add negctrl as separate arg

README.md

@@ -12,7 +12,7 @@
 ## Overview
 `bean` supports end-to-end analysis of pooled sorting screens, with or without reporter.  
 
-<img src="imgs/dag_bean_v2.svg" alt="dag_bean_v2.svg" width="700"/>  
+<img src="imgs/dag_bean_v2.svg" alt="dag_bean_v2.svg" height="500"/>  
 
 `bean` subcommands include the following:
 1. [`count`](https://pinellolab.github.io/crispr-bean/count.html), [`count-samples`](https://pinellolab.github.io/crispr-bean/count_samples.html): Base-editing-aware **mapping** of guide, optionally with reporter from `.fastq` files.
@@ -24,7 +24,7 @@
 * Screen data is saved as *ReporterScreen* object 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). See [Data Structure](#data-structure) section for more information.
 ## Installation 
-First install [PyT}orch](https://pytorch.org/get-started/).
+First install [PyTorch](https://pytorch.org/get-started/).
 Then download from PyPI:
 ```
 pip install crispr-bean[model]

bean/cli/run.py

@@ -167,7 +167,7 @@ def main(args):
                 f"Using {len(negctrl_idx)} negative control elements to adjust phenotypic effect sizes..."
             )
             ndata_negctrl = ndata[negctrl_idx]
-            param_history_dict["negctrl"], save_dict["negctrl"] = deepcopy(
+            param_history_dict_negctrl, save_dict["negctrl"] = deepcopy(
                 run_inference(
                     negctrl_model, negctrl_guide, ndata_negctrl, num_steps=args.n_iter
                 )
@@ -188,6 +188,7 @@ def main(args):
     write_result_table(
         target_info_df,
         param_history_dict,
+        negctrl_params=param_history_dict_negctrl,
         model_label=model_label,
         prefix=f"{prefix}/",
         suffix=args.result_suffix,

bean/model/readwrite.py

@@ -52,6 +52,7 @@ def write_result_table(
     model_label: str,
     prefix: str = "",
     suffix: str = "",
+    negctrl_params=None,
     write_fitted_eff: bool = True,
     adjust_confidence_by_negative_control: bool = True,
     adjust_confidence_negatives: np.ndarray = None,
@@ -100,17 +101,11 @@ def write_result_table(
             )
 
     fit_df = pd.DataFrame(param_dict)
-    if "negctrl" in param_hist_dict.keys():
+    if negctrl_params is not None:
         print("Normalizing with common negative control distribution")
-        mu0 = param_hist_dict["negctrl"]["params"]["mu_loc"].detach().cpu().numpy()
+        mu0 = negctrl_params["params"]["mu_loc"].detach().cpu().numpy()
         if sd_is_fitted:
-            sd0 = (
-                param_hist_dict["negctrl"]["params"]["sd_loc"]
-                .detach()
-                .exp()
-                .cpu()
-                .numpy()
-            )
+            sd0 = negctrl_params["params"]["sd_loc"].detach().exp().cpu().numpy()
         else:
             sd0 = 1.0
         print(f"Fitted mu0={mu0}" + (f", sd0={sd0}." if sd_is_fitted else ""))