TYPE_COLORS = asdict(LarryCellTypeColors())
diff --git a/src/pyrovelocity/styles/common.mplstyle b/src/pyrovelocity/styles/common.mplstyle
index 09fefec64..823cb0721 100644
--- a/src/pyrovelocity/styles/common.mplstyle
+++ b/src/pyrovelocity/styles/common.mplstyle
@@ -59,4 +59,4 @@ mathtext.fontset : cm
 
 # Use LaTeX for math formatting
 text.usetex : True
-text.latex.preamble : \usepackage{amsmath} \usepackage{amssymb}
+text.latex.preamble : \usepackage{amsmath} \usepackage{amssymb} \usepackage[utf8]{inputenc} \usepackage{textcomp}
diff --git a/src/pyrovelocity/styles/configure.py b/src/pyrovelocity/styles/configure.py
new file mode 100644
index 000000000..d12027062
--- /dev/null
+++ b/src/pyrovelocity/styles/configure.py
@@ -0,0 +1,48 @@
+import shutil
+
+import matplotlib as mpl
+import matplotlib.pyplot as plt
+from beartype import beartype
+from beartype.typing import Any, Dict
+
+__all__ = [
+    "configure_matplotlib_style",
+]
+
+
+@beartype
+def configure_matplotlib_style(
+    override_defaults: Dict[str, Any] = {},
+):
+    """
+    Configure the global matplotlib style.
+
+    Accepts a dictionary to override default rcParams set in the
+    `common.mplstyle` file.
+
+    For example, to override the default figure size, use:
+
+    ```
+        {
+            # Example: Set a global figure size
+            "figure.figsize": (8, 6),
+            # Add more global settings as needed
+        },
+    ```
+
+    Args:
+        override_defaults (Dict[str, Any], optional):
+            Dictionary to update default rcParams. Defaults to {}.
+    """
+    plt.style.use("pyrovelocity.styles.common")
+
+    if not shutil.which("latex"):
+        mpl.rcParams.update(
+            {
+                "text.usetex": False,
+            }
+        )
+
+    mpl.rcParams.update(
+        override_defaults,
+    )
diff --git a/src/pyrovelocity/tasks/data.py b/src/pyrovelocity/tasks/data.py
index 20a598508..b547493bd 100644
--- a/src/pyrovelocity/tasks/data.py
+++ b/src/pyrovelocity/tasks/data.py
@@ -4,19 +4,19 @@
 from typing import Optional, Tuple
 from urllib.parse import unquote
 
-import anndata
-import numpy as np
 import requests
 import scanpy as sc
 import validators
-from anndata._core.anndata import AnnData
 from beartype import beartype
 
 import pyrovelocity.io.datasets
+from pyrovelocity.io.subset_data import subset_anndata
 from pyrovelocity.logging import configure_logging
 from pyrovelocity.utils import generate_sample_data, print_anndata
 
-__all__ = ["download_dataset", "load_anndata_from_path", "subset_anndata"]
+__all__ = [
+    "download_dataset",
+]
 
 logger = configure_logging(__name__)
 
@@ -247,96 +247,3 @@ def _validate_url_and_file(url: str) -> Tuple[bool, str]:
         )
     except requests.RequestException as e:
         return False, f"Error occurred: {e}"
-
-
-@beartype
-def subset_anndata(
-    file_path: Optional[str | Path] = None,
-    adata: Optional[anndata._core.anndata.AnnData] = None,
-    n_obs: int = 100,
-    n_vars: Optional[int] = None,
-    save_subset: bool = False,
-    output_path: Optional[str | Path] = None,
-) -> Tuple[anndata._core.anndata.AnnData, str | Path | None]:
-    """
-    Randomly sample observations from a dataset given by file path or AnnData object.
-
-    Args:
-        file_path (str): Path to a .h5ad file containing a dataset. Takes precedence over adata.
-        adata (AnnData): AnnData object. If None, file_path must be provided.
-        n_obs (int): Number of observations to sample. Defaults to 100.
-        save_subset (bool): If True, save the subset to a file. Defaults to False.
-        output_path (str): Path to save the subset. Defaults to None.
-
-    Raises:
-        ValueError: If neither file_path nor adata is provided.
-
-    Returns:
-        AnnData: Subset of the dataset.
-
-    Examples:
-        >>> data_path = download_dataset(data_set_name="pancreas") # xdoctest: +SKIP
-        >>> adata = subset(file_path=data_path, n_obs=100, save_subset=True) # xdoctest: +SKIP
-        >>> print_anndata(adata) # xdoctest: +SKIP
-        >>> print_attributes(adata) # xdoctest: +SKIP
-    """
-    if file_path is not None:
-        file_path = Path(file_path)
-        adata = sc.read(file_path, cache=True)
-    if adata is None:
-        raise ValueError("Either file_path or adata must be provided")
-
-    if n_obs > adata.n_obs:
-        logger.warning(
-            f"n_obs ({n_obs}) is greater than the number of observations in the dataset ({adata.n_obs})"
-        )
-        n_obs = adata.n_obs
-    logger.info("constructing data subset")
-    print_anndata(adata)
-
-    if n_vars is not None:
-        if n_vars > adata.n_vars:
-            logger.warning(
-                f"n_vars ({n_vars}) is greater than the number of variables in the dataset ({adata.n_vars})"
-            )
-            n_vars = adata.n_vars
-        selected_vars_indices = np.random.choice(adata.n_vars, n_vars)
-        logger.info(f"selected {n_vars} vars from {adata.n_vars}")
-        adata = adata[:, selected_vars_indices]
-
-    selected_obs_indices = np.random.choice(adata.n_obs, n_obs)
-    logger.info(f"selected {n_obs} obs from {adata.n_obs}")
-    adata = adata[selected_obs_indices]
-    adata.obs_names_make_unique()
-    adata.var_names_make_unique()
-
-    if save_subset:
-        if output_path is None and file_path is not None:
-            output_path = file_path.parent / Path(
-                file_path.stem + f"_{n_obs}obs" + file_path.suffix
-            )
-        if output_path is None:
-            raise ValueError(
-                "output_path must be provided if save_subset is True and file_path is None"
-            )
-        adata.write(output_path)
-        logger.info(f"saved {n_obs} obs subset: {output_path}")
-
-    print_anndata(adata)
-    return adata.copy(), output_path
-
-
-@beartype
-def load_anndata_from_path(adata_path: str | Path) -> AnnData:
-    adata_path = Path(adata_path)
-    if adata_path.suffix not in {".h5ad", ".loom"}:
-        raise ValueError(
-            f"The input file {adata_path}\n"
-            "must be either a .h5ad or .loom file."
-        )
-    if os.path.isfile(adata_path) and os.access(adata_path, os.R_OK):
-        logger.info(f"Reading input file: {adata_path}")
-        adata = sc.read(filename=adata_path, cache=True)
-        return adata
-    else:
-        raise ValueError(f"Cannot read input file: {adata_path}")
diff --git a/src/pyrovelocity/tasks/preprocess.py b/src/pyrovelocity/tasks/preprocess.py
index 45a11762b..ea9f60342 100644
--- a/src/pyrovelocity/tasks/preprocess.py
+++ b/src/pyrovelocity/tasks/preprocess.py
@@ -19,8 +19,11 @@
 from pyrovelocity.plots._count_histograms import (
     plot_spliced_unspliced_histogram,
 )
-from pyrovelocity.tasks.data import load_anndata_from_path
-from pyrovelocity.utils import ensure_numpy_array, print_anndata
+from pyrovelocity.utils import (
+    ensure_numpy_array,
+    load_anndata_from_path,
+    print_anndata,
+)
 
 __all__ = [
     "assign_colors",
@@ -55,6 +58,7 @@ def preprocess_dataset(
     default_velocity_mode: str = "dynamical",
     vector_field_basis: str = "umap",
     cell_state: str = "clusters",
+    selected_genes: List[str] = [""],
 ) -> Tuple[AnnData, Path, Path]:
     """
     Preprocess data.
@@ -77,6 +81,7 @@ def preprocess_dataset(
         default_velocity_mode (str, optional): default velocity mode. Defaults to "dynamical".
         vector_field_basis (str, optional): vector field basis. Defaults to "umap".
         cell_state (str, optional): Name of the cell state/cluster variable. Defaults to "clusters".
+        selected_genes (Optional[List[str]], optional): List of genes to preserve during preprocessing. Defaults to None.
 
     Returns:
         AnnData: processed AnnData object
@@ -103,6 +108,14 @@ def preprocess_dataset(
     else:
         data_path = "AnnData object"
 
+    if selected_genes and selected_genes != [""]:
+        missing_genes = set(selected_genes) - set(adata.var_names)
+        if missing_genes:
+            logger.warning(
+                f"The following selected genes are missing from the AnnData object: "
+                f"{missing_genes}"
+            )
+
     if use_obs_subset:
         if n_obs_subset > adata.n_obs:
             logger.warning(
@@ -166,6 +179,11 @@ def preprocess_dataset(
         and not overwrite
     ):
         logger.info(f"{processed_path} exists")
+        adata = load_anndata_from_path(processed_path)
+        logger.info(
+            f"loaded precomputed preprocessed data from {processed_path}"
+        )
+        print_anndata(adata)
         return adata, Path(processed_path), reports_processed_path
     else:
         logger.info(f"generating {processed_path} ...")
@@ -195,6 +213,15 @@ def preprocess_dataset(
             logger.info("processing data with cytotrace ...")
             cytotrace_sparse(adata, layer="spliced")
 
+        # TODO: clarify usage of selected_genes
+        # It is possible to pass selected_genes to filter_and_normalize by
+        # including the parameter
+        #
+        # retain_genes=selected_genes
+        #
+        # This is not done here because then the resulting genes would not
+        # necessarily pass the standard filters. However, it is confusing to
+        # have the selected_genes parameter of this function ignored here.
         adata_tmp = scv.pp.filter_and_normalize(
             adata,
             min_shared_counts=min_shared_counts,
@@ -308,7 +335,31 @@ def preprocess_dataset(
                     f"setting n_vars_subset to len(likelihood_sorted_genes): {len(likelihood_sorted_genes)}"
                 )
                 n_vars_subset = len(likelihood_sorted_genes)
-            adata = adata[:, likelihood_sorted_genes[:n_vars_subset]].copy()
+
+            # TODO: clarify usage of selected_genes
+            if selected_genes and selected_genes != [""]:
+                selected_genes_set = set(selected_genes)
+                likelihood_sorted_genes_set = set(
+                    likelihood_sorted_genes[:n_vars_subset]
+                )
+                additional_genes = (
+                    selected_genes_set - likelihood_sorted_genes_set
+                )
+
+                if additional_genes:
+                    n_vars_subset = min(
+                        n_vars_subset + len(additional_genes), adata.n_vars
+                    )
+                    logger.info(
+                        f"Including {len(additional_genes)} additional selected genes. New n_vars_subset: {n_vars_subset}"
+                    )
+
+                final_gene_list = list(
+                    likelihood_sorted_genes[:n_vars_subset]
+                ) + list(additional_genes)
+                adata = adata[:, final_gene_list].copy()
+            else:
+                adata = adata[:, likelihood_sorted_genes[:n_vars_subset]].copy()
         scv.tl.velocity_graph(
             data=adata,
             n_jobs=-1,
@@ -317,7 +368,8 @@ def preprocess_dataset(
 
         scv.tl.velocity_embedding(adata, basis=vector_field_basis)
 
-        scv.tl.latent_time(adata)
+        if n_obs_subset is None or n_obs_subset > 100:
+            scv.tl.latent_time(adata)
 
         print_anndata(adata)
         adata.write(processed_path)
diff --git a/src/pyrovelocity/tasks/summarize.py b/src/pyrovelocity/tasks/summarize.py
index af9d0750e..b2164879b 100644
--- a/src/pyrovelocity/tasks/summarize.py
+++ b/src/pyrovelocity/tasks/summarize.py
@@ -4,7 +4,9 @@
 
 import matplotlib.pyplot as plt
 import scvelo as scv
+from anndata import AnnData
 from beartype import beartype
+from beartype.typing import List
 from pandas import DataFrame
 
 from pyrovelocity.analysis.analyze import top_mae_genes
@@ -15,11 +17,14 @@
     plot_gene_ranking,
     plot_gene_selection_summary,
     plot_parameter_posterior_distributions,
+    plot_report,
     plot_shared_time_uncertainty,
     plot_vector_field_summary,
     posterior_curve,
-    rainbowplot,
+    save_subfigures,
 )
+from pyrovelocity.plots._rainbow import rainbowplot_module as rainbowplot
+from pyrovelocity.styles.colors import LARRY_CELL_TYPE_COLORS
 from pyrovelocity.utils import (
     save_anndata_counts_to_dataframe,
     save_parameter_posterior_mean_dataframe,
@@ -41,6 +46,7 @@ def summarize_dataset(
     vector_field_basis: str,
     reports_path: str | Path = "reports",
     enable_experimental_plots: bool = False,
+    selected_genes: list[str] = [""],
 ) -> Tuple[Path, Path]:
     """
     Construct summary plots for each data set and model.
@@ -143,10 +149,7 @@ def summarize_dataset(
         phase_portraits_exist = (
             len(os.listdir(posterior_phase_portraits_path)) > 0
         )
-    if (
-        all(os.path.isfile(f) for f in output_filenames)
-        and phase_portraits_exist
-    ):
+    if all(os.path.isfile(f) for f in output_filenames):
         logger.info(
             "\n\t"
             + "\n\t".join(str(f) for f in output_filenames)
@@ -186,7 +189,7 @@ def summarize_dataset(
             logger.info(f"Generating figure: {violin_clusters_log}")
             for fig_name in [violin_clusters_lin, violin_clusters_log]:
                 cluster_violin_plots(
-                    data_model,
+                    data_model=data_model,
                     adata=adata,
                     posterior_samples=posterior_samples,
                     cluster_key=cell_state,
@@ -196,6 +199,24 @@ def summarize_dataset(
                     fig_name=fig_name,
                 )
 
+        # phase portraint predictive plots
+        if phase_portraits_exist:
+            logger.info(
+                f"\nFiles exist in posterior phase portraits path:\n"
+                f"{posterior_phase_portraits_path}\n"
+                f"Remove this directory or all its files if you want to regenerate them.\n\n"
+            )
+        else:
+            logger.info("Generating posterior predictive phase portrait plots")
+            posterior_curve(
+                adata=adata,
+                posterior_samples=posterior_samples,
+                gene_set=putative_marker_genes,
+                data_model=data_model,
+                model_path=model_path,
+                output_directory=posterior_phase_portraits_path,
+            )
+
     # ##################
     # save dataframes
     # ##################
@@ -229,6 +250,9 @@ def summarize_dataset(
             vector_field_basis=vector_field_basis,
             plot_name=vector_field_summary_plot,
             cell_state=cell_state,
+            state_color_dict=LARRY_CELL_TYPE_COLORS
+            if "larry" in data_model
+            else None,
         )
 
     # shared time plot
@@ -238,66 +262,37 @@ def summarize_dataset(
         logger.info(f"Generating figure: {shared_time_plot}")
 
         plot_shared_time_uncertainty(
-            posterior_samples=posterior_samples,
             adata=adata,
+            posterior_samples=posterior_samples,
             vector_field_basis=vector_field_basis,
             shared_time_plot=shared_time_plot,
         )
 
+    # extract putative marker genes
     logger.info(f"Searching for marker genes")
     putative_marker_genes = top_mae_genes(
         volcano_data=volcano_data,
-        mae_top_percentile=3,
+        mae_top_percentile=100 * 24 / len(volcano_data),
         min_genes_per_bin=3,
+        max_genes_per_bin=4,
     )
 
-    # phase portraint predictive plots
-    if phase_portraits_exist:
-        logger.info(
-            f"\nFiles exist in posterior phase portraits path:\n"
-            f"{posterior_phase_portraits_path}\n"
-            f"Remove this directory or all its files if you want to regenerate them.\n\n"
-        )
-    else:
-        logger.info("Generating posterior predictive phase portrait plots")
-        posterior_curve(
-            adata=adata,
-            posterior_samples=posterior_samples,
-            gene_set=putative_marker_genes,
-            data_model=data_model,
-            model_path=model_path,
-            output_directory=posterior_phase_portraits_path,
-        )
-
     # volcano plot
     if os.path.isfile(volcano_plot):
         logger.info(f"{volcano_plot} exists")
     else:
         logger.info(f"Generating figure: {volcano_plot}")
 
-        volcano_data, fig = plot_gene_ranking(
-            posterior_samples=[posterior_samples],
-            adata=[adata],
-            selected_genes=putative_marker_genes,
+        plot_gene_ranking(
+            posterior_samples=posterior_samples,
+            adata=adata,
+            putative_marker_genes=putative_marker_genes,
+            selected_genes=selected_genes,
             time_correlation_with="st",
             show_marginal_histograms=True,
             save_volcano_plot=True,
             volcano_plot_path=volcano_plot,
-        )
-
-    # gene selection summary plot
-    if os.path.isfile(gene_selection_summary_plot):
-        logger.info(f"{gene_selection_summary_plot} exists")
-    else:
-        logger.info(f"Generating figure: {gene_selection_summary_plot}")
-        plot_gene_selection_summary(
-            adata=adata,
-            posterior_samples=posterior_samples,
-            basis=vector_field_basis,
-            cell_state=cell_state,
-            plot_name=gene_selection_summary_plot,
-            selected_genes=putative_marker_genes,
-            show_marginal_histograms=False,
+            show_xy_labels=True,
         )
 
     # parameter uncertainty plot
@@ -309,6 +304,7 @@ def summarize_dataset(
             posterior_samples=posterior_samples,
             adata=adata,
             geneset=putative_marker_genes,
+            save_plot=True,
             parameter_uncertainty_plot=parameter_uncertainty_plot,
         )
 
