Proliferation model (initial version) and improved mapping

.github/workflows/documentation.yml

@@ -14,7 +14,7 @@ jobs:
 
     - name: Install Sphinx & Dependencies
       run: |
-        pip install sphinx sphinx_markdown_builder sphinx_rtd_theme sphinx-argparse m2r pandas bio
+        pip install sphinx sphinx_markdown_builder sphinx_rtd_theme sphinx-argparse m2r pandas bio "docutils==0.20"
         sudo apt-get install python3-distutils
     - name: Build Documentation
       working-directory: docs

README.md

@@ -22,7 +22,7 @@
 2. [`profile`](https://pinellolab.github.io/crispr-bean/profile.html): Profile editing preferences of your editor.  
 3. [`qc`](https://pinellolab.github.io/crispr-bean/qc.html): Quality control report and filtering out / masking of aberrant sample and guides  
 4. [`filter`](https://pinellolab.github.io/crispr-bean/filter.html): Filter reporter alleles; essential for `tiling` mode that allows for all alleles generated from gRNA.
-5. [`run`](https://pinellolab.github.io/crispr-bean/run.html): Quantify targeted variants' effect sizes from screen data. **See more about the [model](https://pinellolab.github.io/crispr-bean/model.html) & [output](https://github.com/pinellolab/crispr-bean/tree/main/docs/example_run_output)**
+5. [`run`](https://pinellolab.github.io/crispr-bean/run.html): Quantify targeted variants' effect sizes from screen data. **See more about the [model](https://pinellolab.github.io/crispr-bean/model.html) & [output](https://pinellolab.github.io/crispr-bean/run.html#output)**
 * Screen data is saved as [`ReporterScreen` object](https://pinellolab.github.io/crispr-bean/reporterscreen.html) in the pipeline.
 BEAN stores mapped gRNA and allele counts in `ReporterScreen` object which is compatible with [AnnData](https://anndata.readthedocs.io/en/latest/index.html). 
 

bean/annotate/utils.py

@@ -450,6 +450,16 @@ def check_args(args):
         raise ValueError(
             "Invalid arguments: You should specify exactly one of --translate-fasta, --translate-fastas-csv, --translate-gene, translate-genes-list to translate alleles."
         )
+    if (
+        args.translate_fasta is not None
+        or args.translate_fastas_csv is not None
+        or args.translate_gene is not None
+        or args.translate_genes_list is not None
+    ) and not args.translate:
+        warn(
+            "fastq or gene files for translation provided without `--translate` flag. Setting `--translate` flag to True."
+        )
+        args.translate = True
     if args.translate_genes_list is not None:
         args.translate_genes_list = (
             pd.read_csv(args.translate_genes_list, header=None).values[:, 0].tolist()

bean/cli/count.py

@@ -70,8 +70,11 @@ def main(args):
         counter.screen.uns["allele_counts"] = counter.screen.uns["allele_counts"].loc[
             counter.screen.uns["allele_counts"].allele.map(str) != "", :
         ]
+        counter.screen.get_edit_from_allele("allele_counts", "allele")
         if match_target_pos:
             counter.screen.get_edit_mat_from_uns(target_base_edits, match_target_pos)
+        else:
+            counter.screen.get_edit_mat_from_uns(target_base_edits)
     counter.screen.write(f"{counter.output_dir}.h5ad")
     counter.screen.to_Excel(f"{counter.output_dir}.xlsx")
     info(f"Output written at:\n {counter.output_dir}.h5ad,\n {counter.output_dir}.xlsx")

bean/cli/count_samples.py

@@ -47,7 +47,9 @@ def count_sample(R1: str, R2: str, sample_id: str, args: argparse.Namespace):
     except KeyError as e:
         raise KeyError(args_dict["edited_base"]) from e
 
-    match_target_pos = args_dict["match_target_pos"]
+    match_target_pos = (
+        args_dict["match_target_pos"] if not args_dict["tiling"] else False
+    )
     if (
         "guide_start_seqs_tbl" in args_dict
         and args_dict["guide_start_seqs_tbl"] is not None
@@ -92,12 +94,15 @@ def count_sample(R1: str, R2: str, sample_id: str, args: argparse.Namespace):
         screen = counter.screen
         if screen.X.max() == 0:
             warn(f"Nothing counted for {sample_id}. Check your input.")
-        if counter.count_reporter_edits and match_target_pos:
+        if counter.count_reporter_edits:
             screen.uns["allele_counts"] = screen.uns["allele_counts"].loc[
                 screen.uns["allele_counts"].allele.map(str) != "", :
             ]
             screen.get_edit_from_allele("allele_counts", "allele")
-            screen.get_edit_mat_from_uns(target_base_edits, match_target_pos)
+            if match_target_pos:
+                screen.get_edit_mat_from_uns(target_base_edits, match_target_pos)
+            else:
+                screen.get_edit_mat_from_uns(target_base_edits)
         info(
             f"Done for {sample_id}. \n\
             Output written at {counter.output_dir}.h5ad"

