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README.md

@@ -6,18 +6,13 @@ tags:
 - genomics
 - single-cell
 - model_cls_name:SCANVI
-- scvi_version:0.20.3
-- anndata_version:0.8.0
-- modality:rna
-- tissue:nose
-- tissue:respiratory airway
-- tissue:lung parenchyma
-- annotated:True
+- scvi_version:1.1.0
+- anndata_version:0.10.3
 ---
 
 # Description
 
-The first integrated, universal transcriptomic reference of the human lung on the single-cell level. For more details, see https: //github.com/LungCellAtlas/HLCA.
+To be added...
 
 # Model properties
 
@@ -32,6 +27,7 @@ Many model properties are in the model tags. Some more are listed below.
     "dropout_rate": 0.1,
     "dispersion": "gene",
     "gene_likelihood": "nb",
+    "linear_classifier": false,
     "encode_covariates": true,
     "deeply_inject_covariates": false,
     "use_layer_norm": "both",
@@ -56,7 +52,7 @@ Many model properties are in the model tags. Some more are listed below.
 |     Summary Stat Key     | Value  |
 |--------------------------|--------|
 |         n_batch          |   14   |
-|         n_cells          | 584884 |
+|         n_cells          | 584944 |
 | n_extra_categorical_covs |   0    |
 | n_extra_continuous_covs  |   0    |
 |         n_labels         |   29   |
@@ -88,14 +84,14 @@ to host on the huggingface Model hub.
 sure to provide this field if you want users to be able to access your training data. See the scvi-tools
 documentation for details. -->
 
-Training data url: https://cellxgene.cziscience.com/e/066943a2-fdac-4b29-b348-40cede398e4e.cxg/
+Training data url: N/A
 
 # Training code
 
 This is an optional link to the code used to train the model.
 
-Training code url: https://github.com/LungCellAtlas/HLCA_reproducibility
+Training code url: N/A
 
 # References
 
-An integrated cell atlas of the human lung in health and disease. L Sikkema, D Strobl, L Zappia, E Madissoon, NS Markov, L Zaragosi, M Ansari, M Arguel, L Apperloo, C Bécavin, M Berg, E Chichelnitskiy, M Chung, A Collin, ACA Gay, B Hooshiar Kashani, M Jain, T Kapellos, TM Kole, C Mayr, M von Papen, L Peter, C Ramírez-Suástegui, J Schniering, C Taylor, T Walzthoeni, C Xu, LT Bui, C de Donno, L Dony, M Guo, AJ Gutierrez, L Heumos, N Huang, I Ibarra, N Jackson, P Kadur Lakshminarasimha Murthy, M Lotfollahi, T Tabib, C Talavera-Lopez, K Travaglini, A Wilbrey-Clark, KB Worlock, M Yoshida, Lung Biological Network Consortium, T Desai, O Eickelberg, C Falk, N Kaminski, M Krasnow, R Lafyatis, M Nikolíc, J Powell, J Rajagopal, O Rozenblatt-Rosen, MA Seibold, D Sheppard, D Shepherd, SA Teichmann, A Tsankov, J Whitsett, Y Xu, NE Banovich, P Barbry, TE Duong, KB Meyer, JA Kropski, D Pe’er, HB Schiller, PR Tata, JL Schultze, AV Misharin, MC Nawijn, MD Luecken, F Theis. bioRxiv 2022.03.10.483747; doi: https: //doi.org/10.1101/2022.03.10.483747
+To be added...