@@ -329,12 +325,34 @@ def summarize_dataset(
             rainbow_plot_path=gene_selection_rainbow_plot,
         )
 
+    # gene selection summary plot
+    if os.path.isfile(gene_selection_summary_plot):
+        logger.info(f"{gene_selection_summary_plot} exists")
+    else:
+        logger.info(f"Generating figure: {gene_selection_summary_plot}")
+
+        plot_report(
+            adata=adata,
+            posterior_samples=posterior_samples,
+            volcano_data=volcano_data,
+            putative_marker_genes=putative_marker_genes,
+            selected_genes=selected_genes,
+            vector_field_basis=vector_field_basis,
+            cell_state=cell_state,
+            state_color_dict=LARRY_CELL_TYPE_COLORS
+            if "larry" in data_model
+            else None,
+            report_file_path=gene_selection_summary_plot,
+            figure_file_path=f"{gene_selection_summary_plot}.dill.zst",
+        )
+
     # mean vector field plot
     if os.path.isfile(vector_field_plot):
         logger.info(f"{vector_field_plot} exists")
     else:
         logger.info(f"Generating figure: {vector_field_plot}")
         fig, ax = plt.subplots()
+        ax.axis("off")
 
         scv.pl.velocity_embedding_grid(
             adata,
@@ -342,7 +360,8 @@ def summarize_dataset(
             color=cell_state,
             title="",
             vkey="velocity_pyro",
-            linewidth=1,
+            s=1,
+            linewidth=0.5,
             ax=ax,
             show=False,
             legend_loc="right margin",
diff --git a/src/pyrovelocity/tasks/time_fate_correlation.py b/src/pyrovelocity/tasks/time_fate_correlation.py
new file mode 100644
index 000000000..20b4cd2cd
--- /dev/null
+++ b/src/pyrovelocity/tasks/time_fate_correlation.py
@@ -0,0 +1,245 @@
+from pathlib import Path
+
+import matplotlib
+import matplotlib.pyplot as plt
+from beartype import beartype
+from beartype.typing import List, Tuple
+from matplotlib.axes import Axes
+from matplotlib.figure import Figure
+from matplotlib.gridspec import GridSpec
+from matplotlib.ticker import MaxNLocator
+
+from pyrovelocity.io.datasets import larry_cospar
+from pyrovelocity.logging import configure_logging
+from pyrovelocity.plots import plot_lineage_fate_correlation
+from pyrovelocity.styles import configure_matplotlib_style
+from pyrovelocity.styles.colors import LARRY_CELL_TYPE_COLORS
+from pyrovelocity.utils import load_anndata_from_path
+from pyrovelocity.workflows.main_configuration import (
+    WorkflowConfiguration,
+    larry_configuration,
+    larry_mono_configuration,
+    larry_multilineage_configuration,
+    larry_neu_configuration,
+)
+
+__all__ = [
+    "configure_time_lineage_fate_plot",
+]
+
+logger = configure_logging(__name__)
+
+configure_matplotlib_style()
+
+
+@beartype
+def estimate_time_lineage_fate_correlation(
+    reports_path: str | Path = "reports",
+    model_identifier: str = "model2",
+    configurations: List[WorkflowConfiguration] = [
+        larry_mono_configuration,
+        larry_neu_configuration,
+        larry_multilineage_configuration,
+        larry_configuration,
+    ],
+) -> Path:
+    """
+    This function is a duplicate of the `combine_time_lineage_fate_correlation`
+    task function and will be removed in a future release.
+    """
+    n_rows = len(configurations)
+    n_cols = 7
+    width = 14
+    height = width * (n_rows / n_cols) + 1
+
+    fig = plt.figure(figsize=(width, height))
+
+    gs = fig.add_gridspec(
+        n_rows + 1,
+        n_cols + 1,
+        width_ratios=[0.02] + [1] * n_cols,
+        height_ratios=[1] * n_rows + [0.2],
+    )
+
+    adata_cospar = larry_cospar()
+
+    all_axes = []
+    for i, config in enumerate(configurations):
+        data_set_name = config.download_dataset.data_set_name
+        data_set_model_pairing = f"{data_set_name}_{model_identifier}"
+        model_path = f"models/{data_set_model_pairing}"
+
+        adata_pyrovelocity = load_anndata_from_path(
+            f"{model_path}/postprocessed.h5ad"
+        )
+        posterior_samples_path = f"{model_path}/pyrovelocity.pkl.zst"
+        plot_path = (
+            Path(reports_path)
+            / f"time_fate_correlation_{data_set_model_pairing}.pdf"
+        )
+
+        axes = [fig.add_subplot(gs[i, j + 1]) for j in range(n_cols)]
+        all_axes.append(axes)
+
+        plot_lineage_fate_correlation(
+            posterior_samples_path=posterior_samples_path,
+            adata_pyrovelocity=adata_pyrovelocity,
+            adata_cospar=adata_cospar,
+            all_axes=axes,
+            fig=fig,
+            state_color_dict=LARRY_CELL_TYPE_COLORS,
+            lineage_fate_correlation_path=plot_path,
+            save_plot=False,
+            ylabel="",
+            show_titles=True if i == 0 else False,
+            show_colorbars=False,
+            default_fontsize=12 if matplotlib.rcParams["text.usetex"] else 9,
+        )
+
+    return configure_time_lineage_fate_plot(
+        fig=fig,
+        gs=gs,
+        all_axes=all_axes,
+        row_labels=["a", "b", "c", "d"],
+        vertical_texts=[
+            "Monocytes",
+            "Neutrophils",
+            "Multilineage",
+            "All lineages",
+        ],
+        reports_path=Path(reports_path),
+        model_identifier=model_identifier,
+    )
+
+
+@beartype
+def configure_time_lineage_fate_plot(
+    fig: Figure,
+    gs: GridSpec,
+    all_axes: List[List[Axes]],
+    row_labels: List[str],
+    vertical_texts: List[str],
+    reports_path: Path,
+    model_identifier: str,
+) -> Path:
+    """
+    Finalize the time lineage fate correlation plot by adding labels, legends, and colorbars.
+
+    Args:
+        fig: The main figure object.
+        gs: The GridSpec object used for the subplot layout.
+        all_axes: A list of lists containing all subplot Axes objects.
+        row_labels: Labels for each row (e.g., "a", "b", "c", "d").
+        vertical_texts: Vertical text labels for each row.
+        reports_path: Path to save the final plot.
+        model_identifier: Identifier for the model used.
+
+    Returns:
+        Path: The path where the final plot is saved.
+    """
+    _set_axes_aspect(all_axes)
+    _add_row_labels(fig, gs, row_labels, vertical_texts)
+    _add_legend(fig, gs, all_axes)
+    _adjust_layout(fig)
+    _add_colorbars(fig, gs, all_axes)
+    return _save_plot(fig, reports_path, model_identifier)
+
+
+def _set_axes_aspect(all_axes: List[List[Axes]]) -> None:
+    for row_axes in all_axes:
+        for ax in row_axes:
+            ax.set_aspect("equal", adjustable="box")
+
+
+def _add_row_labels(
+    fig: Figure, gs: GridSpec, row_labels: List[str], vertical_texts: List[str]
+) -> None:
+    for i, (label, vtext) in enumerate(zip(row_labels, vertical_texts)):
+        label_ax = fig.add_subplot(gs[i, 0])
+        label_ax.axis("off")
+        label_ax.text(
+            0.5,
+            1,
+            rf"\textbf{{{label}}}"
+            if matplotlib.rcParams["text.usetex"]
+            else f"{label}",
+            fontweight="bold",
+            fontsize=12,
+            ha="center",
+            va="top",
+        )
+        label_ax.text(
+            0.5, 0.5, vtext, rotation=90, fontsize=12, ha="center", va="center"
+        )
+
+
+def _add_legend(fig: Figure, gs: GridSpec, all_axes: List[List[Axes]]) -> None:
+    legend_ax = fig.add_subplot(gs[-1, 1:3])
+    legend_ax.axis("off")
+    handles, labels = all_axes[-1][0].get_legend_handles_labels()
+    legend_ax.legend(
+        handles=handles,
+        labels=labels,
+        loc="lower left",
+        bbox_to_anchor=(-0.1, -0.2),
+        ncol=4,
+        fancybox=True,
+        prop={"size": 12},
+        fontsize=12,
+        frameon=False,
+        markerscale=4,
+        columnspacing=0.7,
+        handletextpad=0.1,
+    )
+
+
+def _adjust_layout(fig: Figure) -> None:
+    fig.tight_layout()
+    fig.subplots_adjust(
+        left=0.05, right=0.98, top=0.98, bottom=0.08, wspace=0.1, hspace=0.2
+    )
+
+
+def _add_colorbars(
+    fig: Figure, gs: GridSpec, all_axes: List[List[Axes]]
+) -> None:
+    add_colorbar_axes = all_axes[-1][-3:]
+    add_colorbar_artists = [ax.collections[0] for ax in add_colorbar_axes]
+    for i, im in enumerate(add_colorbar_artists):
+        cbar_ax = fig.add_subplot(gs[-1, -3 + i])
+        cbar = fig.colorbar(im, cax=cbar_ax, orientation="horizontal")
+        cbar.locator = MaxNLocator(nbins=2)
+        cbar.update_ticks()
+        cbar_ax.xaxis.set_ticks_position("bottom")
+        cbar_ax.xaxis.set_label_position("bottom")
+        _adjust_colorbar_position(cbar_ax, add_colorbar_axes[i])
+
+
+def _adjust_colorbar_position(cbar_ax: Axes, ref_ax: Axes) -> None:
+    ax_pos = ref_ax.get_position()
+    cbar_width = ax_pos.width * 0.6
+    cbar_height = 0.01
+    cbar_ax.set_position(
+        [ax_pos.x0 + (ax_pos.width - cbar_width), 0.12, cbar_width, cbar_height]
+    )
+
+
+def _save_plot(
+    fig: Figure,
+    reports_path: Path,
+    model_identifier: str,
+) -> Path:
+    combined_plot_path = (
+        reports_path / f"combined_time_fate_correlation_{model_identifier}.pdf"
+    )
+    for ext in ["", ".png"]:
+        fig.savefig(
+            fname=f"{combined_plot_path}{ext}",
+            facecolor=fig.get_facecolor(),
+            bbox_inches="tight",
+            edgecolor="none",
+            dpi=300,
+            transparent=False,
+        )
+    plt.close(fig)
+    return combined_plot_path
diff --git a/src/pyrovelocity/tasks/train.py b/src/pyrovelocity/tasks/train.py
index 0e69e694e..19f465e7c 100644
--- a/src/pyrovelocity/tasks/train.py
+++ b/src/pyrovelocity/tasks/train.py
@@ -18,8 +18,7 @@
 from pyrovelocity.io.compressedpickle import CompressedPickle
 from pyrovelocity.logging import configure_logging
 from pyrovelocity.models import PyroVelocity
-from pyrovelocity.tasks.data import load_anndata_from_path
-from pyrovelocity.utils import print_anndata
+from pyrovelocity.utils import load_anndata_from_path, print_anndata
 
 logger = configure_logging(__name__)
 
diff --git a/src/pyrovelocity/tests/analysis/test_analyze.py b/src/pyrovelocity/tests/analysis/test_analyze.py
index ef6238b56..a1e2446f8 100644
--- a/src/pyrovelocity/tests/analysis/test_analyze.py
+++ b/src/pyrovelocity/tests/analysis/test_analyze.py
@@ -1,9 +1,11 @@
 from typing import List
 
+import numpy as np
 import pandas as pd
 import pytest
 
 from pyrovelocity.analysis.analyze import (
+    mae_per_gene,
     pareto_frontier_genes,
     top_mae_genes,
 )
@@ -53,6 +55,48 @@ def sample_volcano_data_with_ribosomal() -> pd.DataFrame:
     )
 
 
+@pytest.mark.parametrize("xp", [np])
+def test_mae_per_gene_basic(xp):
+    true_counts = xp.array(
+        [
+            [1, 2, 3],
+            [1, 2, 3],
+            [1, 2, 3],
+            [1, 2, 3],
+        ]
+    )
+    pred_counts = xp.array(
+        [
+            [1.1, 2.2, 3.3],
+            [1.1, 2.2, 3.3],
+            [1.1, 2.2, 3.3],
+            [1.1, 2.2, 3.3],
+        ]
+    )
+    mae = mae_per_gene(pred_counts, true_counts)
+    assert xp.allclose(mae, xp.array([-0.1, -0.1, -0.1]), rtol=1e-2, atol=1e-2)
+
+
+@pytest.mark.parametrize("xp", [np])
+def test_mae_per_gene_zero_counts(xp):
+    true_counts = xp.array(
+        [
+            [10, 15, 0],
+            [20, 25, 0],
+        ]
+    )
+    pred_counts = xp.array(
+        [
+            [12, 14, 0],
+            [18, 26, 0],
+        ]
+    )
+    mae = mae_per_gene(pred_counts, true_counts)
+    assert xp.allclose(
+        mae, xp.array([-0.133, -0.05, -0.0]), rtol=1e-2, atol=1e-2
+    )
+
+
 def test_pareto_frontier_genes_basic(sample_volcano_data):
     result = pareto_frontier_genes(
         sample_volcano_data, num_genes=3, max_iters=1000
diff --git a/src/pyrovelocity/tests/conftest.py b/src/pyrovelocity/tests/conftest.py
index dfe1d3158..b656db385 100644
--- a/src/pyrovelocity/tests/conftest.py
+++ b/src/pyrovelocity/tests/conftest.py
@@ -1,8 +1,11 @@
+from importlib.resources import files
+
 import pytest
 import scanpy as sc
 
 from pyrovelocity.analysis.analyze import top_mae_genes
 from pyrovelocity.io.compressedpickle import CompressedPickle
+from pyrovelocity.io.serialization import load_anndata_from_json
 from pyrovelocity.tasks.data import download_dataset
 from pyrovelocity.tasks.postprocess import postprocess_dataset
 from pyrovelocity.tasks.preprocess import preprocess_dataset
@@ -11,6 +14,14 @@
 from pyrovelocity.utils import generate_sample_data
 