bean/cli/run.py

@@ -85,8 +85,8 @@ def main(args, return_data=False):
     file_logger = logging.FileHandler(f"{prefix}/bean_run.log")
     file_logger.setLevel(logging.INFO)
     logging.getLogger().addHandler(file_logger)
+    info(f"Running: {' '.join(sys.argv[:])}")
     if args.cuda:
-        os.environ["CUDA_VISIBLE_DEVICES"] = "1"
         torch.set_default_tensor_type(torch.cuda.FloatTensor)
     else:
         torch.set_default_tensor_type(torch.FloatTensor)
@@ -96,6 +96,10 @@ def main(args, return_data=False):
 
     # Format bdata into data structure compatible with Pyro model
     bdata = prepare_bdata(bdata, args, warn, prefix)
+    negctrl_idx = np.where(
+        bdata.guides[args.negctrl_col].map(lambda s: s.lower())
+        == args.negctrl_col_value.lower()
+    )[0]
     ndata = DATACLASS_DICT[args.selection][model_label](
         screen=bdata,
         device=args.device,
@@ -115,20 +119,24 @@ def main(args, return_data=False):
         popt=args.popt,
         replicate_col=args.replicate_col,
         use_bcmatch=(not args.ignore_bcmatch),
+        negctrl_guide_idx=negctrl_idx,
     )
+    if args.save_raw:
+        pkl.dump(bdata, open(f"{prefix}/ndata.pkl", "wb"))
     guide_index = ndata.screen.guides.index.copy()
     assert len(guide_index) == bdata.n_obs, (len(guide_index), bdata.n_obs)
     if return_data:
         return ndata
     # Build variant dataframe
     adj_negctrl_idx = None
+    _control_condition = args.control_condition.split(",")[0]
     if args.library_design == "variant":
         if not args.uniform_edit:
             if "edit_rate" not in ndata.screen.guides.columns:
                 ndata.screen.get_edit_from_allele()
                 ndata.screen.get_edit_mat_from_uns(rel_pos_is_reporter=True)
                 ndata.screen.get_guide_edit_rate(
-                    unsorted_condition_label=args.control_condition
+                    unsorted_condition_label=_control_condition
                 )
         target_info_df = _get_guide_target_info(
             ndata.screen, args, cols_include=[args.negctrl_col]
@@ -139,6 +147,12 @@ def main(args, return_data=False):
                 == args.negctrl_col_value.lower()
             )[0]
     else:
+        if "edit_rate_norm" not in ndata.screen.guides.columns:
+            ndata.screen.get_edit_from_allele()
+            ndata.screen.get_edit_mat_from_uns(rel_pos_is_reporter=True)
+            ndata.screen.get_guide_edit_rate(
+                unsorted_condition_label=_control_condition
+            )
         if args.splice_site_path is not None:
             splice_site = pd.read_csv(args.splice_site_path).pos
         target_info_df = be.an.translate_allele.annotate_edit(
@@ -198,7 +212,6 @@ def main(args, return_data=False):
             ],
             axis=1,
         )
-
     # Add user-defined prior.
     if args.prior_params is not None:
         prior_params = _check_prior_params(args.prior_params, ndata)
@@ -210,13 +223,12 @@ def main(args, return_data=False):
         with open(f"{prefix}/{model_label}.result.pkl", "rb") as handle:
             param_history_dict = pkl.load(handle)
     else:
-        param_history_dict, save_dict = deepcopy(
-            run_inference(model, guide, ndata, num_steps=args.n_iter)
-        )
+        save_dict = dict()
         if args.fit_negctrl:
             negctrl_model, negctrl_guide = identify_negctrl_model_guide(
                 args, "X_bcmatch" in bdata.layers
             )
+            print(f"Using {negctrl_model} & {negctrl_guide}")
             negctrl_idx = np.where(
                 ndata.screen.guides[args.negctrl_col].map(lambda s: s.lower())
                 == args.negctrl_col_value.lower()
@@ -225,16 +237,36 @@ def main(args, return_data=False):
                 f"Using {len(negctrl_idx)} negative control elements to adjust phenotypic effect sizes..."
             )
             ndata_negctrl = ndata[negctrl_idx]
+            print(
+                f"ndata size factor: {ndata.size_factor}, {ndata_negctrl.size_factor}\esf"
+            )
+            if args.save_raw:
+                pkl.dump(ndata_negctrl, open(f"{prefix}/ndata_negctrl.pkl", "wb"))
             param_history_dict_negctrl, save_dict["negctrl"] = deepcopy(
                 run_inference(
                     negctrl_model, negctrl_guide, ndata_negctrl, num_steps=args.n_iter
                 )
             )
+            if args.selection == "survival":
+                print(
+                    f"Feeding mu_negctrl={param_history_dict_negctrl['mu_loc'], param_history_dict_negctrl['mu_scale']} into model..."
+                )
+                model = partial(
+                    model,
+                    mu_negctrl=(
+                        param_history_dict_negctrl["mu_loc"].detach().mean(),
+                        param_history_dict_negctrl["mu_scale"].detach().mean(),
+                    ),
+                )
         else:
             param_history_dict_negctrl = None
         save_dict["data"] = ndata
+        param_history_dict, save_dict_model = deepcopy(
+            run_inference(model, guide, ndata, num_steps=args.n_iter)
+        )
+        for k, v in save_dict_model.items():
+            save_dict[k] = v
     # Save results
-
     outfile_path = (
         f"{prefix}/bean_element[sgRNA]_result.{model_label}{args.result_suffix}.csv"
     )
@@ -266,5 +298,6 @@ def main(args, return_data=False):
         sample_covariates=(
             ndata.sample_covariates if hasattr(ndata, "sample_covariates") else None
         ),
+        is_survival_screen=(args.selection == "survival"),
     )
     info("Done!")

