v0.1

pyproject.toml

@@ -25,7 +25,7 @@ dependencies = [
     "spatialdata",
     "spatialdata-io",
     "spatialdata-plot",
-    "napari-spatialdata",
+    #"napari-spatialdata",
     "squidpy",
     #"torch==1.13.1",
 

src/scispy/pl/__init__.py

@@ -1,4 +1,13 @@
-from .basic import get_palette, plot_per_groups, plot_qc, plot_sdata, plot_shape_along_axis, plot_shapes
+from .basic import (
+    get_palette,
+    legend_without_duplicate_labels,
+    plot_multi_sdata,
+    plot_per_groups,
+    plot_qc,
+    plot_sdata,
+    plot_shape_along_axis,
+    plot_shapes,
+)
 
 __all__ = [
     "plot_shapes",
@@ -7,4 +16,6 @@
     "plot_qc",
     "plot_per_groups",
     "plot_sdata",
+    "plot_multi_sdata",
+    "legend_without_duplicate_labels",
 ]

src/scispy/pl/basic.py

@@ -15,6 +15,7 @@ def plot_shapes(
     shapes_lst: tuple = [],  # the shapes to plot
     label_obs_key: str = "celltype_spatial",
     shape_key: str = "arteries",
+    color_dict: tuple = [],
     target_coordinates: str = "microns",
     figsize: tuple = (12, 6),
     save: bool = False,
@@ -33,6 +34,8 @@ def plot_shapes(
         label_key in sdata.table.obs to consider
     shape_key
         SpatialData shape element to consider
+    color_dict
+        dictionary of colors to use
     target_coordinates
         target_coordinates system of sdata object
     figsize
@@ -43,9 +46,6 @@ def plot_shapes(
     """
     region_key = sdata.table.uns["spatialdata_attrs"]["region"]
 
-    if group_lst is None:
-        group_lst = sdata.table.obs[label_obs_key].unique().tolist()
-
     fig, axs = plt.subplots(ncols=len(shapes_lst), nrows=1, figsize=figsize)
     for i in range(0, len(shapes_lst)):
         poly = sdata[shape_key][sdata[shape_key].name == shapes_lst[i]].geometry.item()
@@ -60,12 +60,16 @@ def plot_shapes(
         )
 
         sdata2.pl.render_images().pl.show(ax=axs[i])
-        sdata2.pl.render_shapes(elements=shape_key, outline=True, fill_alpha=0.25, outline_color="red").pl.show(
-            ax=axs[i]
-        )
-        # sdata2.pl.render_shapes(elements=region_key, color=label_obs_key, groups=group_lst).pl.show(ax=axs[i])
-        # sdata2.pp.get_elements([region_key]).pl.render_shapes(color=label_obs_key, groups=group_lst).pl.show(ax=axs[i])
-        sdata2.pl.render_shapes(elements=region_key).pl.show(ax=axs[i])
+        if group_lst is None:
+            group_lst = sdata.table.obs[label_obs_key].unique().tolist()
+
+        if color_dict is not None:
+            mypal = [color_dict[x] for x in group_lst]
+            sdata2.pl.render_shapes(elements=region_key, color=label_obs_key, groups=group_lst, palette=mypal).pl.show(
+                ax=axs[i]
+            )
+        else:
+            sdata2.pl.render_shapes(elements=region_key, color=label_obs_key, groups=group_lst).pl.show(ax=axs[i])
 
         axs[i].set_title(shapes_lst[i])
         # if(i < len(shapes_lst)):
@@ -78,9 +82,10 @@ def plot_shape_along_axis(
     sdata: sd.SpatialData,
     group_lst: tuple = [],  # the cell types to consider
     gene_lst: tuple = [],  # the genes to consider
-    label_obs_key: str = "celltype_spatial",
+    label_obs_key: str = "celltype",
     sdata_group_key: str = "celltypes",
     target_coordinates: str = "microns",
+    color_dict: tuple = [],
     scale_expr: bool = False,
     bin_size: int = 50,
     rotation_angle: int = 0,
@@ -126,7 +131,8 @@ def plot_shape_along_axis(
     #    images=True,
     # )
 
-    sdata2 = sdata_rotate(sdata, rotation_angle, target_coordinates)
+    sdata_rotate(sdata, rotation_angle)
+    sdata2 = sdata.transform_to_coordinate_system(target_coordinates)
 