 
+@pytest.fixture
+def adata_preprocessed_pancreas_50_7():
+    fixture_file_path = (
+        files("pyrovelocity.tests.data") / "preprocessed_pancreas_50_7.json"
+    )
+    return load_anndata_from_json(fixture_file_path)
+
+
 @pytest.fixture
 def default_sample_data():
     """Default sample data from `pyrovelocity.utils.generate_sample_data`.
diff --git a/src/pyrovelocity/tests/data/pancreas_50_13.json b/src/pyrovelocity/tests/data/pancreas_50_13.json
new file mode 100644
index 000000000..6c30b6f0c
--- /dev/null
+++ b/src/pyrovelocity/tests/data/pancreas_50_13.json
@@ -0,0 +1,2178 @@
+{
+  "shape": [50, 13],
+  "obs": {
+    "index": [
+      "CAGCATAGTTGCGCAC",
+      "ACACCCTAGGACAGCT",
+      "CTAGTGAGTTTGGCGC",
+      "TGTGGTACAGTCGATT",
+      "CAAGGCCTCTCAACTT",
+      "CGGGTCAGTCACTGGC",
+      "AACTGGTTCTTACCGC",
+      "AAACGGGTCAGCTCTC",
+      "AGATTGCGTCTGGAGA",
+      "AACCATGGTTGAACTC",
+      "GTTTCTAGTGCTTCTC",
+      "TTTGGTTAGACAGGCT",
+      "TCCCGATCAGTCAGCC",
+      "ATTCTACGTTTAGGAA",
+      "GATTCAGGTCCGACGT",
+      "ACTGATGCATCAGTAC",
+      "TAGCCGGTCAATCACG",
+      "CTACACCGTCGCCATG",
+      "CCTACACCAGCGAACA",
+      "GCTGCTTTCTGGTGTA",
+      "TGCTACCAGTTTCCTT",
+      "ACGAGCCCACCTTGTC",
+      "GCATGATAGGATGGTC",
+      "AAGACCTTCTGACCTC",
+      "AGCAGCCGTCGCATCG",
+      "GCAATCAGTGTTAAGA",
+      "CTGGTCTAGGTGATTA",
+      "AGGGTGAGTGCGAAAC",
+      "CGGTTAATCTCGTTTA",
+      "GAGTCCGCATAAGACA",
+      "CAAGTTGTCTAACTGG",
+      "AAGACCTAGTAAGTAC",
+      "GTAACGTGTGCCTGCA",
+      "AGGCCGTGTGAAATCA",
+      "GGCGTGTTCAGCTTAG",
+      "ACGATGTAGAGTGAGA",
+      "GACGTTATCGGAGCAA",
+      "CGCGGTACAAACGCGA",
+      "ATCCGAACACGGTAGA",
+      "AGAGTGGCACCTCGGA",
+      "CGACTTCGTGTGGTTT",
+      "CAGCCGAAGTAGGTGC",
+      "GACCAATGTAAGAGAG",
+      "CTAGCCTGTGCGATAG",
+      "CACCACTGTCGGCTCA",
+      "GCACATAAGTATCTCG",
+      "CCGGGATGTGTCAATC",
+      "AGCTCTCTCCTGCTTG",
+      "GCGGGTTCAAGTAATG",
+      "TGTGTTTTCATTCACT"
+    ],
+    "clusters_coarse": [
+      "Endocrine",
+      "Ngn3 high EP",
+      "Pre-endocrine",
+      "Ngn3 low EP",
+      "Pre-endocrine",
+      "Ductal",
+      "Ductal",
+      "Ngn3 low EP",
+      "Ductal",
+      "Pre-endocrine",
+      "Endocrine",
+      "Endocrine",
+      "Ngn3 low EP",
+      "Endocrine",
+      "Pre-endocrine",
+      "Ngn3 high EP",
+      "Endocrine",
+      "Ductal",
+      "Ductal",
+      "Ngn3 low EP",
+      "Pre-endocrine",
+      "Ngn3 high EP",
+      "Ngn3 high EP",
+      "Ductal",
+      "Ngn3 high EP",
+      "Pre-endocrine",
+      "Endocrine",
+      "Endocrine",
+      "Ductal",
+      "Pre-endocrine",
+      "Pre-endocrine",
+      "Endocrine",
+      "Ngn3 high EP",
+      "Ductal",
+      "Endocrine",
+      "Ngn3 high EP",
+      "Pre-endocrine",
+      "Ductal",
+      "Ductal",
+      "Pre-endocrine",
+      "Endocrine",
+      "Ductal",
+      "Endocrine",
+      "Ductal",
+      "Ductal",
+      "Ductal",
+      "Ngn3 high EP",
+      "Ngn3 high EP",
+      "Endocrine",
+      "Endocrine"
+    ],
+    "clusters": [
+      "Beta",
+      "Ngn3 high EP",
+      "Pre-endocrine",
+      "Ngn3 low EP",
+      "Pre-endocrine",
+      "Ductal",
+      "Ductal",
+      "Ngn3 low EP",
+      "Ductal",
+      "Pre-endocrine",
+      "Beta",
+      "Alpha",
+      "Ngn3 low EP",
+      "Beta",
+      "Pre-endocrine",
+      "Ngn3 high EP",
+      "Beta",
+      "Ductal",
+      "Ductal",
+      "Ngn3 low EP",
+      "Pre-endocrine",
+      "Ngn3 high EP",
+      "Ngn3 high EP",
+      "Ductal",
+      "Ngn3 high EP",
+      "Pre-endocrine",
+      "Epsilon",
+      "Epsilon",
+      "Ductal",
+      "Pre-endocrine",
+      "Pre-endocrine",
+      "Alpha",
+      "Ngn3 high EP",
+      "Ductal",
+      "Alpha",
+      "Ngn3 high EP",
+      "Pre-endocrine",
+      "Ductal",
+      "Ductal",
+      "Pre-endocrine",
+      "Alpha",
+      "Ductal",
+      "Alpha",
+      "Ductal",
+      "Ductal",
+      "Ductal",
+      "Ngn3 high EP",
+      "Ngn3 high EP",
+      "Alpha",
+      "Alpha"
+    ],
+    "S_score": [
+      -0.22630095481872559, -0.20675146579742432, -0.3353894054889679,
+      -0.09509168565273285, -0.10973085463047028, 0.3364957869052887,
+      -0.03788253664970398, -0.12347911298274994, 0.5614822506904602,
+      -0.24560028314590454, -0.16220076382160187, -0.1486142873764038,
+      0.03927233815193176, -0.24019774794578552, -0.1068091094493866,
+      -0.03278049826622009, -0.2822374701499939, 0.07667481899261475,
+      0.004554927349090576, 0.090004563331604, -0.27468299865722656,
+      -0.0788775384426117, -0.16026689112186432, -0.0910664051771164,
+      -0.11147382855415344, -0.17557771503925323, -0.03733953833580017,
+      -0.26399070024490356, -0.2112804651260376, -0.215547576546669,
+      -0.10604114830493927, -0.15694434940814972, -0.1162041425704956,
+      -0.11791162192821503, -0.18583017587661743, 0.03647613525390625,
+      -0.24028140306472778, -0.0828862339258194, 0.1513555645942688,
+      -0.14206331968307495, -0.2458702027797699, 0.01110944151878357,
+      -0.1184825748205185, -0.1375492513179779, -0.02013486623764038,
+      -0.13548269867897034, -0.1461016684770584, -0.09621983766555786,
+      -0.16584078967571259, -0.14329449832439423
+    ],
+    "G2M_score": [
+      -0.19608090817928314, -0.174663245677948, -0.16875243186950684,
+      -0.19221362471580505, -0.2849780023097992, 0.5493474006652832,
+      -0.22771470248699188, -0.2298336774110794, 0.36178526282310486,
+      -0.2080020010471344, -0.23812207579612732, -0.2842731475830078,
+      0.940440833568573, -0.19113689661026, -0.18721112608909607,
+      -0.14182895421981812, -0.2715652585029602, 0.629919171333313,
+      -0.1587229073047638, -0.24558581411838531, -0.23010151088237762,
+      -0.23789678514003754, 0.3399551808834076, -0.27084171772003174,
+      -0.2641700804233551, -0.23901616036891937, -0.23635409772396088,
+      -0.20217551290988922, -0.21176986396312714, -0.13667170703411102,
+      -0.19004984200000763, -0.2838878035545349, -0.24859781563282013,
+      1.0052828788757324, -0.19458886981010437, -0.18634459376335144,
+      -0.24196723103523254, -0.1780945062637329, -0.16956689953804016,
+      -0.3152511715888977, -0.3334091305732727, 0.8408318758010864,
+      -0.27892571687698364, -0.22221140563488007, 0.6608805656433105,
+      -0.2942494750022888, -0.1348460614681244, -0.29876279830932617,
+      -0.20470497012138367, -0.1721334308385849
+    ],
+    "n_genes_by_counts": [
+      2458, 2485, 1870, 1855, 2147, 3473, 2993, 2282, 2720, 1938, 2427, 1923,
+      3601, 2620, 2076, 2049, 2798, 4168, 2580, 2836, 2203, 2238, 2048, 2471,
+      2075, 2211, 2135, 2645, 2558, 2282, 2139, 1870, 2056, 3031, 2245, 2638,
+      2269, 2147, 2856, 1949, 2111, 3886, 2502, 2417, 3944, 2519, 2507, 2105,
+      2550, 2195
+    ],
+    "total_counts": [
+      5939.0, 6336.0, 4165.0, 3900.0, 4991.0, 11642.0, 8449.0, 5892.0, 8451.0,
+      3884.0, 6482.0, 4398.0, 11730.0, 6881.0, 4338.0, 4871.0, 8990.0, 16732.0,
+      7142.0, 7944.0, 4854.0, 5523.0, 5101.0, 6633.0, 4858.0, 5158.0, 4592.0,
+      7024.0, 7300.0, 5331.0, 5258.0, 3643.0, 5013.0, 9120.0, 5026.0, 7383.0,
+      5457.0, 4937.0, 8494.0, 4509.0, 4661.0, 14375.0, 6186.0, 5885.0, 17153.0,
+      6682.0, 6771.0, 5628.0, 5790.0, 4749.0
+    ],
+    "total_counts_mt": [
+      35.0, 44.0, 31.0, 28.0, 17.0, 90.0, 56.0, 57.0, 59.0, 30.0, 58.0, 63.0,
+      94.0, 41.0, 42.0, 35.0, 46.0, 120.0, 35.0, 51.0, 32.0, 40.0, 32.0, 59.0,
+      40.0, 44.0, 20.0, 45.0, 38.0, 22.0, 35.0, 16.0, 37.0, 104.0, 42.0, 41.0,
+      47.0, 41.0, 42.0, 30.0, 48.0, 67.0, 93.0, 46.0, 87.0, 35.0, 59.0, 30.0,
+      29.0, 22.0
+    ],
+    "pct_counts_mt": [
+      0.5893248319625854, 0.694444477558136, 0.7442977428436279,
+      0.7179486751556396, 0.34061309695243835, 0.773063063621521,
+      0.6628003716468811, 0.9674134850502014, 0.6981422305107117,
+      0.7723996043205261, 0.8947855830192566, 1.4324692487716675,
+      0.8013640642166138, 0.5958436131477356, 0.9681881666183472,
+      0.7185382843017578, 0.5116796493530273, 0.7171885967254639,
+      0.4900588095188141, 0.6419939398765564, 0.6592500805854797,
+      0.7242440581321716, 0.6273279786109924, 0.8894919157028198,
+      0.823384165763855, 0.853043794631958, 0.4355400800704956,
+      0.6406605839729309, 0.5205479264259338, 0.4126805365085602,
+      0.665652334690094, 0.43919846415519714, 0.7380810379981995,
+      1.1403508186340332, 0.835654616355896, 0.5553297996520996,
+      0.8612791299819946, 0.8304638862609863, 0.4944666624069214,
+      0.665336012840271, 1.0298219919204712, 0.4660869538784027,
+      1.5033947229385376, 0.7816482186317444, 0.5071998834609985,
+      0.5237953066825867, 0.8713631629943848, 0.5330490469932556,
+      0.5008635520935059, 0.4632554054260254
+    ],
+    "total_counts_ribo": [
+      858.0, 1371.0, 869.0, 1073.0, 899.0, 3104.0, 2079.0, 1578.0, 2760.0,
+      645.0, 914.0, 766.0, 2753.0, 1034.0, 791.0, 1124.0, 1407.0, 4296.0,
+      2070.0, 2172.0, 882.0, 1281.0, 1081.0, 1884.0, 1069.0, 1108.0, 597.0,
+      1067.0, 1940.0, 1027.0, 1245.0, 639.0, 1387.0, 2220.0, 814.0, 2103.0,
+      1015.0, 1230.0, 2445.0, 894.0, 671.0, 3549.0, 931.0, 1247.0, 4693.0,
+      1729.0, 1577.0, 1129.0, 607.0, 797.0
+    ],
+    "pct_counts_ribo": [
+      14.446876525878906, 21.63825798034668, 20.86434555053711,
+      27.512821197509766, 18.012422561645508, 26.662084579467773,
+      24.606462478637695, 26.78207778930664, 32.65885543823242,
+      16.606592178344727, 14.1005859375, 17.417007446289062, 23.469736099243164,
+      15.026885032653809, 18.234209060668945, 23.07534408569336,
+      15.65072250366211, 25.67535400390625, 28.983476638793945,
+      27.341388702392578, 18.17057991027832, 23.19391632080078,
+      21.191923141479492, 28.4034366607666, 22.004940032958984,
+      21.48119354248047, 13.000871658325195, 15.190773963928223,
+      26.575342178344727, 19.264678955078125, 23.678203582763672,
+      17.540489196777344, 27.668062210083008, 24.342103958129883,
+      16.195781707763672, 28.484355926513672, 18.599964141845703,
+      24.913915634155273, 28.78502655029297, 19.82701301574707,
+      14.396052360534668, 24.688695907592773, 15.050113677978516,
+      21.189464569091797, 27.35964584350586, 25.875484466552734,
+      23.290502548217773, 20.060413360595703, 10.483592987060547,
+      16.782480239868164
+    ]
+  },
+  "var": {
+    "index": [
+      "Mrpl15",
+      "Gm37323",
+      "Sox17",
+      "Ttr",
+      "Gm37381",
+      "Spp1",
+      "Cpe",
+      "Rp1-1",
+      "Ins2",
+      "Cck",
+      "Xkr4",
+      "Krt7",
+      "Rp1"
+    ],
+    "highly_variable_genes": [
+      "False",
+      NaN,
+      NaN,
+      "True",
+      NaN,
+      "True",
+      "True",
+      NaN,
+      "True",
+      "True",
+      "False",
+      "True",
+      NaN
+    ],
+    "mt": [
+      false,
+      false,
+      false,
+      false,
+      false,
+      false,
+      false,
+      false,
+      false,
+      false,
+      false,
+      false,
+      false
+    ],
+    "ribo": [
+      false,
+      false,
+      false,
+      false,
+      false,
+      false,
+      false,
+      false,
+      false,
+      false,
+      false,
+      false,
+      false
+    ],
+    "n_cells_by_counts": [4, 0, 0, 47, 0, 26, 29, 0, 4, 25, 0, 47, 0],
+    "mean_counts": [
+      0.07999999821186066, 0.0, 0.0, 14.960000038146973, 0.0,
+      27.420000076293945, 3.700000047683716, 0.0, 4.599999904632568,
+      7.179999828338623, 0.0, 5.380000114440918, 0.0
+    ],
+    "pct_dropout_by_counts": [
+      92.0, 100.0, 100.0, 6.000000000000005, 100.0, 48.0, 42.00000000000001,
+      100.0, 92.0, 50.0, 100.0, 6.000000000000005, 100.0
+    ],
+    "total_counts": [
+      4.0, 0.0, 0.0, 748.0, 0.0, 1371.0, 185.0, 0.0, 230.0, 359.0, 0.0, 269.0,
+      0.0
+    ]
+  },
+  "X": [
+    [1.0, 0.0, 0.0, 51.0, 0.0, 0.0, 9.0, 0.0, 10.0, 2.0, 0.0, 3.0, 0.0],
+    [0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 15.0, 0.0],
+    [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 3.0, 0.0, 0.0, 29.0, 0.0, 8.0, 0.0],
+    [0.0, 0.0, 0.0, 3.0, 0.0, 18.0, 2.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0],
+    [0.0, 0.0, 0.0, 28.0, 0.0, 0.0, 0.0, 0.0, 0.0, 15.0, 0.0, 22.0, 0.0],
+    [0.0, 0.0, 0.0, 5.0, 0.0, 38.0, 0.0, 0.0, 0.0, 0.0, 0.0, 3.0, 0.0],
+    [1.0, 0.0, 0.0, 16.0, 0.0, 85.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+    [0.0, 0.0, 0.0, 19.0, 0.0, 105.0, 0.0, 0.0, 0.0, 1.0, 0.0, 2.0, 0.0],
+    [0.0, 0.0, 0.0, 27.0, 0.0, 15.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0],
+    [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 9.0, 0.0, 0.0, 0.0, 0.0, 6.0, 0.0],
+    [0.0, 0.0, 0.0, 9.0, 0.0, 0.0, 10.0, 0.0, 114.0, 0.0, 0.0, 4.0, 0.0],
+    [0.0, 0.0, 0.0, 38.0, 0.0, 0.0, 8.0, 0.0, 0.0, 1.0, 0.0, 3.0, 0.0],
+    [0.0, 0.0, 0.0, 12.0, 0.0, 58.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.0, 0.0],
+    [0.0, 0.0, 0.0, 28.0, 0.0, 0.0, 13.0, 0.0, 40.0, 0.0, 0.0, 2.0, 0.0],
+    [0.0, 0.0, 0.0, 14.0, 0.0, 1.0, 2.0, 0.0, 0.0, 2.0, 0.0, 6.0, 0.0],
+    [0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 62.0, 0.0, 8.0, 0.0],
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diff --git a/src/pyrovelocity/tests/data/postprocessed_pancreas_50_7.json b/src/pyrovelocity/tests/data/postprocessed_pancreas_50_7.json
new file mode 100644
index 000000000..01f593f8c
--- /dev/null
+++ b/src/pyrovelocity/tests/data/postprocessed_pancreas_50_7.json
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+      "Endocrine",
+      "Ngn3 low EP",
+      "Endocrine",
+      "Ngn3 low EP",
+      "Pre-endocrine",
+      "Ductal",
+      "Endocrine",
+      "Ngn3 high EP",
+      "Ngn3 high EP",
+      "Ngn3 high EP",
+      "Ductal",
+      "Endocrine",
+      "Endocrine",
+      "Ductal",
+      "Ductal",
+      "Endocrine",
+      "Endocrine",
+      "Endocrine",
+      "Pre-endocrine",
+      "Ngn3 low EP",
+      "Pre-endocrine",
+      "Ductal",
+      "Pre-endocrine",
+      "Endocrine",
+      "Endocrine",
+      "Ductal",
+      "Ngn3 high EP",
+      "Endocrine",
+      "Ngn3 low EP",
+      "Ngn3 high EP",
+      "Endocrine",
+      "Ngn3 high EP",
+      "Ngn3 high EP",
+      "Pre-endocrine"
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+      21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38,
+      39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49
+    ],
+    "index": [
+      "ACGAGGATCATCACCC",
+      "ATCATGGCAGCTGTGC",
+      "TGGACGCAGTGTTGAA",
+      "CAAGGCCAGACGACGT",
+      "AGCCTAAGTTCCATGA",
+      "TCCCGATCAGCATGAG",
+      "GTCATTTTCATAGCAC",
+      "ACTGTCCCACGTAAGG",
+      "CGCTGGAGTCCATCCT",
+      "TGTGTTTGTATATCCG",
+      "ACTGAGTGTCTTCAAG",
+      "AGGTCCGTCCTGCAGG",
+      "TCACAAGTCTGGTTCC",
+      "CAGCATAGTAATAGCA",
+      "TTGACTTTCACCGGGT",
+      "TCGCGTTAGATCTGAA",
+      "TTCTACATCTACTCAT",
+      "AGGCCGTGTACCAGTT",
+      "CCTACCACACGTAAGG",
+      "GTTCGGGAGTCACGCC",
+      "CGTTGGGGTTTAAGCC",
+      "CAAGGCCAGTTGCAGG",
+      "AAACGGGCAAAGAATC",
+      "TTTACTGCAGCATACT",
+      "GGATTACAGACATAAC",
+      "GATGCTAAGCGATAGC",
+      "CCCAGTTTCGTATCAG",
+      "CGAACATTCGGAAACG",
+      "GGACGTCGTCGAGATG",
+      "GAACCTAAGAATCTCC",
+      "TTTGCGCAGACAGACC",
+      "ATTATCCAGGACATTA",
+      "AAGGAGCCAAAGGTGC",
+      "TGGACGCAGCGTCTAT",
+      "CAGCTAACAGCTTAAC",
+      "TACTCATAGGCCATAG",
+      "CAAGTTGGTGTGCCTG",
+      "CGAACATTCTAACGGT",
+      "GGATTACGTATCTGCA",
+      "TTGCGTCCATACCATG",
+      "ATGAGGGCAAGGTTTC",
+      "GAAGCAGGTCCGAAGA",
+      "GACTGCGAGAATCTCC",
+      "TTGAACGTCTTTAGTC",
+      "TCAATCTGTCAAAGAT",
+      "GGGACCTAGCTGCCCA",
+      "CGGAGTCCATAAAGGT",
+      "ACTGATGTCTGCGACG",
+      "AACTCCCAGAACTCGG",
+      "CTGAAGTCAGCTGGCT"
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+      "0",
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diff --git a/src/pyrovelocity/tests/data/preprocessed_pancreas_50_7.json b/src/pyrovelocity/tests/data/preprocessed_pancreas_50_7.json
new file mode 100644
index 000000000..dd8cb19a6
--- /dev/null
+++ b/src/pyrovelocity/tests/data/preprocessed_pancreas_50_7.json
@@ -0,0 +1,6583 @@
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+}
diff --git a/src/pyrovelocity/tests/data/trained_pancreas_50_7.json b/src/pyrovelocity/tests/data/trained_pancreas_50_7.json
new file mode 100644
index 000000000..d655c852f
--- /dev/null
+++ b/src/pyrovelocity/tests/data/trained_pancreas_50_7.json
@@ -0,0 +1,6731 @@
+{
+  "shape": [50, 7],
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+        0.0, 0.0, 0.0, 0.0
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+      ],
+      [
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+        -0.909518301486969, 0.0, -0.8076501488685608, 0.0, 0.0,
+        -0.7757360935211182, 0.0, -0.2061820924282074, 0.0, 0.0,
+        -0.5298843383789062, -0.7831470966339111, -0.4682753086090088,
+        -0.7832844853401184, -0.42705103754997253, -0.7877506017684937,
+        -0.5298843383789062, -0.762237012386322, -0.9466448426246643,
+        -0.8186197876930237, -0.7877506017684937, 0.0, 0.0, 0.0,
+        -0.8391203880310059, 0.0, 0.0, 0.0, -0.5514553189277649,
+        -0.7905962467193604, -0.32642799615859985, -0.7859317064285278, 0.0,
+        0.0, 0.0, 0.0, -0.5298843383789062, 0.0, -0.7917589545249939,
+        -0.7748526334762573, -0.18125492334365845, -0.8075706958770752,
+        -0.8187859654426575, 0.0
+      ]
+    ],
+    "velocity_params": {
+      "embeddings": ["umap"],
+      "fit_offset": false,
+      "mode": "dynamical",
+      "mode_neighbors": "distances",
+      "n_recurse_neighbors": 2,
+      "perc": [5, 95]
+    }
+  }
+}
diff --git a/src/pyrovelocity/tests/fixtures/generate_anndata_fixtures.py b/src/pyrovelocity/tests/fixtures/generate_anndata_fixtures.py
new file mode 100644
index 000000000..b6870c6e4
--- /dev/null
+++ b/src/pyrovelocity/tests/fixtures/generate_anndata_fixtures.py
@@ -0,0 +1,224 @@
+"""
+Execute this script to generate test fixture data:
+
+python src/pyrovelocity/tests/fixtures/generate_anndata_fixtures.py
+
+This script should not be executed by pytest.
+It is used to generate test fixture data.
+"""
+
+import shutil
+import tempfile
+from pathlib import Path
+
+from anndata import AnnData
+from beartype import beartype
+from beartype.typing import List, Union
+
+from pyrovelocity.io.datasets import pancreas
+from pyrovelocity.io.serialization import (
+    load_anndata_from_json,
+    save_anndata_to_json,
+)
+from pyrovelocity.tasks.postprocess import postprocess_dataset
+from pyrovelocity.tasks.preprocess import preprocess_dataset
+from pyrovelocity.tasks.train import train_dataset
+from pyrovelocity.utils import (
+    anndata_string,
+    configure_logging,
+    load_anndata_from_path,
+    print_anndata,
+    print_string_diff,
+)
+from pyrovelocity.workflows.main_configuration import (
+    pancreas_summary_configuration,
+)
+
+__all__ = ["generate_pancreas_fixture_data"]
+
+logger = configure_logging(__name__)
+
+
+@beartype
+def generate_pancreas_fixture_data(
+    output_path: str
+    | Path = "src/pyrovelocity/tests/data/preprocessed_pancreas.json",
+    n_obs: int = 50,
+    n_vars: int = 7,
+) -> Path:
+    """
+    Generate a test fixture for the pancreas dataset.
+
+    Note that the selected_genes are not enforced in the preprocess_dataset
+    function. This is to prefer higher quality genes over the selected_genes
+    in generating the test fixture data.
+
+    Args:
+        output_path: Path to save the JSON fixture.
+        n_obs: Number of observations to keep.
+        n_vars: Number of variables to keep.
+    """
+    output_path = Path(output_path)
+
+    adata: AnnData = pancreas()
+
+    adata, _, _ = preprocess_dataset(
+        data_set_name="pancreas",
+        adata=adata,
+        use_obs_subset=True,
+        n_obs_subset=n_obs,
+        use_vars_subset=True,
+        n_vars_subset=n_vars,
+        process_cytotrace=True,
+    )
+
+    selected_genes: List[str] = pancreas_summary_configuration.selected_genes
+    selected_genes_in_adata: List[str] = [
+        gene for gene in selected_genes if gene in adata.var_names
+    ]
+    if len(selected_genes_in_adata) < len(selected_genes):
+        logger.warning(
+            f"\nSome selected genes are not in the downsampled data:\n"
+            f"selected_genes: {selected_genes}\n"
+            f"selected_genes_in_adata: {selected_genes_in_adata}\n"
+        )
+
+    genes_to_keep: List[str] = list(
+        set(adata.var_names[:n_vars].tolist() + selected_genes_in_adata)
+    )
+    adata = adata[:, genes_to_keep]
+    preprocessed_anndata_string = anndata_string(adata)
+
+    print_anndata(adata)
+    save_anndata_to_json(adata, output_path)
+
+    logger.info(f"Test fixture saved to {output_path}")
+
+    try:
+        logger.info("Attempting to load the serialized AnnData object...")
+        loaded_adata = load_anndata_from_json(output_path)
+        loaded_anndata_string = anndata_string(loaded_adata)
+        logger.info("Successfully loaded the serialized AnnData object.")
+        print_string_diff(
+            text1=preprocessed_anndata_string,
+            text2=loaded_anndata_string,
+            diff_title="Preprocessed vs Loaded AnnData",
+        )
+        print_anndata(loaded_adata)
+    except Exception as e:
+        logger.error(f"Error loading serialized AnnData object: {str(e)}")
+
+    return output_path
+
+
+@beartype
+def generate_postprocessed_pancreas_fixture_data(
+    input_path: str
+    | Path = "src/pyrovelocity/tests/data/preprocessed_pancreas_50_7.json",
+    trained_output_path: str
+    | Path = "src/pyrovelocity/tests/data/trained_pancreas_50_7.json",
+    postprocessed_output_path: str
+    | Path = "src/pyrovelocity/tests/data/postprocessed_pancreas_50_7.json",
+    max_epochs: int = 10,
+    retain_temp_files: bool = False,
+    retain_dir: str
+    | Path = "src/pyrovelocity/tests/data/train_postprocess_artifacts",
+) -> tuple[Path, Path]:
+    """
+    Generate trained and postprocessed test fixtures for the pancreas dataset.
+
+    Args:
+        input_path: Path to load the preprocessed JSON fixture.
+        trained_output_path: Path to save the trained JSON fixture.
+        postprocessed_output_path: Path to save the postprocessed JSON fixture.
+        max_epochs: Number of epochs to train the model.
+        retain_temp_files: If True, copy temporary files to retain_dir.
+        retain_dir: Directory to copy temporary files to if retain_temp_files is True.
+
+    Returns:
+        Tuple of paths to the saved trained and postprocessed JSON fixtures.
+    """
+    input_path = Path(input_path)
+    trained_output_path = Path(trained_output_path)
+    postprocessed_output_path = Path(postprocessed_output_path)
+
+    adata = load_anndata_from_json(input_path)
+
+    with tempfile.TemporaryDirectory() as tmp_dir:
+        models_path = Path(tmp_dir) / "models"
+        logger.info(f"Using temporary directory: {models_path}")
+
+        result = train_dataset(
+            adata=adata,
+            data_set_name="pancreas",
+            model_identifier="model2",
+            models_path=models_path,
+            max_epochs=max_epochs,
+            force=True,
+        )
+
+        (
+            data_model,
+            data_model_path,
+            trained_data_path,
+            model_path,
+            posterior_samples_path,
+            metrics_path,
+            run_info_path,
+            loss_plot_path,
+            loss_csv_path,
+        ) = result
+
+        trained_adata = load_anndata_from_path(trained_data_path)
+
+        (
+            pyrovelocity_data_path,
+            postprocessed_data_path,
+        ) = postprocess_dataset(
+            data_model=data_model,
+            data_model_path=data_model_path,
+            trained_data_path=trained_data_path,
+            model_path=model_path,
+            posterior_samples_path=posterior_samples_path,
+            metrics_path=metrics_path,
+            vector_field_basis="umap",
+            number_posterior_samples=4,
+        )
+
+        postprocessed_adata = load_anndata_from_path(postprocessed_data_path)
+
+        if retain_temp_files:
+            retain_dir = Path(retain_dir)
+            retain_dir.mkdir(parents=True, exist_ok=True)
+            logger.info(f"Copying temporary files to {retain_dir}")
+            shutil.copytree(tmp_dir, retain_dir, dirs_exist_ok=True)
+
+    preprocessed_anndata_string = anndata_string(adata)
+    trained_anndata_string = anndata_string(trained_adata)
+    postprocessed_anndata_string = anndata_string(postprocessed_adata)
+
+    print_string_diff(
+        text1=preprocessed_anndata_string,
+        text2=trained_anndata_string,
+        diff_title="Preprocessed vs Trained AnnData",
+    )
+    print_string_diff(
+        text1=trained_anndata_string,
+        text2=postprocessed_anndata_string,
+        diff_title="Trained vs Postprocessed AnnData",
+    )
+    print_anndata(postprocessed_adata)
+
+    save_anndata_to_json(trained_adata, trained_output_path)
+    logger.info(f"Trained test fixture saved to {trained_output_path}")
+    save_anndata_to_json(postprocessed_adata, postprocessed_output_path)
+    logger.info(
+        f"Postprocessed test fixture saved to {postprocessed_output_path}"
+    )
+
+    return trained_output_path, postprocessed_output_path
+
+
+if __name__ == "__main__":
+    generate_pancreas_fixture_data()
+    generate_postprocessed_pancreas_fixture_data()
diff --git a/src/pyrovelocity/tests/io/test_hash.py b/src/pyrovelocity/tests/io/test_hash.py
new file mode 100644
index 000000000..79fb844db
--- /dev/null
+++ b/src/pyrovelocity/tests/io/test_hash.py
@@ -0,0 +1,50 @@
+import hashlib
+from pathlib import Path
+
+import pytest
+
+from pyrovelocity.io.hash import hash_file
+
+
+def create_file_with_content(path: Path, content: str):
+    path.write_text(content)
+    return path
+
+
+def calculate_sha256(content: str):
+    return hashlib.sha256(content.encode()).hexdigest()
+
+
+@pytest.fixture
+def sample_files(tmp_path):
+    files = {
+        "empty": create_file_with_content(tmp_path / "empty.txt", ""),
+        "small": create_file_with_content(tmp_path / "small.txt", "test file"),
+        "medium": create_file_with_content(
+            tmp_path / "medium.txt", "A" * 10000
+        ),
+        "large": create_file_with_content(
+            tmp_path / "large.txt", "B" * 1000000
+        ),
+    }
+    return files
+
+
+def test_hash_file(sample_files):
+    for file_type, file_path in sample_files.items():
+        content = file_path.read_text()
+        expected_hash = calculate_sha256(content)
+        assert (
+            hash_file(file_path) == expected_hash
+        ), f"Hash mismatch for {file_type} file"
+
+
+def test_hash_file_nonexistent_file(tmp_path):
+    non_existent_file = tmp_path / "nonexistent.txt"
+    with pytest.raises(FileNotFoundError):
+        hash_file(non_existent_file)
+
+
+def test_hash_file_directory(tmp_path):
+    with pytest.raises(IsADirectoryError):
+        hash_file(tmp_path)
diff --git a/src/pyrovelocity/tests/io/test_serialization.py b/src/pyrovelocity/tests/io/test_serialization.py
index 90d6265bc..9e96b4a8b 100644
--- a/src/pyrovelocity/tests/io/test_serialization.py
+++ b/src/pyrovelocity/tests/io/test_serialization.py
@@ -1,4 +1,5 @@
-import tempfile
+import json
+from unittest.mock import patch
 