bean/framework/ReporterScreen.py

@@ -382,9 +382,9 @@ def get_edit_mat_from_uns(
             reporter_length = self.uns["reporter_length"]
         if "reproter_right_flank_length" in self.uns:
             reporter_right_flank_length = self.uns["reporter_right_flank_length"]
-        if edit_count_key not in self.uns or len(self.uns[edit_count_key]) == 0:
+        if edit_count_key not in self.uns:
             raise ValueError(
-                "Edit count isn't calculated. "
+                f"Edit count isn't calculated or not provided with specified key `{edit_count_key}`. "
                 + "Call .get_edit_from_allele(allele_count_key, allele_key)"
             )
         edits = self.uns[edit_count_key].copy()
@@ -410,7 +410,7 @@ def get_edit_mat_from_uns(
         edits["guide_start_pos"] = (
             reporter_length
             - reporter_right_flank_length
-            - guide_len[edits.guide_idx].reset_index(drop=True)
+            - guide_len.iloc[edits.guide_idx].reset_index(drop=True)
         )
         if not match_target_position:
             edits["rel_pos"] = edits.edit.map(lambda e: e.rel_pos)
@@ -434,12 +434,14 @@ def get_edit_mat_from_uns(
                 edits.target_pos_matches,
                 ["guide", "edit"] + self.samples.index.tolist(),
             ]
-
+        good_edits = good_edits.copy()
         good_guide_idx = guide_name_to_idx.loc[good_edits.guide, "index"].astype(int)
+        edit_mat = np.zeros(self.layers["edits"].shape)
         for gidx, eidx in zip(good_guide_idx, good_edits.index):
-            self.layers["edits"][gidx, :] += good_edits.loc[
+            edit_mat[gidx, :] = edit_mat[gidx, :] + good_edits.loc[
                 eidx, self.samples.index.tolist()
             ].astype(int)
+        self.layers["edits"] = edit_mat
         print("New edit matrix saved in .layers['edits']. Returning old edits.")
         return old_edits
 
@@ -505,17 +507,21 @@ def get_guide_edit_rate(
             prior_weight = 1
         n_edits = self.layers[edit_layer].copy()[:, bulk_idx].sum(axis=1)
         n_counts = self.layers[count_layer].copy()[:, bulk_idx].sum(axis=1)
-        edit_rate = (n_edits + prior_weight / 2) / (
-            (n_counts * num_targetable_sites) + prior_weight / 2
-        )
+        edit_rate = (n_edits + prior_weight / 2) / ((n_counts) + prior_weight / 2)
+        if normalize_by_editable_base:
+            edit_rate_norm = (n_edits + prior_weight / 2) / (
+                (n_counts * num_targetable_sites) + prior_weight / 2
+            )
         edit_rate[n_counts < bcmatch_thres] = np.nan
         if normalize_by_editable_base:
             print("normalize by editable counts")
-            edit_rate[num_targetable_sites == 0] = np.nan
+            edit_rate_norm[num_targetable_sites == 0] = np.nan
         if return_result:
             return edit_rate
         else:
             self.guides["edit_rate"] = edit_rate
+            if normalize_by_editable_base:
+                self.guides["edit_rate_norm"] = edit_rate_norm
             print(self.guides.edit_rate)
 
     def get_edit_rate(