     # get elements key, probably need to do better here in the futur !!
     dataset_id = sdata2.table.obs.dataset_id.unique().tolist()[0]
@@ -146,6 +152,9 @@ def plot_shape_along_axis(
     cats = sdata.table.obs[label_obs_key].cat.categories.tolist()
     colors = list(sdata.table.uns[label_obs_key + "_colors"])
     mypal = dict(zip(cats, colors))
+    if color_dict is not None:
+        mypal = color_dict
+
     mypal = {x: mypal[x] for x in group_lst}
 
     # compute values dataframes
@@ -161,14 +170,14 @@ def plot_shape_along_axis(
             }
             vals = pd.concat([vals, pd.DataFrame([new_row])], ignore_index=True)
 
-    valct = pd.DataFrame({"microns": [], "count": [], "cell_type": []})
+    valct = pd.DataFrame({"microns": [], "count": [], "celltype": []})
     for ct in range(0, len(group_lst)):
         df2 = df_celltypes[df_celltypes["ct"] == group_lst[ct]]
         for i in range(0, step_number):
             new_row = {
                 "microns": (x_min + (i + 0.5) * bin_size),
                 "count": df2[(df2.x > (x_min + i * bin_size)) & (df2.x < (x_min + (i + 1) * bin_size))].shape[0],
-                "cell_type": group_lst[ct],
+                "celltype": group_lst[ct],
             }
             valct = pd.concat([valct, pd.DataFrame([new_row])], ignore_index=True)
 
@@ -183,7 +192,7 @@ def plot_shape_along_axis(
         elements=sdata_polygon_key, color=label_obs_key, palette=list(mypal.values()), groups=group_lst
     ).pl.show(ax=ax1)
 
-    sns.lineplot(data=valct, x="microns", y="count", hue="cell_type", linewidth=0.9, palette=mypal, ax=ax2)
+    sns.lineplot(data=valct, x="microns", y="count", hue="celltype", linewidth=0.9, palette=mypal, ax=ax2)
     ax2.get_legend().remove()
 
     if scale_expr is True:
@@ -217,6 +226,7 @@ def plot_shape_along_axis(
 def plot_sdata(
     sdata: sd.SpatialData,
     color_key: str = "celltype",
+    mypal: tuple = None,
 ):
     """Plot sdata object (i.e. embedding and polygons). This should always works if well synchronized sdata object
 
@@ -227,13 +237,57 @@ def plot_sdata(
     color_key
         color key from .table.obs
     """
-    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 6))
-    sc.pl.embedding(sdata.table, "umap", color=color_key, ax=ax1, show=False)
-    ax1.get_legend().remove()
-    if sdata.contains_element(sdata.table.uns["spatialdata_attrs"]["region"]):
-        sdata.pl.render_shapes(elements=sdata.table.uns["spatialdata_attrs"]["region"], color=color_key).pl.show(ax=ax2)
+    fig, ax = plt.subplots(figsize=(12, 6))
+    if mypal is not None:
+        sdata.pl.render_shapes(
+            elements=sdata.table.uns["spatialdata_attrs"]["region"],
+            color="celltype",
+            palette=list(mypal.values()),
+            groups=list(mypal.keys()),
+        ).pl.show(ax=ax)
     else:
-        sq.pl.spatial_scatter(sdata.table, color=color_key, shape=None, size=1, ax=ax2)
+        sdata.pl.render_shapes(elements=sdata.table.uns["spatialdata_attrs"]["region"], color="celltype").pl.show(ax=ax)
+
+    ax.grid(which="both", linestyle="dashed")
+    ax.minorticks_on()
+    ax.tick_params(which="minor", bottom=False, left=False)
+
+    legend_without_duplicate_labels(ax)
+    plt.tight_layout()
+
+
+def plot_multi_sdata(
+    sdata: sd.SpatialData,
+    color_key: str = "celltype",
+):
+    """Plot sdata object (i.e. embedding and polygons). This should always works if well synchronized sdata object
+
+    Parameters
+    ----------
+    sdata
+        SpatialData object.
+    color_key
+        color key from .table.obs
+    """
+    sdata.table.obs[color_key] = sdata.table.obs[color_key].cat.remove_unused_categories()
+    sdata.table.uns.pop(color_key + "_colors", None)
+
+    fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(20, 6))
+
+    sns.scatterplot(x="center_x", y="center_y", data=sdata.table.obs, s=1, hue=color_key, ax=ax1)
+    ax1.axis("equal")
+    ax1.get_legend().remove()
+    ax1.set_title("anndata.obs coordinates")
+    ax1.invert_yaxis()
+
+    sdata.pl.render_shapes(elements=sdata.table.uns["spatialdata_attrs"]["region"], color=color_key).pl.show(ax=ax2)
+    ax2.get_legend().remove()
+    ax2.set_title("spatialdata polygons")
+
+    sq.pl.spatial_scatter(sdata.table, color=color_key, shape=None, size=1, ax=ax3)
+    # ax2.get_legend().remove()
+    ax3.set_title("squidpy spatial")
+
     plt.tight_layout()
 