 import numpy as np
 import pandas as pd
@@ -6,6 +7,7 @@
 from anndata import AnnData
 from scipy import sparse
 
+from pyrovelocity.io.hash import hash_file
 from pyrovelocity.io.serialization import (
     create_sample_anndata,
     deserialize_anndata,
@@ -15,73 +17,174 @@
 )
 
 
-@pytest.fixture
+@pytest.fixture(scope="function")
 def sample_adata():
+    """Create a sample AnnData object for testing."""
     return create_sample_anndata(5, 4)
 
 
+@pytest.fixture(scope="function")
+def mock_hash_file():
+    """Mock the hash_file function."""
+    with patch("pyrovelocity.io.serialization.hash_file") as mock:
+        mock.return_value = "mocked_hash"
+        yield mock
+
+
 def test_create_sample_anndata():
+    """Test the creation of a sample AnnData object."""
     adata = create_sample_anndata(5, 4)
-    assert isinstance(adata, AnnData)
-    assert adata.n_obs == 5
-    assert adata.n_vars == 4
-    assert "batch" in adata.obs.columns
-    assert "a" in adata.obsm
-    assert "a" in adata.layers
+    assert isinstance(adata, AnnData), "Expected an AnnData object"
+    assert adata.n_obs == 5, "Expected 5 observations"
+    assert adata.n_vars == 4, "Expected 4 variables"
+    assert "batch" in adata.obs.columns, "Expected 'batch' column in obs"
+    assert "a" in adata.obsm, "Expected 'a' in obsm"
+    assert "a" in adata.layers, "Expected 'a' in layers"
 
 
 def test_serialize_deserialize_anndata(sample_adata):
+    """Test serialization and deserialization of AnnData object."""
     serialized = serialize_anndata(sample_adata)
     deserialized = deserialize_anndata(serialized)
 
-    assert np.allclose(sample_adata.X, deserialized.X)
-    assert sample_adata.obs.equals(deserialized.obs)
-    assert sample_adata.var.equals(deserialized.var)
-    assert np.allclose(sample_adata.obsm["a"], deserialized.obsm["a"])
-    assert np.allclose(sample_adata.layers["a"], deserialized.layers["a"])
-    assert sample_adata.uns == deserialized.uns
+    assert np.allclose(sample_adata.X, deserialized.X), "Mismatch in X matrix"
+    assert sample_adata.obs.equals(
+        deserialized.obs
+    ), "Mismatch in obs DataFrame"
+    assert sample_adata.var.equals(
+        deserialized.var
+    ), "Mismatch in var DataFrame"
+    assert np.allclose(
+        sample_adata.obsm["a"], deserialized.obsm["a"]
+    ), "Mismatch in obsm['a']"
+    assert np.allclose(
+        sample_adata.layers["a"], deserialized.layers["a"]
+    ), "Mismatch in layers['a']"
+    assert sample_adata.uns == deserialized.uns, "Mismatch in uns dictionary"
 
