Merge branch 'master' of github.com:calico/solo

README.md

@@ -17,40 +17,42 @@ If you don't have conda follow the instructions here: https://docs.conda.io/proj
 
 ### How to solo
 ```
-usage: solo [-h] [-d DOUBLET_DEPTH] [-g] [-o OUT_DIR] [-r DOUBLET_RATIO]
-            [-s SEED] [-k KNOWN_DOUBLETS] [-t {multinomial,average,sum}]
-            [-e EXPECTED_NUMBER_OF_DOUBLETS] [-p]
-            model_json_file data_file
-
-positional arguments:
-  model_json_file       json file to pass VAE parameters
-  data_file             path to h5ad, loom or 10x directory containing cell by
-                        genes counts
+usage: solo [-h] -j MODEL_JSON_FILE -d DATA_PATH
+            [--set-reproducible-seed REPRODUCIBLE_SEED]
+            [--doublet-depth DOUBLET_DEPTH] [-g] [-a] [-o OUT_DIR]
+            [-r DOUBLET_RATIO] [-s SEED] [-e EXPECTED_NUMBER_OF_DOUBLETS] [-p]
+            [-recalibrate_scores] [--version]
 
 optional arguments:
   -h, --help            show this help message and exit
-  -d DOUBLET_DEPTH      Depth multiplier for a doublet relative to the average
+  -j MODEL_JSON_FILE    json file to pass VAE parameters (default: None)
+  -d DATA_PATH          path to h5ad, loom, or 10x mtx dir cell by genes
+                        counts (default: None)
+  --set-reproducible-seed REPRODUCIBLE_SEED
+                        Reproducible seed, give an int to set seed (default:
+                        None)
+  --doublet-depth DOUBLET_DEPTH
+                        Depth multiplier for a doublet relative to the average
                         of its constituents (default: 2.0)
   -g                    Run on GPU (default: True)
+  -a                    output modified anndata object with solo scores Only
+                        works for anndata (default: False)
   -o OUT_DIR
-  -r DOUBLET_RATIO      Ratio of doublets to true cells (default: 2.0)
+  -r DOUBLET_RATIO      Ratio of doublets to true cells (default: 2)
   -s SEED               Path to previous solo output directory. Seed VAE
                         models with previously trained solo model. Directory
                         structure is assumed to be the same as solo output
                         directory structure. should at least have a vae.pt a
                         pickled object of vae weights and a latent.npy an
                         np.ndarray of the latents of your cells. (default:
                         None)
-  -k KNOWN_DOUBLETS     Experimentally defined doublets tsv file. Should be a
-                        single column of True/False. True indicates the cell
-                        is a doublet. No header. (default: None)
-  -t {multinomial,average,sum}
-                        Please enter multinomial, average, or sum (default:
-                        multinomial)
   -e EXPECTED_NUMBER_OF_DOUBLETS
                         Experimentally expected number of doublets (default:
                         None)
-  -p                    Plot outputs (default: True)
+  -p                    Plot outputs for solo (default: False)
+  -recalibrate_scores   Recalibrate doublet scores (not recommended anymore)
+                        (default: False)
+  --version             Get version of solo-sc (default: False)
 ```
 
 Warning: If you are going directly from cellranger 10x output you may want to manually inspect your data prior to running solo.
@@ -71,11 +73,11 @@ model_json example:
 
 Outputs:
 * `is_doublet.npy`  np boolean array, true if a cell is a doublet, differs from `preds.npy` if `-e expected_number_of_doublets` parameter was used
-* `vae.pt` scVI weights for vae
-* `classifier.pt` scVI weights for classifier
+* `vae` scVI directory for vae
+* `classifier.pt` scVI directory for classifier
 * `latent.npy` latent embedding for each cell             
 * `preds.npy` doublet predictions
-* `softmax_scores.npy` updated softmax of doublet scores (see paper)
+* `softmax_scores.npy` updated softmax of doublet scores (see paper), same as `no_update_softmax_scores.npy` now
 * `no_update_softmax_scores.npy` raw softmax of doublet scores
 
 * `logit_scores.npy`	logit of doublet scores