 
@@ -292,32 +346,41 @@ def get_palette(color_key: str) -> dict:
             "AlvFibro": "#d58936",
             "MyoFibro": "#69140e",
         }
-    elif color_key == "celltype2":
+    elif color_key == "celltype paolo":
+        # paolo
         palette = {
-            # paolo
-            "cartilage": "#005f73",
-            "myeloid": "#0a9396",
-            "skeletal muscle": "#94d2bd",
-            "stromal": "#e9d8a6",
-            "endothelial": "#64a6bd",
-            "lymphatic": "#90a8c3",
-            "pericyte": "#d7b9d5",
-            "vascular": "#f4cae0",
-            "basal": "#003049",
-            "ciliated": "#d62828",
-            "deuterosomal": "#f77f00",
-            "secretory": "#fcbf49",
-            "excitatory neuron": "#797d62",
-            "neuron": "#4a4e69",
-            "glia": "#9b9b7a",
-            "olfactory sensory neuron": "#d9ae94",
-            "satelite": "#ffcb69",
-            "sustentacular": "#b58463",
-            # per compartment
-            #'cartilage nasal': '#fb8500',
-            #'vascular lymphatic': '#ef233c',
-            #'olfactory epithelium': '#344966',
-            #'migrating neuron': '#606c38',
+            "Cartilages": "#9DAF07",
+            "Stromal0": "#99D6A9",
+            "Stromal1": "#1B8F76",
+            "Stromal2": "#BBD870",
+            "Stromal3": "#4CAD4C",
+            "Lymphatic EC": "#F78896",
+            "Vascular EC": "#E788C2",
+            "Pericytes": "#0B4B19",
+            "Satellites": "#9CBBA6",
+            "Skeletal muscle": "#9EB3DD",  # 3868A6
+            "Glia progenitors": "#E3D9AC",
+            "Olfactory ensheathing glia": "#9ecae1",  # AE8C0D
+            "Schwann cells": "#FAF9BA",  # C0AC51
+            "Neurons ALK+": "#7D1C53",
+            "Respiratory HBCs": "#D50000",
+            "Olfactory HBCs": "#1A237E",
+            "Keratinocytes": "#0000FF",
+            "Duct/MUC": "#BC9EDD",
+            "Multiciliated": "#FAF204",
+            "Deuterosomal": "#2EECDB",
+            "Sustentaculars": "#ff7f00",  # E17C10
+            "GBCs": "#706fd3",
+            "Early OSNs": "#5AC2BF",  # 04C9FA
+            "Migratory neurons": "#457b9d",  ###
+            "VNO neurons": "#0D16C8",
+            "Neuron progenitors": "#61559E",  # 6A0B78
+            "Excitatory neurons": "#95819F",
+            "Inhibitory neurons": "#800EF1",
+            "GnRH neurons": "#E00EF1",
+            "Myeloid": "#7E909D",  # CB7647
+            "Microglia": "#91BFB7",
+            "junk neurons": "#f1faee",
         }
     elif color_key == "leiden":  # default is 40 colors returned
         l = list(range(0, 39, 1))
@@ -404,3 +467,19 @@ def plot_per_groups(adata, clust_key, size=60, is_spatial=False, frameon=False,
             legend_loc=legend_loc,
             **kwargs,
         )
+
+
+def legend_without_duplicate_labels(figure):
+    """Remove duplicated labels in figure legend
+
+    Parameters
+    ----------
+    figure
+        matplotlib figure.
+    """
+    # code from here
+    # https://stackoverflow.com/questions/19385639/duplicate-items-in-legend-in-matplotlib
+
+    handles, labels = plt.gca().get_legend_handles_labels()
+    by_label = dict(zip(labels, handles))
+    figure.legend(by_label.values(), by_label.keys(), loc="center left", bbox_to_anchor=(1.05, 0.5), fontsize=6, ncol=1)