 
-def test_save_load_anndata_json(sample_adata, tmp_path):
+def test_save_load_anndata_json(sample_adata, tmp_path, mock_hash_file, caplog):
+    """Test saving and loading AnnData object to/from JSON."""
     file_path = tmp_path / "test_adata.json"
+
     save_anndata_to_json(sample_adata, file_path)
-    assert file_path.exists()
+    assert (
+        file_path.exists()
+    ), f"Expected file {file_path} to exist after saving"
+    mock_hash_file.assert_called_once_with(file_path)
+    assert (
+        f"\nSaved file: {file_path}\nSHA-256 hash: mocked_hash\n" in caplog.text
+    )
+
+    caplog.clear()
 
     loaded_adata = load_anndata_from_json(file_path)
+    mock_hash_file.assert_called_with(file_path)
+    assert (
+        f"\nLoading file: {file_path}\nSHA-256 hash: mocked_hash\n"
+        in caplog.text
+    )
+
+    assert np.allclose(
+        sample_adata.X, loaded_adata.X
+    ), "Mismatch in X matrix after loading"
+    assert sample_adata.obs.equals(
+        loaded_adata.obs
+    ), "Mismatch in obs DataFrame after loading"
+    assert sample_adata.var.equals(
+        loaded_adata.var
+    ), "Mismatch in var DataFrame after loading"
+    assert np.allclose(
+        sample_adata.obsm["a"], loaded_adata.obsm["a"]
+    ), "Mismatch in obsm['a'] after loading"
+    assert np.allclose(
+        sample_adata.layers["a"], loaded_adata.layers["a"]
+    ), "Mismatch in layers['a'] after loading"
+    assert (
+        sample_adata.uns == loaded_adata.uns
+    ), "Mismatch in uns dictionary after loading"
+
+
+@pytest.fixture
+def expected_hash(sample_adata, tmp_path):
+    """Generate expected hash for a sample AnnData object."""
+    file_path = tmp_path / "temp_adata.json"
+    save_anndata_to_json(sample_adata, file_path)
+    return hash_file(file_path)
+
+
+def test_save_load_anndata_json_with_hash(
+    sample_adata, tmp_path, expected_hash, caplog
+):
+    """Test saving and loading AnnData object to/from JSON with hash validation."""
+    file_path = tmp_path / "test_adata.json"
+
+    saved_hash = save_anndata_to_json(
+        sample_adata, file_path, expected_hash=expected_hash
+    )
+    assert saved_hash == expected_hash
+    assert "Hash validation succeeded" in caplog.text
+
+    caplog.clear()
+
+    loaded_adata = load_anndata_from_json(
+        file_path, expected_hash=expected_hash
+    )
+    assert "Hash validation succeeded" in caplog.text
+
     assert np.allclose(sample_adata.X, loaded_adata.X)
     assert sample_adata.obs.equals(loaded_adata.obs)
     assert sample_adata.var.equals(loaded_adata.var)
-    assert np.allclose(sample_adata.obsm["a"], loaded_adata.obsm["a"])
-    assert np.allclose(sample_adata.layers["a"], loaded_adata.layers["a"])
-    assert sample_adata.uns == loaded_adata.uns
+
+
+def test_save_load_anndata_json_with_incorrect_hash(
+    sample_adata, tmp_path, expected_hash, caplog
+):
+    """Test saving and loading AnnData object to/from JSON with incorrect hash."""
+    file_path = tmp_path / "test_adata.json"
+    incorrect_hash = "incorrect_hash_value"
+    assert incorrect_hash != expected_hash
+
+    save_anndata_to_json(sample_adata, file_path, expected_hash=incorrect_hash)
+    assert "Hash mismatch" in caplog.text
+
+    caplog.clear()
+
+    load_anndata_from_json(file_path, expected_hash=incorrect_hash)
+    assert "Hash mismatch" in caplog.text
 
 
 def test_serialize_large_anndata():
+    """Test serialization of a large AnnData object."""
     large_adata = create_sample_anndata(1000, 100)
     serialized = serialize_anndata(large_adata)
-    assert isinstance(serialized, dict)
+    assert isinstance(
+        serialized, dict
+    ), "Expected serialized object to be a dictionary"
     assert all(
         key in serialized
         for key in ["X", "obs", "var", "obsm", "layers", "uns"]
-    )
+    ), "Missing expected keys in serialized dictionary"
 
 
 def test_deserialize_invalid_data():
+    """Test deserialization with invalid data."""
     invalid_data = {
         "X": [[1, 2], [3, 4]],
-        # "obs": {},
         "var": {},
         "uns": {},
         "obsm": {},
         "varm": {},
         "layers": {},
-        # "shape": (2, 2),
     }
-    with pytest.raises(ValueError):
+    with pytest.raises(
+        ValueError, match="Invalid data format: missing required keys"
+    ):
         deserialize_anndata(invalid_data)
 
 
-def test_save_load_empty_anndata(tmp_path):
+def test_save_load_empty_anndata(tmp_path, mock_hash_file, caplog):
+    """Test saving and loading an empty AnnData object."""
     empty_adata = AnnData(
         X=np.empty((0, 0)),
         obs=pd.DataFrame(index=[]),
@@ -90,11 +193,19 @@ def test_save_load_empty_anndata(tmp_path):
     file_path = tmp_path / "empty_adata.json"
     save_anndata_to_json(empty_adata, file_path)
     loaded_adata = load_anndata_from_json(file_path)
-    assert loaded_adata.n_obs == 0
-    assert loaded_adata.n_vars == 0
+    assert (
+        loaded_adata.n_obs == 0
+    ), "Expected 0 observations in loaded empty AnnData"
+    assert (
+        loaded_adata.n_vars == 0
+    ), "Expected 0 variables in loaded empty AnnData"
+    assert (
+        mock_hash_file.call_count == 2
+    ), "Expected hash_file to be called twice"
 
 
 def test_serialize_deserialize_sparse_matrix():
+    """Test serialization and deserialization of AnnData with sparse matrix."""
     sparse_adata = AnnData(X=sparse.random(100, 100, density=0.1, format="csr"))
     sparse_adata.X[sparse_adata.X < 0.9] = 0
     sparse_adata.X = sparse_adata.X.tocsr()
@@ -102,10 +213,13 @@ def test_serialize_deserialize_sparse_matrix():
     serialized = serialize_anndata(sparse_adata)
     deserialized = deserialize_anndata(serialized)
 
-    assert np.allclose(sparse_adata.X.toarray(), deserialized.X)
+    assert np.allclose(
+        sparse_adata.X.toarray(), deserialized.X
+    ), "Mismatch in sparse matrix after serialization/deserialization"
 
 
 def test_serialize_deserialize_with_complex_uns():
+    """Test serialization and deserialization of AnnData with complex uns."""
     adata = create_sample_anndata(100, 50)
     adata.uns["complex_item"] = {
         "nested": {"a": 1, "b": [1, 2, 3]},
@@ -118,40 +232,88 @@ def test_serialize_deserialize_with_complex_uns():
     assert (
         deserialized.uns["complex_item"]["nested"]
         == adata.uns["complex_item"]["nested"]
-    )
+    ), "Mismatch in nested dictionary in uns"
     assert np.array_equal(
         deserialized.uns["complex_item"]["array"],
         adata.uns["complex_item"]["array"],
-    )
+    ), "Mismatch in numpy array in uns"
+
+
+def test_serialize_deserialize_with_nested_numpy_array_in_uns(sample_adata):
+    """Test serialization and deserialization of AnnData with nested numpy arrays in uns."""
+    adata = sample_adata.copy()
+    adata.uns["numpy_array"] = np.array([1, 2, 3])
+    adata.uns["nested_dict"] = {"array": np.array([4, 5, 6])}
+
+    serialized = serialize_anndata(adata)
+    deserialized = deserialize_anndata(serialized)
+
+    assert isinstance(
+        deserialized.uns["numpy_array"], np.ndarray
+    ), "Expected numpy array in uns"
+    assert np.array_equal(
+        adata.uns["numpy_array"], deserialized.uns["numpy_array"]
+    ), "Mismatch in numpy array in uns"
+    assert isinstance(
+        deserialized.uns["nested_dict"]["array"], np.ndarray
+    ), "Expected numpy array in nested dict in uns"
+    assert np.array_equal(
+        adata.uns["nested_dict"]["array"],
+        deserialized.uns["nested_dict"]["array"],
+    ), "Mismatch in numpy array in nested dict in uns"
 
 
 def test_load_anndata_from_nonexistent_file():
-    with pytest.raises(FileNotFoundError):
+    """Test loading AnnData from a nonexistent file."""
+    with pytest.raises(FileNotFoundError, match="No such file or directory"):
         load_anndata_from_json("nonexistent_file.json")
 
 
 def test_save_anndata_to_invalid_path(sample_adata):
+    """Test saving AnnData to an invalid path."""
     with pytest.raises(OSError):
         save_anndata_to_json(sample_adata, "/invalid/path/adata.json")
 
 
-def test_roundtrip_with_all_attributes():
+def test_roundtrip_with_all_attributes(tmp_path, mock_hash_file):
+    """Test full roundtrip serialization and deserialization with all AnnData attributes."""
     adata = create_sample_anndata(10, 5)
     adata.raw = adata.copy()
     adata.obsp["distances"] = np.random.random((10, 10))
     adata.varp["gene_correlation"] = np.random.random((5, 5))
 
-    with tempfile.NamedTemporaryFile(
-        mode="w+", suffix=".json", delete=False
-    ) as tmp:
-        save_anndata_to_json(adata, tmp.name)
-        loaded_adata = load_anndata_from_json(tmp.name)
-
-    assert np.allclose(adata.X, loaded_adata.X)
-    assert adata.obs.equals(loaded_adata.obs)
-    assert adata.var.equals(loaded_adata.var)
-    assert np.allclose(adata.raw.X, loaded_adata.raw.X)
-    assert np.allclose(adata.obsp["distances"], loaded_adata.obsp["distances"])
+    file_path = tmp_path / "roundtrip_adata.json"
+    save_anndata_to_json(adata, file_path)
+    loaded_adata = load_anndata_from_json(file_path)
+
+    assert np.allclose(
+        adata.X, loaded_adata.X
+    ), "Mismatch in X matrix after roundtrip"
+    assert adata.obs.equals(
+        loaded_adata.obs
+    ), "Mismatch in obs DataFrame after roundtrip"
+    assert adata.var.equals(
+        loaded_adata.var
+    ), "Mismatch in var DataFrame after roundtrip"
+    assert np.allclose(
+        adata.raw.X, loaded_adata.raw.X
+    ), "Mismatch in raw.X after roundtrip"
+    assert np.allclose(
+        adata.obsp["distances"], loaded_adata.obsp["distances"]
+    ), "Mismatch in obsp['distances'] after roundtrip"
     assert np.allclose(
         adata.varp["gene_correlation"], loaded_adata.varp["gene_correlation"]
-    )
+    ), "Mismatch in varp['gene_correlation'] after roundtrip"
+    assert (
+        mock_hash_file.call_count == 2
+    ), "Expected hash_file to be called twice"
+
+
+def test_handle_corrupted_json(tmp_path):
+    """Test handling of corrupted JSON files."""
+    file_path = tmp_path / "corrupted.json"
+    with file_path.open("w") as f:
+        f.write("{This is not valid JSON")
+
+    with pytest.raises(json.JSONDecodeError):
+        load_anndata_from_json(file_path)
diff --git a/src/pyrovelocity/tests/io/test_subset_data.py b/src/pyrovelocity/tests/io/test_subset_data.py
new file mode 100644
index 000000000..49ed91f37
--- /dev/null
+++ b/src/pyrovelocity/tests/io/test_subset_data.py
@@ -0,0 +1,65 @@
+"""Tests for `pyrovelocity.io.subset_data` module."""
+from pathlib import Path
+
+import pytest
+
+from pyrovelocity.io.subset_data import (
+    subset_anndata,
+)
+
+
+def test_subset_data_module():
+    from pyrovelocity.io import subset_data
+
+    print(subset_data.__file__)
+
+
+def test_subset_from_adata(default_sample_data):
+    """Test deriving a data subset from an AnnData object."""
+    n_obs = 50
+    n_vars = 10
+    subset_adata, _ = subset_anndata(
+        adata=default_sample_data, n_obs=n_obs, n_vars=n_vars
+    )
+    assert subset_adata.n_obs == n_obs
+    assert subset_adata.n_vars == n_vars
+
+
+def test_subset_from_file(default_sample_data_file):
+    """Test deriving a data subset from a file."""
+    n_obs = 50
+    subset_adata, _ = subset_anndata(
+        file_path=default_sample_data_file, n_obs=n_obs
+    )
+    assert subset_adata.n_obs == n_obs
+
+
+def test_invalid_input():
+    """Test handling of invalid inputs."""
+    with pytest.raises(ValueError):
+        subset_anndata()  # No AnnData object or file path provided
+
+
+def test_save_subset(default_sample_data, tmp_path):
+    """Test saving the subset to a file."""
+    n_obs = 50
+    output_file = tmp_path / "subset_adata.h5ad"
+    _, output_path = subset_anndata(
+        adata=default_sample_data,
+        n_obs=n_obs,
+        save_subset=True,
+        output_path=output_file,
+    )
+    assert output_path == output_file
+    assert Path(output_file).exists()
+
+
+def test_subset_specific_n_obs_vars(default_sample_data):
+    """Test deriving a data subset with specific n_obs and n_vars."""
+    n_obs = 50
+    n_vars = 10
+    subset_adata, _ = subset_anndata(
+        adata=default_sample_data, n_obs=n_obs, n_vars=n_vars
+    )
+    assert subset_adata.n_obs == n_obs
+    assert subset_adata.n_vars == n_vars
diff --git a/src/pyrovelocity/tests/models/test_velocity_model.py b/src/pyrovelocity/tests/models/test_velocity_model.py
index 1804fb2e8..7bb2dbcb9 100644
--- a/src/pyrovelocity/tests/models/test_velocity_model.py
+++ b/src/pyrovelocity/tests/models/test_velocity_model.py
@@ -5,8 +5,10 @@
 from pyro.distributions import Poisson
 from pyro.nn import PyroModule
 
-from pyrovelocity.models._velocity_model import LogNormalModel
-from pyrovelocity.models._velocity_model import VelocityModelAuto
+from pyrovelocity.models._velocity_model import (
+    LogNormalModel,
+    VelocityModelAuto,
+)
 
 
 def test_load__velocity_model():
@@ -76,7 +78,7 @@ def velocity_model_auto(self):
             decoder_on=False,
             add_offset=False,
             correct_library_size=True,
-            guide_type="velocity",
+            guide_type="auto",
             cell_specific_kinetics=None,
             kinetics_num=None,
         )
@@ -96,6 +98,38 @@ def test_inherited_create_plates(self, velocity_model_auto):
         assert cell_plate.size == 3
         assert gene_plate.size == 4
 
+    def test_get_rna(self, velocity_model_auto):
+        u_scale = torch.rand(4)
+        s_scale = torch.rand(4)
+        alpha = torch.rand(4)
+        beta = torch.rand(4)
+        gamma = torch.rand(4)
+        t = torch.rand(3, 1)
+        u0 = torch.zeros(4)
+        s0 = torch.zeros(4)
+        t0 = torch.zeros(4)
+        switching = torch.rand(4)
+        u_inf = torch.rand(4)
+        s_inf = torch.rand(4)
+
+        u, s = velocity_model_auto.get_rna(
+            u_scale,
+            s_scale,
+            alpha,
+            beta,
+            gamma,
+            t,
+            u0,
+            s0,
+            t0,
+            switching,
+            u_inf,
+            s_inf,
+        )
+
+        assert u.shape == (3, 4)
+        assert s.shape == (3, 4)
+
     def test_forward_method(self, velocity_model_auto):
         """Test the forward method"""
         u_obs = torch.rand(3, 4)
@@ -122,3 +156,23 @@ def test_forward_method(self, velocity_model_auto):
 
         assert u.shape == (3, 4)
         assert s.shape == (3, 4)
+
+    @pytest.mark.parametrize("add_offset", [True, False])
+    def test_add_offset(self, add_offset):
+        model = VelocityModelAuto(
+            num_cells=100,
+            num_genes=50,
+            likelihood="Poisson",
+            add_offset=add_offset,
+        )
+        assert model.add_offset == add_offset
+
+    @pytest.mark.parametrize("guide_type", ["auto", "auto_t0_constraint"])
+    def test_guide_type(self, guide_type):
+        model = VelocityModelAuto(
+            num_cells=100,
+            num_genes=50,
+            likelihood="Poisson",
+            guide_type=guide_type,
+        )
+        assert model.guide_type == guide_type
diff --git a/src/pyrovelocity/tests/models/test_velocity_module.py b/src/pyrovelocity/tests/models/test_velocity_module.py
index 59e7e9cc8..058c8246a 100644
--- a/src/pyrovelocity/tests/models/test_velocity_module.py
+++ b/src/pyrovelocity/tests/models/test_velocity_module.py
@@ -1,7 +1,105 @@
 """Tests for `pyrovelocity._velocity_module` module."""
 
 
+import pytest
+import torch
+from pyro.nn import PyroModule
+
+from pyrovelocity.models._velocity_module import VelocityModule
+
+
 def test_load__velocity_module():
     from pyrovelocity.models import _velocity_module
 
     print(_velocity_module.__file__)
+
+
+class TestVelocityModule:
+    @pytest.fixture
+    def default_config(self):
+        return {
+            "num_cells": 100,
+            "num_genes": 50,
+            "model_type": "auto",
+            "guide_type": "auto",
+            "likelihood": "Poisson",
+            "shared_time": True,
+            "t_scale_on": False,
+            "plate_size": 2,
+            "latent_factor": "none",
+            "latent_factor_operation": "selection",
+            "latent_factor_size": 30,
+            "inducing_point_size": 0,
+            "include_prior": True,
+            "use_gpu": "cpu",
+            "num_aux_cells": 0,
+            "only_cell_times": True,
+            "decoder_on": False,
+            "add_offset": False,
+            "correct_library_size": True,
+            "cell_specific_kinetics": None,
+            "kinetics_num": None,
+        }
+
+    @pytest.fixture
+    def velocity_module(self, default_config):
+        return VelocityModule(**default_config)
+
+    # def test_initialization(self, velocity_module, default_config):
+    #     assert isinstance(velocity_module, PyroModule)
+    #     assert velocity_module.num_cells == default_config["num_cells"]
+    #     assert velocity_module.num_genes == default_config["num_genes"]
+    #     assert velocity_module.guide_type == default_config["guide_type"]
+
+    # @pytest.mark.parametrize("guide_type", ["auto", "auto_t0_constraint"])
+    # def test_guide_type(self, default_config, guide_type):
+    #     config = default_config.copy()
+    #     config["guide_type"] = guide_type
+    #     velocity_module = VelocityModule(**config)
+    #     assert velocity_module.guide_type == guide_type
+
+    # def test_forward(self, velocity_module):
+    #     u_obs = torch.rand(100, 50)
+    #     s_obs = torch.rand(100, 50)
+    #     u_log_library = torch.rand(100, 1)
+    #     s_log_library = torch.rand(100, 1)
+
+    #     output = velocity_module(
+    #         u_obs,
+    #         s_obs,
+    #         u_log_library,
+    #         s_log_library,
+    #         u_log_library,
+    #         s_log_library,
+    #         u_log_library,
+    #         s_log_library,
+    #         torch.arange(100),
+    #     )
+
+    #     assert isinstance(output, tuple)
+    #     assert len(output) == 2
+    #     assert all(isinstance(item, dict) for item in output)
+
+    # def test_create_predictive(self, velocity_module):
+    #     predictive = velocity_module.create_predictive()
+    #     assert callable(predictive)
+
+    # def test_get_fn_args_from_batch(self, velocity_module):
+    #     batch = {
+    #         "U": torch.rand(100, 50),
+    #         "X": torch.rand(100, 50),
+    #         "u_lib_size": torch.rand(100, 1),
+    #         "s_lib_size": torch.rand(100, 1),
+    #         "u_lib_size_mean": torch.rand(100, 1),
+    #         "s_lib_size_mean": torch.rand(100, 1),
+    #         "u_lib_size_scale": torch.rand(100, 1),
+    #         "s_lib_size_scale": torch.rand(100, 1),
+    #         "ind_x": torch.arange(100),
+    #     }
+
+    #     args, kwargs = velocity_module._get_fn_args_from_batch(batch)
+
+    #     assert isinstance(args, tuple)
+    #     assert len(args) == 10  # 9 from batch + 1 None for time_info
+    #     assert isinstance(kwargs, dict)
+    #     assert len(kwargs) == 0
diff --git a/src/pyrovelocity/tests/plots/conftest.py b/src/pyrovelocity/tests/plots/conftest.py
index 18bbf29a2..10a81b628 100644
--- a/src/pyrovelocity/tests/plots/conftest.py
+++ b/src/pyrovelocity/tests/plots/conftest.py
@@ -6,7 +6,7 @@
 
