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README.md

@@ -1,27 +1,123 @@
 ---
-license: cc-by-4.0
 library_name: scvi-tools
+license: cc-by-4.0
 tags:
 - biology
 - genomics
 - single-cell
 - model_cls_name:SCANVI
-- scvi_version:1.1.0
-- anndata_version:0.10.3
+- scvi_version:1.2.0
+- anndata_version:0.11.1
 - modality:rna
-- tissue:Skin
+- tissue:various
 - annotated:True
 ---
 
-# Description
 
-Tabula Sapiens is a benchmark, first-draft human cell atlas of nearly 500,000 cells from 24 organs of 15 normal human subjects.
+ScANVI is a variational inference model for single-cell RNA-seq data that can learn an underlying
+latent space, integrate technical batches and impute dropouts.
+In addition, to scVI, ScANVI is a semi-supervised model that can leverage labeled data to learn a
+cell-type classifier in the latent space and afterward predict cell types of new data.
+The learned low-dimensional latent representation of the data can be used for visualization and
+clustering.
+
+scANVI takes as input a scRNA-seq gene expression matrix with cells and genes as well as a
+cell-type annotation for a subset of cells.
+We provide an extensive [user guide](https://docs.scvi-tools.org/en/1.2.0/user_guide/models/scanvi.html).
+
+- See our original manuscript for further details of the model:
+[scANVI manuscript](https://www.embopress.org/doi/full/10.15252/msb.20209620).
+- See our manuscript on [scvi-hub](https://www.biorxiv.org/content/10.1101/2024.03.01.582887v2)
+how to leverage pre-trained models.
 
-# Model properties
+This model can be used for fine tuning on new data using our Arches framework:
+[Arches tutorial](https://docs.scvi-tools.org/en/1.0.0/tutorials/notebooks/scarches_scvi_tools.html).
 
-Many model properties are in the model tags. Some more are listed below.
 
