From a95fd4fedde20b38ec8b4fea2bcc6d814d010e25 Mon Sep 17 00:00:00 2001 From: njbernstein Date: Wed, 30 Jun 2021 10:44:43 -0700 Subject: [PATCH 1/2] Update README.md --- README.md | 38 ++++++++++++++++++++------------------ 1 file changed, 20 insertions(+), 18 deletions(-) diff --git a/README.md b/README.md index 01150da..79af4b1 100644 --- a/README.md +++ b/README.md @@ -17,23 +17,28 @@ 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 @@ -41,16 +46,13 @@ optional arguments: 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. From 983205c7206425a5932b02c78b4a54a86de84586 Mon Sep 17 00:00:00 2001 From: njbernstein Date: Wed, 30 Jun 2021 10:46:17 -0700 Subject: [PATCH 2/2] Update README.md --- README.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 79af4b1..2546ef7 100644 --- a/README.md +++ b/README.md @@ -73,11 +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