 
 @pytest.fixture
-def adata_preprocessed():
+def adata_preprocessed_3_4():
     fixture_file_path = (
         files("pyrovelocity.tests.data") / "preprocessed_3_4.json"
     )
diff --git a/src/pyrovelocity/tests/plots/test_count_histograms.py b/src/pyrovelocity/tests/plots/test_count_histograms.py
index 1622f86a2..1bc2e0131 100644
--- a/src/pyrovelocity/tests/plots/test_count_histograms.py
+++ b/src/pyrovelocity/tests/plots/test_count_histograms.py
@@ -1,8 +1,8 @@
 from pyrovelocity.plots import plot_spliced_unspliced_histogram
 
 
-def test_plot_spliced_unspliced_histogram(adata_preprocessed, tmp_path):
-    chart = plot_spliced_unspliced_histogram(adata_preprocessed)
+def test_plot_spliced_unspliced_histogram(adata_preprocessed_3_4, tmp_path):
+    chart = plot_spliced_unspliced_histogram(adata_preprocessed_3_4)
     chart.save(tmp_path / "spliced_unspliced_histogram.pdf")
     chart.save(tmp_path / "spliced_unspliced_histogram.svg")
     chart.save(tmp_path / "spliced_unspliced_histogram.png")
diff --git a/src/pyrovelocity/tests/plots/test_genes.py b/src/pyrovelocity/tests/plots/test_genes.py
index dd9962626..a56b7e1ef 100644
--- a/src/pyrovelocity/tests/plots/test_genes.py
+++ b/src/pyrovelocity/tests/plots/test_genes.py
@@ -12,8 +12,8 @@ def test_model2_plot_gene_ranking(
     data_model2_reports_path,
 ):
     plot_gene_ranking(
-        posterior_samples=[posterior_samples_model2],
-        adata=[postprocessed_model2_data],
+        posterior_samples=posterior_samples_model2,
+        adata=postprocessed_model2_data,
         selected_genes=putative_model2_marker_genes,
         time_correlation_with="st",
         show_marginal_histograms=True,
@@ -31,8 +31,8 @@ def test_model1_plot_gene_ranking(
     data_model1_reports_path,
 ):
     plot_gene_ranking(
-        posterior_samples=[posterior_samples_model1],
-        adata=[postprocessed_model1_data],
+        posterior_samples=posterior_samples_model1,
+        adata=postprocessed_model1_data,
         selected_genes=putative_model1_marker_genes,
         time_correlation_with="st",
         show_marginal_histograms=True,
diff --git a/src/pyrovelocity/tests/tasks/test_data.py b/src/pyrovelocity/tests/tasks/test_data.py
index 262de7f10..5b1c49e71 100644
--- a/src/pyrovelocity/tests/tasks/test_data.py
+++ b/src/pyrovelocity/tests/tasks/test_data.py
@@ -8,9 +8,8 @@
 from pyrovelocity.tasks.data import (
     _validate_url_and_file,
     download_dataset,
-    load_anndata_from_path,
-    subset_anndata,
 )
+from pyrovelocity.utils import load_anndata_from_path
 
 
 def test_load_data_module():
@@ -149,57 +148,6 @@ def test_validate_invalid_url_format():
     assert "Invalid URL format" in message
 
 
-def test_subset_from_adata(default_sample_data):
-    """Test deriving a data subset from an AnnData object."""
-    n_obs = 50
-    n_vars = 10
-    subset_adata, _ = subset_anndata(
-        adata=default_sample_data, n_obs=n_obs, n_vars=n_vars
-    )
-    assert subset_adata.n_obs == n_obs
-    assert subset_adata.n_vars == n_vars
-
-
-def test_subset_from_file(default_sample_data_file):
-    """Test deriving a data subset from a file."""
-    n_obs = 50
-    subset_adata, _ = subset_anndata(
-        file_path=default_sample_data_file, n_obs=n_obs
-    )
-    assert subset_adata.n_obs == n_obs
-
-
-def test_invalid_input():
-    """Test handling of invalid inputs."""
-    with pytest.raises(ValueError):
-        subset_anndata()  # No AnnData object or file path provided
-
-
-def test_save_subset(default_sample_data, tmp_path):
-    """Test saving the subset to a file."""
-    n_obs = 50
-    output_file = tmp_path / "subset_adata.h5ad"
-    _, output_path = subset_anndata(
-        adata=default_sample_data,
-        n_obs=n_obs,
-        save_subset=True,
-        output_path=output_file,
-    )
-    assert output_path == output_file
-    assert Path(output_file).exists()
-
-
-def test_subset_specific_n_obs_vars(default_sample_data):
-    """Test deriving a data subset with specific n_obs and n_vars."""
-    n_obs = 50
-    n_vars = 10
-    subset_adata, _ = subset_anndata(
-        adata=default_sample_data, n_obs=n_obs, n_vars=n_vars
-    )
-    assert subset_adata.n_obs == n_obs
-    assert subset_adata.n_vars == n_vars
-
-
 def test_load_valid_h5ad_file(default_sample_data_file):
     """Test loading from a valid .h5ad file."""
     adata = load_anndata_from_path(default_sample_data_file)
diff --git a/src/pyrovelocity/tests/tasks/test_train.py b/src/pyrovelocity/tests/tasks/test_train.py
index ab4489708..f79df388c 100644
--- a/src/pyrovelocity/tests/tasks/test_train.py
+++ b/src/pyrovelocity/tests/tasks/test_train.py
@@ -2,6 +2,10 @@
 
 import pytest
 
+from pyrovelocity.io.serialization import save_anndata_to_json
+from pyrovelocity.tasks.train import train_dataset
+from pyrovelocity.utils import load_anndata_from_path
+
 
 def test_load_train():
     from pyrovelocity.tasks import train
@@ -19,3 +23,42 @@ def test_train_dataset(train_dataset_output):
 @pytest.mark.integration
 def test_train_dataset_model1(train_dataset_model1_output):
     return train_dataset_model1_output
+
+
+def test_train_dataset_pancreas(adata_preprocessed_pancreas_50_7, tmp_path):
+    data_set_name = "pancreas"
+    model_identifier = "model2"
+    models_path = tmp_path / "models"
+
+    result = train_dataset(
+        adata=adata_preprocessed_pancreas_50_7,
+        data_set_name=data_set_name,
+        model_identifier=model_identifier,
+        models_path=models_path,
+        max_epochs=10,
+        force=True,
+    )
+
+    (
+        data_model,
+        data_model_path,
+        trained_data_path,
+        model_path,
+        posterior_samples_path,
+        metrics_path,
+        run_info_path,
+        loss_plot_path,
+        loss_csv_path,
+    ) = result
+
+    assert data_model == f"{data_set_name}_{model_identifier}"
+    assert trained_data_path.exists()
+    assert model_path.exists()
+    assert posterior_samples_path.exists()
+    assert metrics_path.exists()
+    assert run_info_path.exists()
+    assert loss_plot_path.exists()
+    assert loss_csv_path.exists()
+
+    trained_adata = load_anndata_from_path(trained_data_path)
+    save_anndata_to_json(trained_adata, tmp_path / "trained_adata.json")
diff --git a/src/pyrovelocity/utils.py b/src/pyrovelocity/utils.py
index be041ff2d..5f06fc409 100644
--- a/src/pyrovelocity/utils.py
+++ b/src/pyrovelocity/utils.py
@@ -16,26 +16,22 @@
 import pandas as pd
 import rich.syntax
 import rich.tree
+import scanpy as sc
 import scvelo as scv
 import seaborn as sns
 import yaml
 from anndata._core.anndata import AnnData
 from beartype import beartype
-from beartype.typing import Callable, Dict, List, Tuple
+from beartype.typing import Callable, Dict, List, Tuple, TypeAlias
 from einops import EinopsError, reduce
 from jaxtyping import ArrayLike
+from scipy import sparse
 from scvi.data import synthetic_iid
 
 from pyrovelocity.io.compressedpickle import CompressedPickle
+from pyrovelocity.io.hash import hash_file
 from pyrovelocity.logging import configure_logging
 
-# import torch
-# from scipy.sparse import issparse
-# from sklearn.decomposition import PCA
-# from torch.nn.functional import relu
-# from pyrovelocity.models._transcription_dynamics import inv
-# from pyrovelocity.models import mrna_dynamics
-
 __all__ = [
     "anndata_counts_to_df",
     "attributes",
@@ -44,6 +40,7 @@
     "generate_public_api",
     "generate_sample_data",
     "internal_help",
+    "load_anndata_from_path",
     "mae",
     "mae_evaluate",
     "pretty_log_dict",
@@ -51,12 +48,17 @@
     "print_anndata",
     "print_attributes",
     "print_config_tree",
+    "quartile_coefficient_of_dispersion",
     "save_anndata_counts_to_dataframe",
     "str_to_bool",
 ]
 
 logger = configure_logging(__name__)
 
+SPMatrix: TypeAlias = sparse.spmatrix
+NDArray: TypeAlias = np.ndarray
+NDArrayOrSPMatrix: TypeAlias = NDArray | SPMatrix
+
 
 def mae(pred_counts, true_counts):
     """
@@ -175,18 +177,13 @@ def print_attributes(obj):
 def pretty_log_dict(d: dict) -> str:
     dict_as_string = "\n"
     for key, value in d.items():
-        # key_colored = colored(key, "green")
         key_colored = key
         if isinstance(value, ArrayLike):
             value_colored = f"{value.shape} {value.dtype}"
         else:
             value_lines = str(value).split("\n")
-            value_colored = "\n".join(
-                # colored(line, "white") for line in value_lines
-                line
-                for line in value_lines
-            )
-        dict_as_string += f"{key_colored}:\n{value_colored}\n"
+            value_colored = "\n".join(line for line in value_lines)
+        dict_as_string += f"{key_colored}:\n{type(value)}\n{value_colored}\n\n"
     return dict_as_string
 
 
@@ -310,6 +307,7 @@ def print_anndata(
 @beartype
 def anndata_string(
     anndata_obj: AnnData,
+    max_categories: int = 10,
 ) -> str:
     """
     Print a formatted representation of an AnnData object.
@@ -330,21 +328,61 @@ def anndata_string(
         >>> import pandas as pd
         >>> np.random.seed(42)
         >>> X = np.random.randn(10, 5)
-        >>> obs = pd.DataFrame({"clusters_coarse": np.random.randint(0, 2, 10),
-        ...                     "clusters": np.random.randint(0, 2, 10),
-        ...                     "S_score": np.random.rand(10),
-        ...                     "G2M_score": np.random.rand(10)})
+        >>> obs = pd.DataFrame(
+        ...    {"clusters_coarse": np.random.randint(0, 3, 10),
+        ...     "clusters": pd.Categorical(["A", "B", "A", "C", "B", "C", "A", "B", "C", "A"]),
+        ...     "condition": pd.Categorical(["control", "treatment", "control", "treatment", "control",
+        ...                                  "treatment", "control", "treatment", "control", "treatment"]),
+        ...     "S_score": np.random.rand(10),
+        ...     "G2M_score": np.random.rand(10)
+        ... })
         >>> var = pd.DataFrame({"gene_name": [f"gene_{i}" for i in range(5)]})
         >>> adata = AnnData(X, obs=obs, var=var)
+        >>> adata.uns['small_array'] = np.array([1, 2, 3])
+        >>> adata.uns['sparse_matrix'] = sparse.csr_matrix(([1, 2, 3], ([0, 1, 2], [1, 2, 0])), shape=(3, 3))
+        >>> adata.uns['dictionary'] = {'key1': 'value1', 'key2': 'value2'}
+        >>> adata.uns['dataframe'] = pd.DataFrame({'A': np.random.rand(10), 'B': np.random.rand(10)})
         >>> print_anndata(adata)  # doctest: +NORMALIZE_WHITESPACE
     """
     assert isinstance(
         anndata_obj, AnnData
     ), "Input object must be of type AnnData."
 
-    def format_elements(elements):
-        formatted = "\n".join([f"        {elem}," for elem in elements])
-        return formatted
+    @beartype
+    def format_elements(elements, prop_name: str) -> str:
+        formatted = []
+        for elem in elements:
+            elem_str = f"        {elem},"
+            if prop_name in ["obs", "var"]:
+                df = getattr(anndata_obj, prop_name)
+                elem_type = df[elem].dtype
+                elem_str += f" {elem_type},"
+                if pd.api.types.is_categorical_dtype(df[elem]):
+                    num_categories = len(df[elem].cat.categories)
+                    elem_str += f" {num_categories},"
+                    if num_categories < max_categories:
+                        categories = ", ".join(
+                            map(str, df[elem].cat.categories)
+                        )
+                        elem_str += f" [{categories}],"
+                else:
+                    num_distinct = df[elem].nunique()
+                    elem_str += f" {num_distinct},"
+            elif prop_name in ["uns", "obsm", "varm", "layers", "obsp", "varp"]:
+                obj = getattr(anndata_obj, prop_name)[elem]
+                if isinstance(obj, NDArrayOrSPMatrix):
+                    obj_type = type(obj).__name__
+                    obj_shape = " x ".join(map(str, obj.shape))
+                    elem_str += f" {obj_type}, {obj_shape},"
+                elif isinstance(obj, pd.DataFrame):
+                    obj_type = type(obj).__name__
+                    obj_shape = " x ".join(map(str, obj.shape))
+                    elem_str += f" {obj_type}, {obj_shape},"
+                else:
+                    obj_type = type(obj).__name__
+                    elem_str += f" {obj_type},"
+            formatted.append(elem_str)
+        return "\n".join(formatted)
 
     anndata_string = [
         f"\nAnnData object with n_obs × n_vars = {anndata_obj.n_obs} × {anndata_obj.n_vars}"
@@ -364,7 +402,7 @@ def format_elements(elements):
     for prop_name, elements in properties.items():
         if len(elements) > 0:
             anndata_string.append(
-                f"    {prop_name}:\n{format_elements(elements)}"
+                f"    {prop_name}:\n{format_elements(elements, prop_name)}"
             )
 
     return "\n".join(anndata_string)
@@ -603,6 +641,49 @@ def internal_help(obj: Callable | ModuleType):
     print("\n".join(processed_lines))
 
 
+@beartype
+def load_anndata_from_path(adata_path: str | Path) -> AnnData:
+    adata_path = Path(adata_path)
+    if adata_path.suffix not in {".h5ad", ".loom"}:
+        raise ValueError(
+            f"The input file {adata_path}\n"
+            "must be either a .h5ad or .loom file."
+        )
+    if os.path.isfile(adata_path) and os.access(adata_path, os.R_OK):
+        logger.info(f"Reading input file: {adata_path}")
+        adata = sc.read(filename=adata_path, cache=True)
+        adata_hash = hash_file(adata_path)
+        logger.info(
+            f"\nSuccessfully read input file: {adata_path}\n"
+            f"SHA-256 hash: {adata_hash}"
+        )
+        return adata
+    else:
+        raise ValueError(f"Cannot read input file: {adata_path}")
+
+
+@beartype
+def quartile_coefficient_of_dispersion(data: NDArray) -> NDArray:
+    q1 = np.percentile(data, 25, axis=0)
+    q3 = np.percentile(data, 75, axis=0)
+    return (q3 - q1) / (q3 + q1)
+
+
+@beartype
+def setup_colors(
+    adata: AnnData,
+    cell_state: str,
+) -> Dict[str, Tuple[float, float, float]]:
+    clusters = adata.obs.loc[:, cell_state]
+    color_dict = dict(
+        zip(
+            clusters.cat.categories,
+            sns.color_palette("deep", clusters.cat.categories.shape[0]),
+        )
+    )
+    return color_dict
+
+
 # TODO: remove unused functions
 # def log(x):
 #     """
diff --git a/src/pyrovelocity/workflows/constants.py b/src/pyrovelocity/workflows/constants.py
index 83dcd8a6c..86f1e3993 100644
--- a/src/pyrovelocity/workflows/constants.py
+++ b/src/pyrovelocity/workflows/constants.py
@@ -10,24 +10,49 @@
 
 load_dotenv()
 
-# Extract a subset of observations and variables from each
-# data set for testing purposes prior to model training when True.
+# Demo mode switches to the following settings:
+#
+#   PYROVELOCITY_TESTING_FLAG=True
+#   PYROVELOCITY_DATA_SUBSET=True
+#   PYROVELOCITY_UPLOAD_RESULTS=False
+#   PYROVELOCITY_OVERWRITE_CACHE=True
+#
 # Defaults to False if not set.
-PYROVELOCITY_TESTING_FLAG = str_to_bool(
-    os.getenv("PYROVELOCITY_TESTING_FLAG", "False")
+PYROVELOCITY_DEMO_FLAG = str_to_bool(
+    os.getenv("PYROVELOCITY_DEMO_FLAG", "False")
 )
 
-# Uses only a subset of the list of supported data sets when True.
-# Defaults to False if not set.
-PYROVELOCITY_DATA_SUBSET = str_to_bool(
-    os.getenv("PYROVELOCITY_DATA_SUBSET", "False")
-)
+if PYROVELOCITY_DEMO_FLAG:
+    PYROVELOCITY_TESTING_FLAG = True
+    PYROVELOCITY_DATA_SUBSET = True
+    PYROVELOCITY_UPLOAD_RESULTS = False
+    PYROVELOCITY_OVERWRITE_CACHE = True
+else:
+    # Extract a subset of observations and variables from each
+    # data set for testing purposes prior to model training when True.
+    # Defaults to False if not set.
+    PYROVELOCITY_TESTING_FLAG = str_to_bool(
+        os.getenv("PYROVELOCITY_TESTING_FLAG", "False")
+    )
 
-# Overwrite the cache of processed data sets when True.
-# Defaults to False if not set.
-PYROVELOCITY_OVERWRITE_CACHE = str_to_bool(
-    os.getenv("PYROVELOCITY_OVERWRITE_CACHE", "False")
-)
+    # Uses only a subset of the list of supported data sets when True.
+    # Defaults to False if not set.
+    PYROVELOCITY_DATA_SUBSET = str_to_bool(
+        os.getenv("PYROVELOCITY_DATA_SUBSET", "False")
+    )
+
+    # Overwrite the cache of processed data sets when True.
+    # Defaults to False if not set.
+    PYROVELOCITY_OVERWRITE_CACHE = str_to_bool(
+        os.getenv("PYROVELOCITY_OVERWRITE_CACHE", "False")
+    )
+
+    # Upload summary results of the workflow to human-readable
+    # object storage path when True.
+    # Defaults to True if not set.
+    PYROVELOCITY_UPLOAD_RESULTS = str_to_bool(
+        os.getenv("PYROVELOCITY_UPLOAD_RESULTS", "True")
+    )
 
 # Use the cache of processed data sets when True.
 # Defaults to True if not set.
@@ -35,12 +60,6 @@
     os.getenv("PYROVELOCITY_CACHE_FLAG", "True")
 )
 
-# Upload summary results of the workflow to human-readable
-# object storage path when True.
-# Defaults to True if not set.
-PYROVELOCITY_UPLOAD_RESULTS = str_to_bool(
-    os.getenv("PYROVELOCITY_UPLOAD_RESULTS", "True")
-)
 
 logger.info(
     f"\nPYROVELOCITY_TESTING_FLAG: {PYROVELOCITY_TESTING_FLAG}\n"
diff --git a/src/pyrovelocity/workflows/main_configuration.py b/src/pyrovelocity/workflows/main_configuration.py
index 82c3d2a9c..1353d992c 100644
--- a/src/pyrovelocity/workflows/main_configuration.py
+++ b/src/pyrovelocity/workflows/main_configuration.py
@@ -158,6 +158,11 @@ class PostprocessConfiguration(DataClassJSONMixin):
     )
 
 
+@dataclass
+class SummarizeConfiguration(DataClassJSONMixin):
+    selected_genes: list[str] = field(default_factory=lambda: [""])
+
+
 @dataclass
 class WorkflowConfiguration(DataClassJSONMixin):
     download_dataset: DownloadDatasetInterface
@@ -165,6 +170,7 @@ class WorkflowConfiguration(DataClassJSONMixin):
     training_configuration_1: PyroVelocityTrainInterface
     training_configuration_2: PyroVelocityTrainInterface
     postprocess_configuration: PostprocessConfiguration
+    summarize_configuration: SummarizeConfiguration
     training_resources_requests: ResourcesJSON
     training_resources_limits: ResourcesJSON
     postprocessing_resources_requests: ResourcesJSON
@@ -203,6 +209,7 @@ class PostprocessOutputs(DataClassJSONMixin):
 
 @dataclass
 class SummarizeOutputs(DataClassJSONMixin):
+    data_model: str
     data_model_reports: FlyteDirectory
     dataframe: FlyteFile
     run_metrics_path: FlyteFile
@@ -210,14 +217,22 @@ class SummarizeOutputs(DataClassJSONMixin):
     loss_plot_path: FlyteFile
     loss_csv_path: FlyteFile
     combined_metrics_path: FlyteFile
+    pyrovelocity_data: FlyteFile
+    postprocessed_data: FlyteFile
 
 
 @dataclass
 class CombinedMetricsOutputs(DataClassJSONMixin):
-    json_metrics: FlyteFile
-    latex_metrics: FlyteFile
-    html_metrics: FlyteFile
-    md_metrics: FlyteFile
+    metrics_json: FlyteFile
+    metrics_latex: FlyteFile
+    metrics_html: FlyteFile
+    metrics_md: FlyteFile
+    elbo_latex: FlyteFile
+    elbo_html: FlyteFile
+    elbo_md: FlyteFile
+    mae_latex: FlyteFile
+    mae_html: FlyteFile
+    mae_md: FlyteFile
 
 
 simulated_dataset_args = DownloadDatasetInterface(
@@ -250,12 +265,23 @@ class CombinedMetricsOutputs(DataClassJSONMixin):
 simulated_postprocess_configuration = PostprocessConfiguration(
     number_posterior_samples=NUMBER_POSTERIOR_SAMPLES,
 )
+simulated_summary_configuration = SummarizeConfiguration(
+    selected_genes=[
+        "150",
+        "366",
+        "1853",
+        "1157",
+        "804",
+        "360",
+    ]
+)
 simulated_configuration = WorkflowConfiguration(
     download_dataset=simulated_dataset_args,
     preprocess_data=simulated_preprocess_data_args,
     training_configuration_1=simulated_train_model1_args,
     training_configuration_2=simulated_train_model2_args,
     postprocess_configuration=simulated_postprocess_configuration,
+    summarize_configuration=simulated_summary_configuration,
     training_resources_requests=default_training_resource_requests,
     training_resources_limits=default_training_resource_limits,
     postprocessing_resources_requests=default_resource_requests,
@@ -291,12 +317,23 @@ class CombinedMetricsOutputs(DataClassJSONMixin):
 pancreas_postprocess_configuration = PostprocessConfiguration(
     number_posterior_samples=NUMBER_POSTERIOR_SAMPLES,
 )
+pancreas_summary_configuration = SummarizeConfiguration(
+    selected_genes=[
+        "Cpe",
+        "Ins2",
+        "Cck",
+        "Ttr",
+        "Krt7",
+        "Spp1",
+    ]
+)
 pancreas_configuration = WorkflowConfiguration(
     download_dataset=pancreas_dataset_args,
     preprocess_data=pancreas_preprocess_data_args,
     training_configuration_1=pancreas_train_model1_args,
     training_configuration_2=pancreas_train_model2_args,
     postprocess_configuration=pancreas_postprocess_configuration,
+    summarize_configuration=pancreas_summary_configuration,
     training_resources_requests=default_training_resource_requests,
     training_resources_limits=default_training_resource_limits,
     postprocessing_resources_requests=medium_resource_requests,
@@ -332,12 +369,23 @@ class CombinedMetricsOutputs(DataClassJSONMixin):
 bonemarrow_postprocess_configuration = PostprocessConfiguration(
     number_posterior_samples=NUMBER_POSTERIOR_SAMPLES,
 )
+bonemarrow_summary_configuration = SummarizeConfiguration(
+    selected_genes=[
+        "ELANE",
+        "HDAC7",
+        "CITED2",
+        "SLC40A1",
+        "VPREB1",
+        "MYB",
+    ]
+)
 bonemarrow_configuration = WorkflowConfiguration(
     download_dataset=bonemarrow_dataset_args,
     preprocess_data=bonemarrow_preprocess_data_args,
     training_configuration_1=bonemarrow_train_model1_args,
     training_configuration_2=bonemarrow_train_model2_args,
     postprocess_configuration=bonemarrow_postprocess_configuration,
+    summarize_configuration=bonemarrow_summary_configuration,
     training_resources_requests=default_training_resource_requests,
     training_resources_limits=default_training_resource_limits,
     postprocessing_resources_requests=medium_resource_requests,
@@ -374,12 +422,23 @@ class CombinedMetricsOutputs(DataClassJSONMixin):
 pbmc5k_postprocess_configuration = PostprocessConfiguration(
     number_posterior_samples=NUMBER_POSTERIOR_SAMPLES,
 )
+pbmc5k_summary_configuration = SummarizeConfiguration(
+    selected_genes=[
+        "LYZ",
+        "S100A9",
+        "IGHM",
+        "HLA-DQA1",
+        "MS4A1",
+        "IL32",
+    ]
+)
 pbmc5k_configuration = WorkflowConfiguration(
     download_dataset=pbmc5k_dataset_args,
     preprocess_data=pbmc5k_preprocess_data_args,
     training_configuration_1=pbmc5k_train_model1_args,
     training_configuration_2=pbmc5k_train_model2_args,
     postprocess_configuration=pbmc5k_postprocess_configuration,
+    summarize_configuration=pbmc5k_summary_configuration,
     training_resources_requests=large_training_resource_requests,
     training_resources_limits=large_training_resource_limits,
     postprocessing_resources_requests=large_resource_requests,
@@ -416,12 +475,23 @@ class CombinedMetricsOutputs(DataClassJSONMixin):
 pbmc10k_postprocess_configuration = PostprocessConfiguration(
     number_posterior_samples=NUMBER_POSTERIOR_SAMPLES,
 )
+pbmc10k_summary_configuration = SummarizeConfiguration(
+    selected_genes=[
+        "LYZ",
+        "S100A9",
+        "IGHM",
+        "HLA-DQA1",
+        "MS4A1",
+        "IL32",
+    ]
+)
 pbmc10k_configuration = WorkflowConfiguration(
     download_dataset=pbmc10k_dataset_args,
     preprocess_data=pbmc10k_preprocess_data_args,
     training_configuration_1=pbmc10k_train_model1_args,
     training_configuration_2=pbmc10k_train_model2_args,
     postprocess_configuration=pbmc10k_postprocess_configuration,
+    summarize_configuration=pbmc10k_summary_configuration,
     training_resources_requests=large_training_resource_requests,
     training_resources_limits=large_training_resource_limits,
     postprocessing_resources_requests=large_resource_requests,
@@ -460,12 +530,23 @@ class CombinedMetricsOutputs(DataClassJSONMixin):
 pbmc68k_postprocess_configuration = PostprocessConfiguration(
     number_posterior_samples=NUMBER_POSTERIOR_SAMPLES,
 )
+pbmc68k_summary_configuration = SummarizeConfiguration(
+    selected_genes=[
+        "TPT1",
+        "EEF1A1",
+        "LTB",
+        "GNLY",
+        "TMSB4X",
+        "FTL",
+    ]
+)
 pbmc68k_configuration = WorkflowConfiguration(
     download_dataset=pbmc68k_dataset_args,
     preprocess_data=pbmc68k_preprocess_data_args,
     training_configuration_1=pbmc68k_train_model1_args,
     training_configuration_2=pbmc68k_train_model2_args,
     postprocess_configuration=pbmc68k_postprocess_configuration,
+    summarize_configuration=pbmc68k_summary_configuration,
     training_resources_requests=large_training_resource_requests,
     training_resources_limits=large_training_resource_limits,
     postprocessing_resources_requests=large_resource_requests,
@@ -502,12 +583,23 @@ class CombinedMetricsOutputs(DataClassJSONMixin):
 pons_postprocess_configuration = PostprocessConfiguration(
     number_posterior_samples=NUMBER_POSTERIOR_SAMPLES,
 )
+pons_summary_configuration = SummarizeConfiguration(
+    selected_genes=[
+        "Mag",
+        "Cldn11",
+        "Cntn1",
+        "Marcks",
+        "Tubb4a",
+        "Mbp",
+    ]
+)
 pons_configuration = WorkflowConfiguration(
     download_dataset=pons_dataset_args,
     preprocess_data=pons_preprocess_data_args,
     training_configuration_1=pons_train_model1_args,
     training_configuration_2=pons_train_model2_args,
     postprocess_configuration=pons_postprocess_configuration,
+    summarize_configuration=pons_summary_configuration,
     training_resources_requests=default_training_resource_requests,
     training_resources_limits=default_training_resource_limits,
     postprocessing_resources_requests=medium_resource_requests,
@@ -544,12 +636,23 @@ class CombinedMetricsOutputs(DataClassJSONMixin):
 larry_neu_postprocess_configuration = PostprocessConfiguration(
     number_posterior_samples=NUMBER_POSTERIOR_SAMPLES,
 )
+larry_neu_summary_configuration = SummarizeConfiguration(
+    selected_genes=[
+        "S100a8",
+        "S100a9",
+        "Ngp",
+        "Lilrb4",
+        "Mpp1",
+        "Srgn",
+    ]
+)
 larry_neu_configuration = WorkflowConfiguration(
     download_dataset=larry_neu_dataset_args,
     preprocess_data=larry_neu_preprocess_data_args,
     training_configuration_1=larry_neu_train_model1_args,
     training_configuration_2=larry_neu_train_model2_args,
     postprocess_configuration=larry_neu_postprocess_configuration,
+    summarize_configuration=larry_neu_summary_configuration,
     training_resources_requests=default_training_resource_requests,
     training_resources_limits=default_training_resource_limits,
     postprocessing_resources_requests=medium_resource_requests,
@@ -586,12 +689,23 @@ class CombinedMetricsOutputs(DataClassJSONMixin):
 larry_mono_postprocess_configuration = PostprocessConfiguration(
     number_posterior_samples=NUMBER_POSTERIOR_SAMPLES,
 )
+larry_mono_summary_configuration = SummarizeConfiguration(
+    selected_genes=[
+        "Vim",
+        "Fcer1g",
+        "Fth1",
+        "Ctsc",
+        "Itm2b",
+        "Sell",
+    ]
+)
 larry_mono_configuration = WorkflowConfiguration(
     download_dataset=larry_mono_dataset_args,
     preprocess_data=larry_mono_preprocess_data_args,
     training_configuration_1=larry_mono_train_model1_args,
     training_configuration_2=larry_mono_train_model2_args,
     postprocess_configuration=larry_mono_postprocess_configuration,
+    summarize_configuration=larry_mono_summary_configuration,
     training_resources_requests=default_training_resource_requests,
     training_resources_limits=default_training_resource_limits,
     postprocessing_resources_requests=medium_resource_requests,
@@ -628,12 +742,23 @@ class CombinedMetricsOutputs(DataClassJSONMixin):
 larry_multilineage_postprocess_configuration = PostprocessConfiguration(
     number_posterior_samples=NUMBER_POSTERIOR_SAMPLES,
 )
+larry_multilineage_summary_configuration = SummarizeConfiguration(
+    selected_genes=[
+        "Itgb2",
+        "S100a9",
+        "Fcer1g",
+        "Lilrb4",
+        "Vim",
+        "Serbp1",
+    ]
+)
 larry_multilineage_configuration = WorkflowConfiguration(
     download_dataset=larry_multilineage_dataset_args,
     preprocess_data=larry_multilineage_preprocess_data_args,
     training_configuration_1=larry_multilineage_train_model1_args,
     training_configuration_2=larry_multilineage_train_model2_args,
     postprocess_configuration=larry_multilineage_postprocess_configuration,
+    summarize_configuration=larry_multilineage_summary_configuration,
     training_resources_requests=default_training_resource_requests,
     training_resources_limits=default_training_resource_limits,
     postprocessing_resources_requests=medium_resource_requests,
@@ -672,6 +797,16 @@ class CombinedMetricsOutputs(DataClassJSONMixin):
 larry_postprocess_configuration = PostprocessConfiguration(
     number_posterior_samples=NUMBER_POSTERIOR_SAMPLES,
 )
+larry_summary_configuration = SummarizeConfiguration(
+    selected_genes=[
+        "Itgb2",
+        "S100a8",
+        "Lyz2",
+        "Fcer1g",
+        "Csf2rb",
+        "Ms4a3",
+    ]
+)
 larry_accelerator_type = "nvidia-tesla-a100"
 larry_configuration = WorkflowConfiguration(
     download_dataset=larry_dataset_args,
@@ -679,6 +814,7 @@ class CombinedMetricsOutputs(DataClassJSONMixin):
     training_configuration_1=larry_train_model1_args,
     training_configuration_2=larry_train_model2_args,
     postprocess_configuration=larry_postprocess_configuration,
+    summarize_configuration=larry_summary_configuration,
     training_resources_requests=extra_large_training_resource_requests,
     training_resources_limits=extra_large_training_resource_limits,
     postprocessing_resources_requests=large_resource_requests,
diff --git a/src/pyrovelocity/workflows/main_workflow.py b/src/pyrovelocity/workflows/main_workflow.py
index ad0d21d3f..187df3f4f 100644
--- a/src/pyrovelocity/workflows/main_workflow.py
+++ b/src/pyrovelocity/workflows/main_workflow.py
@@ -3,11 +3,13 @@
 from datetime import timedelta
 from pathlib import Path
 
+import matplotlib
 from beartype.typing import List
 from flytekit import Resources, current_context, dynamic, task
 from flytekit.extras.accelerators import T4, GPUAccelerator
 from flytekit.types.directory import FlyteDirectory
 from flytekit.types.file import FlyteFile
+from matplotlib import pyplot as plt
 from returns.result import Failure, Success
 
 from pyrovelocity.interfaces import (
@@ -19,18 +21,26 @@
     copy_files_to_directory,
     create_tarball_from_filtered_dir,
 )
+from pyrovelocity.io.datasets import larry_cospar
 from pyrovelocity.io.gcs import upload_file_concurrently
-from pyrovelocity.io.json import (
+from pyrovelocity.io.metrics import (
     add_duration_to_run_info,
     combine_json_files,
-    generate_tables,
+    generate_and_save_metric_tables,
     load_json,
 )
 from pyrovelocity.logging import configure_logging
+from pyrovelocity.plots import (
+    plot_lineage_fate_correlation,
+)
+from pyrovelocity.styles.colors import LARRY_CELL_TYPE_COLORS
 from pyrovelocity.tasks.data import download_dataset
 from pyrovelocity.tasks.postprocess import postprocess_dataset
 from pyrovelocity.tasks.preprocess import preprocess_dataset
 from pyrovelocity.tasks.summarize import summarize_dataset
+from pyrovelocity.tasks.time_fate_correlation import (
+    configure_time_lineage_fate_plot,
+)
 from pyrovelocity.tasks.train import train_dataset
 from pyrovelocity.workflows.constants import (
     PYROVELOCITY_CACHE_FLAG,
@@ -42,6 +52,7 @@
     PostprocessOutputs,
     PreprocessOutputs,
     ResourcesJSON,
+    SummarizeConfiguration,
     SummarizeOutputs,
     TrainingOutputs,
     WorkflowConfiguration,
@@ -79,10 +90,11 @@
 DOWNLOAD_CACHE_VERSION = f"{CACHE_VERSION}.0"
 PREPROCESS_CACHE_VERSION = f"{CACHE_VERSION}.0"
 TRAIN_CACHE_VERSION = f"{CACHE_VERSION}.0"
-POSTPROCESS_CACHE_VERSION = f"{CACHE_VERSION}.0"
-SUMMARIZE_CACHE_VERSION = f"{CACHE_VERSION}.0"
-UPLOAD_CACHE_VERSION = f"{CACHE_VERSION}.4"
-COMBINE_METRICS_CACHE_VERSION = f"{CACHE_VERSION}.4"
+POSTPROCESS_CACHE_VERSION = f"{CACHE_VERSION}.2"
+SUMMARIZE_CACHE_VERSION = f"{CACHE_VERSION}.3"
+UPLOAD_CACHE_VERSION = f"{CACHE_VERSION}.7"
+LINEAGE_FATE_CORRELATION_CACHE_VERSION = f"{CACHE_VERSION}.5"
+COMBINE_METRICS_CACHE_VERSION = f"{CACHE_VERSION}.5"
 DEFAULT_ACCELERATOR_TYPE: GPUAccelerator = T4
 
 
@@ -252,6 +264,7 @@ def summarize_data(
     preprocess_data_args: PreprocessDataInterface,
     postprocessing_outputs: PostprocessOutputs,
     training_outputs: TrainingOutputs,
+    summarize_configuration: SummarizeConfiguration,
 ) -> SummarizeOutputs:
     model_path = training_outputs.model_path.download()
     pyrovelocity_data_path = postprocessing_outputs.pyrovelocity_data.download()
@@ -277,6 +290,7 @@ def summarize_data(
         postprocessed_data_path=postprocessed_data_path,
         cell_state=preprocess_data_args.cell_state,
         vector_field_basis=preprocess_data_args.vector_field_basis,
+        selected_genes=summarize_configuration.selected_genes,
     )
     print(
         f"\ndata_model_reports_path: {data_model_reports_path}\n",
@@ -314,6 +328,7 @@ def summarize_data(
         )
 
     return SummarizeOutputs(
+        data_model=str(training_outputs.data_model),
         data_model_reports=FlyteDirectory(path=str(data_model_reports_path)),
         dataframe=FlyteFile(path=str(dataframe_path)),
         run_metrics_path=FlyteFile(path=str(metrics_path)),
@@ -321,6 +336,8 @@ def summarize_data(
         loss_plot_path=FlyteFile(path=str(loss_plot_path)),
         loss_csv_path=FlyteFile(path=str(loss_csv_path)),
         combined_metrics_path=FlyteFile(path=str(combined_metrics_path)),
+        pyrovelocity_data=FlyteFile(path=str(pyrovelocity_data_path)),
+        postprocessed_data=FlyteFile(path=str(postprocessed_data_path)),
     )
 
 
@@ -355,6 +372,7 @@ def upload_summary(
             ".json",
             ".pdf",
             ".png",
+            ".dill.zst",
         ),
     )
 
@@ -383,6 +401,7 @@ def map_model_configurations_over_data_set(
     train_model_configuration_1: PyroVelocityTrainInterface = PyroVelocityTrainInterface(),
     train_model_configuration_2: PyroVelocityTrainInterface = PyroVelocityTrainInterface(),
     postprocess_configuration: PostprocessConfiguration = PostprocessConfiguration(),
+    summarize_configuration: SummarizeConfiguration = SummarizeConfiguration(),
     train_model_resource_requests: ResourcesJSON = default_training_resource_requests,
     train_model_resource_limits: ResourcesJSON = default_training_resource_limits,
     postprocessing_resource_requests: ResourcesJSON = default_resource_requests,
@@ -433,10 +452,9 @@ def map_model_configurations_over_data_set(
         train_model_configuration_2,
     ]
 
-    model_outputs: list[TrainingOutputs] = []
-    dataset_summaries: list[SummarizeOutputs] = []
+    model_outputs: list[SummarizeOutputs] = []
     for train_model_configuration in train_model_configurations:
-        model_output = train_model(
+        training_output = train_model(
             preprocess_outputs=processed_outputs,
             train_model_configuration=train_model_configuration,
         ).with_overrides(
@@ -444,21 +462,21 @@ def map_model_configurations_over_data_set(
             limits=Resources(**asdict(train_model_resource_limits)),
             accelerator=GPUAccelerator(accelerator_type),
         )
-        model_outputs.append(model_output)
 
-        postprocessing_outputs = postprocess_data(
+        postprocessing_output = postprocess_data(
             preprocess_data_args=preprocess_data_args,
-            training_outputs=model_output,
+            training_outputs=training_output,
             postprocess_configuration=postprocess_configuration,
         ).with_overrides(
             requests=Resources(**asdict(postprocessing_resource_requests)),
             limits=Resources(**asdict(postprocessing_resource_limits)),
         )
 
-        dataset_summary = summarize_data(
+        summarize_output = summarize_data(
             preprocess_data_args=preprocess_data_args,
-            postprocessing_outputs=postprocessing_outputs,
-            training_outputs=model_output,
+            postprocessing_outputs=postprocessing_output,
+            training_outputs=training_output,
+            summarize_configuration=summarize_configuration,
         ).with_overrides(
             requests=Resources(**asdict(summarizing_resource_requests)),
             limits=Resources(**asdict(summarizing_resource_limits)),
@@ -466,13 +484,137 @@ def map_model_configurations_over_data_set(
 
         if upload_results:
             upload_summary(
-                summarize_outputs=dataset_summary,
-                training_outputs=model_output,
+                training_outputs=training_output,
+                summarize_outputs=summarize_output,
             )
 
-        dataset_summaries.append(dataset_summary)
+        model_outputs.append(summarize_output)
 
-    return dataset_summaries
+    return model_outputs
+
+
+@task(
+    cache=PYROVELOCITY_CACHE_FLAG,
+    cache_version=LINEAGE_FATE_CORRELATION_CACHE_VERSION,
+    retries=3,
+    interruptible=False,
+    timeout=timedelta(minutes=120),
+    requests=Resources(cpu="8", mem="30Gi", ephemeral_storage="100Gi"),
+    limits=Resources(cpu="16", mem="100Gi", ephemeral_storage="500Gi"),
+    enable_deck=False,
+)
+def combine_time_lineage_fate_correlation(
+    results: List[List[SummarizeOutputs]],
+) -> List[FlyteFile]:
+    print(results)
+    model_ordered_results = list(map(list, zip(*results)))
+    print(model_ordered_results)
+    time_lineage_fate_correlation_plots = []
+
+    for model_results in model_ordered_results:
+        print(model_results)
+        n_rows = len(model_results)
+        n_cols = 7
+        width = 14
+        height = width * (n_rows / n_cols) + 1
+
+        fig = plt.figure(figsize=(width, height))
+
+        gs = fig.add_gridspec(
+            n_rows + 1,
+            n_cols + 1,
+            width_ratios=[0.02] + [1] * n_cols,
+            height_ratios=[1] * n_rows + [0.2],
+        )
+
+        adata_cospar = larry_cospar()
+
+        all_axes = []
+        for i, model_output in enumerate(model_results):
+            data_set_model_pairing = model_output.data_model
+
+            postprocessed_data_path = model_output.postprocessed_data.download()
+
+            posterior_samples_path = model_output.pyrovelocity_data.download()
+
+            plot_path = Path(
+                f"time_fate_correlation_{data_set_model_pairing}.pdf"
+            )
+
+            axes = [fig.add_subplot(gs[i, j + 1]) for j in range(n_cols)]
+            all_axes.append(axes)
+
+            plot_lineage_fate_correlation(
+                posterior_samples_path=posterior_samples_path,
+                adata_pyrovelocity=postprocessed_data_path,
+                adata_cospar=adata_cospar,
+                all_axes=axes,
+                fig=fig,
+                state_color_dict=LARRY_CELL_TYPE_COLORS,
+                lineage_fate_correlation_path=plot_path,
+                save_plot=False,
+                ylabel="",
+                show_titles=True if i == 0 else False,
+                show_colorbars=False,
+                default_fontsize=12
+                if matplotlib.rcParams["text.usetex"]
+                else 9,
+            )
+
+        time_lineage_fate_correlation_plot = configure_time_lineage_fate_plot(
+            fig=fig,
+            gs=gs,
+            all_axes=all_axes,
+            row_labels=[
+                "a",
+                "b",
+                "c",
+                "d",
+            ][:n_rows],
+            vertical_texts=[
+                "Monocytes",
+                "Neutrophils",
+                "Multilineage",
+                "All lineages",
+            ][:n_rows],
+            reports_path=Path("."),
+            model_identifier=data_set_model_pairing,
+        )
+
+        time_lineage_fate_correlation_plots.append(
+            time_lineage_fate_correlation_plot
+        )
+
+    ctx = current_context()
+    execution_id = ctx.execution_id.name
+
+    attempted_upload_results = []
+
+    for file in time_lineage_fate_correlation_plots:
+        for ext in ["", ".png"]:
+            upload_result = upload_file_concurrently(
+                bucket_name=f"pyrovelocity/reports/{execution_id}",
+                source_filename=f"{file}{ext}",
+                destination_blob_name=f"{file}{ext}",
+            )
+            attempted_upload_results.append(upload_result)
+
+    if all(isinstance(result, Success) for result in attempted_upload_results):
+        logger.info("\nAll time lineage fate correlation uploads successful.")
+    else:
+        logger.error(
+            "\nOne or more time lineage fate correlation uploads failed."
+        )
+        failed_uploads = [
+            str(file)
+            for file, result in zip(
+                time_lineage_fate_correlation_plots, attempted_upload_results
+            )
+            if isinstance(result, Failure)
+        ]
+        logger.info(f"Failed uploads: {', '.join(failed_uploads)}")
+
+    return [FlyteFile(path=str(x)) for x in time_lineage_fate_correlation_plots]
 
 
 @task(
@@ -507,32 +649,15 @@ def combine_all_metrics(
     with json_metrics_file.open("w") as f:
         json.dump(combined_metrics, f, indent=2)
 
-    latex_table, html_table, markdown_table, _ = generate_tables(
-        combined_metrics
+    files_to_upload = generate_and_save_metric_tables(
+        json_data=combined_metrics,
+        output_dir=Path("."),
     )
 
-    latex_metrics_file = Path("combined_metrics_table.tex")
-    with latex_metrics_file.open("w") as f:
-        f.write(latex_table)
-
-    html_metrics_file = Path("combined_metrics_table.html")
-    with html_metrics_file.open("w") as f:
-        f.write(html_table)
-
-    md_metrics_file = Path("combined_metrics_table.md")
-    with md_metrics_file.open("w") as f:
-        f.write(markdown_table)
-
     ctx = current_context()
     execution_id = ctx.execution_id.name
 
-    files_to_upload = [
-        json_metrics_file,
-        latex_metrics_file,
-        html_metrics_file,
-        md_metrics_file,
-    ]
-    upload_results = []
+    attempted_upload_results = []
 
     for file in files_to_upload:
         upload_result = upload_file_concurrently(
@@ -540,29 +665,41 @@ def combine_all_metrics(
             source_filename=file,
             destination_blob_name=str(file),
         )
-        upload_results.append(upload_result)
+        attempted_upload_results.append(upload_result)
 
-    if all(isinstance(result, Success) for result in upload_results):
+    if all(isinstance(result, Success) for result in attempted_upload_results):
         print("\nAll uploads successful.")
         return CombinedMetricsOutputs(
-            json_metrics=FlyteFile(path=str(json_metrics_file)),
-            latex_metrics=FlyteFile(path=str(latex_metrics_file)),
-            html_metrics=FlyteFile(path=str(html_metrics_file)),
-            md_metrics=FlyteFile(path=str(md_metrics_file)),
+            metrics_json=FlyteFile(path=str(files_to_upload[0])),
+            metrics_html=FlyteFile(path=str(files_to_upload[1])),
+            metrics_latex=FlyteFile(path=str(files_to_upload[2])),
+            metrics_md=FlyteFile(path=str(files_to_upload[3])),
+            elbo_html=FlyteFile(path=str(files_to_upload[4])),
+            elbo_latex=FlyteFile(path=str(files_to_upload[5])),
+            elbo_md=FlyteFile(path=str(files_to_upload[6])),
+            mae_html=FlyteFile(path=str(files_to_upload[7])),
+            mae_latex=FlyteFile(path=str(files_to_upload[8])),
+            mae_md=FlyteFile(path=str(files_to_upload[9])),
         )
     else:
         print("\nOne or more uploads failed.")
         failed_uploads = [
             str(file)
-            for file, result in zip(files_to_upload, upload_results)
+            for file, result in zip(files_to_upload, attempted_upload_results)
             if isinstance(result, Failure)
         ]
         print(f"Failed uploads: {', '.join(failed_uploads)}")
         return CombinedMetricsOutputs(
-            json_metrics=FlyteFile(path=""),
-            latex_metrics=FlyteFile(path=""),
-            html_metrics=FlyteFile(path=""),
-            md_metrics=FlyteFile(path=""),
+            metrics_json=FlyteFile(path=""),
+            metrics_latex=FlyteFile(path=""),
+            metrics_html=FlyteFile(path=""),
+            metrics_md=FlyteFile(path=""),
+            elbo_html=FlyteFile(path=""),
+            elbo_latex=FlyteFile(path=""),
+            elbo_md=FlyteFile(path=""),
+            mae_html=FlyteFile(path=""),
+            mae_latex=FlyteFile(path=""),
+            mae_md=FlyteFile(path=""),
         )
 
 
@@ -585,31 +722,44 @@ def training_workflow(
     Conditionally executes configurations based on the value of PYROVELOCITY_DATA_SUBSET.
     """
     results = []
+
+    stationary_configurations = [
+        (pbmc5k_configuration, "pbmc5k"),
+        (pbmc10k_configuration, "pbmc10k"),
+        (pbmc68k_configuration, "pbmc68k"),
+    ]
+
+    developmental_configurations = [
+        # (bonemarrow_configuration, "bonemarrow"),
+        (pancreas_configuration, "pancreas"),
+        # (pons_configuration, "pons"),
+    ]
+
+    lineage_traced_results = []
+    lineage_traced_configurations = [
+        (larry_mono_configuration, "larry_mono"),
+        (larry_neu_configuration, "larry_neu"),
+        (larry_multilineage_configuration, "larry_multilineage"),
+        (larry_configuration, "larry"),
+    ]
+
     configurations = [
-        (simulated_configuration, "simulated"),
+        # (simulated_configuration, "simulated"),
     ]
 
     if not PYROVELOCITY_DATA_SUBSET:
-        configurations += [
-            (pancreas_configuration, "pancreas"),
-            (bonemarrow_configuration, "bonemarrow"),
-            (pbmc5k_configuration, "pbmc5k"),
-            (pbmc10k_configuration, "pbmc10k"),
-            (pbmc68k_configuration, "pbmc68k"),
-            (pons_configuration, "pons"),
-            (larry_configuration, "larry"),
-            (larry_neu_configuration, "larry_neu"),
-            (larry_mono_configuration, "larry_mono"),
-            (larry_multilineage_configuration, "larry_multilineage"),
-        ]
+        # configurations += stationary_configurations
+        configurations += developmental_configurations
+        # configurations += lineage_traced_configurations
 
-    for config, _ in configurations:
+    for config, data_set_name in configurations:
         result = map_model_configurations_over_data_set(
             download_dataset_args=config.download_dataset,
             preprocess_data_args=config.preprocess_data,
             train_model_configuration_1=config.training_configuration_1,
             train_model_configuration_2=config.training_configuration_2,
             postprocess_configuration=config.postprocess_configuration,
+            summarize_configuration=config.summarize_configuration,
             train_model_resource_requests=config.training_resources_requests,
             train_model_resource_limits=config.training_resources_limits,
             postprocessing_resource_requests=config.postprocessing_resources_requests,
@@ -619,10 +769,15 @@ def training_workflow(
             accelerator_type=config.accelerator_type,
             upload_results=config.upload_results,
         )
+        if "larry" in data_set_name:
+            lineage_traced_results.append(result)
         results.append(result)
 
     combine_all_metrics(results=results)
 
+    if len(lineage_traced_results) > 0:
+        combine_time_lineage_fate_correlation(results=lineage_traced_results)
+
     return results