-**model_init_params**:
+# Model Description
+
+Tabula Sapiens is a benchmark, first-draft human cell atlas of nearly 500,000 cells from 24 organs of 15 normal human subjects.
+
+# Metrics
+
+We provide here key performance metrics for the uploaded model, if provided by the data uploader.
+
+<details>
+<summary><strong>Coefficient of variation</strong></summary>
+
+The cell-wise coefficient of variation summarizes how well variation between different cells is
+preserved by the generated model expression. Below a squared Pearson correlation coefficient of 0.4
+, we would recommend not to use generated data for downstream analysis, while the generated latent
+space might still be useful for analysis.
+
+**Cell-wise Coefficient of Variation**:
+
+| Metric                  | Training Value | Validation Value |
+|-------------------------|----------------|------------------|
+| Mean Absolute Error | 2.33  | 2.31           |
+| Pearson Correlation | 0.63  | 0.61  |
+| Spearman Correlation | 0.61 | 0.59  |
+| R² (R-Squared) | 0.10  | 0.11      |
+
+The gene-wise coefficient of variation summarizes how well variation between different genes is
+preserved by the generated model expression. This value is usually quite high.
+
+**Gene-wise Coefficient of Variation**:
+
+| Metric                  | Training Value |
+|-------------------------|----------------|
+| Mean Absolute Error | 13.92   |
+| Pearson Correlation | 0.56  |
+| Spearman Correlation | 0.61 |
+| R² (R-Squared) | -1.21  |
+
+</details>
+
+<details>
+<summary><strong>Differential expression metric</strong></summary>
+
+The differential expression metric provides a summary of the differential expression analysis
+between cell types or input clusters. We provide here the F1-score, Pearson Correlation
+Coefficient of Log-Foldchanges, Spearman Correlation Coefficient, and Area Under the Precision
+Recall Curve (AUPRC) for the differential expression analysis using Wilcoxon Rank Sum test for each
+cell-type.
+
+**Differential expression**:
+
+| Index | gene_f1 | lfc_mae | lfc_pearson | lfc_spearman | roc_auc | pr_auc | n_cells |
+| --- | --- | --- | --- | --- | --- | --- | --- |
+| stromal cell | 0.90 | 1.29 | 0.70 | 0.95 | 0.46 | 0.90 | 2943.00 |
+| endothelial cell | 0.84 | 1.70 | 0.66 | 0.92 | 0.36 | 0.85 | 1309.00 |
+| CD8-positive, alpha-beta memory T cell | 0.88 | 3.52 | 0.61 | 0.77 | 0.23 | 0.76 | 857.00 |
+| macrophage | 0.87 | 2.46 | 0.61 | 0.84 | 0.28 | 0.84 | 846.00 |
+| mast cell | 0.90 | 3.31 | 0.61 | 0.79 | 0.20 | 0.75 | 645.00 |
+| mature NK T cell | 0.84 | 3.97 | 0.58 | 0.74 | 0.24 | 0.75 | 605.00 |
+| T cell | 0.91 | 4.45 | 0.56 | 0.70 | 0.22 | 0.73 | 519.00 |
+| muscle cell | 0.91 | 3.40 | 0.61 | 0.79 | 0.36 | 0.79 | 437.00 |
+| CD4-positive, alpha-beta memory T cell | 0.83 | 4.39 | 0.60 | 0.69 | 0.23 | 0.71 | 339.00 |
+| CD1c-positive myeloid dendritic cell | 0.81 | 4.54 | 0.61 | 0.72 | 0.29 | 0.82 | 165.00 |
+| CD8-positive, alpha-beta cytotoxic T cell | 0.80 | 5.81 | 0.52 | 0.54 | 0.31 | 0.71 | 129.00 |
+| regulatory T cell | 0.85 | 5.06 | 0.60 | 0.63 | 0.30 | 0.77 | 126.00 |
+| epithelial cell | 0.69 | 4.50 | 0.61 | 0.72 | 0.35 | 0.71 | 106.00 |
+| naive thymus-derived CD8-positive, alpha-beta T cell | 0.84 | 5.45 | 0.57 | 0.55 | 0.28 | 0.75 | 88.00 |
+| naive thymus-derived CD4-positive, alpha-beta T cell | 0.81 | 5.32 | 0.56 | 0.56 | 0.28 | 0.71 | 71.00 |
+| melanocyte | 0.71 | 6.10 | 0.53 | 0.57 | 0.38 | 0.73 | 64.00 |
+| CD4-positive helper T cell | 0.78 | 5.73 | 0.54 | 0.54 | 0.30 | 0.73 | 59.00 |
+| Langerhans cell | 0.70 | 5.94 | 0.54 | 0.56 | 0.37 | 0.75 | 36.00 |
+| plasma cell | 0.53 | 7.21 | 0.46 | 0.45 | 0.30 | 0.73 | 24.00 |
+| CD141-positive myeloid dendritic cell | 0.64 | 6.55 | 0.52 | 0.52 | 0.28 | 0.75 | 17.00 |
+| smooth muscle cell | 0.60 | 5.75 | 0.53 | 0.51 | 0.38 | 0.69 | 13.00 |
+
+</details>
+
+# Model Properties
+
+We provide here key parameters used to setup and train the model.
+
+<details>
+<summary><strong>Model Parameters</strong></summary>
+
+These provide the settings to setup the original model:
 ```json
 {
     "n_hidden": 128,
@@ -38,7 +134,12 @@ Many model properties are in the model tags. Some more are listed below.
 }
 ```
 
-**model_setup_anndata_args**:
+</details>
+
+<details>
+<summary><strong>Setup Data Arguments</strong></summary>
+
+Arguments passed to setup_anndata of the original model:
 ```json
 {
     "labels_key": "cell_ontology_class",
@@ -47,11 +148,34 @@ Many model properties are in the model tags. Some more are listed below.
     "batch_key": "donor_assay",
     "size_factor_key": null,
     "categorical_covariate_keys": null,
-    "continuous_covariate_keys": null
+    "continuous_covariate_keys": null,
+    "use_minified": false
 }
 ```
 
-**model_summary_stats**:
+</details>
+
+<details>
+<summary><strong>Data Registry</strong></summary>
+
+Registry elements for AnnData manager:
+|   Registry Key    |         scvi-tools Location          |
+|-------------------|--------------------------------------|
+|         X         |               adata.X                |
+|       batch       |       adata.obs['_scvi_batch']       |
+|      labels       |      adata.obs['_scvi_labels']       |
+|    latent_qzm     |   adata.obsm['scanvi_latent_qzm']    |
+|    latent_qzv     |   adata.obsm['scanvi_latent_qzv']    |
+|    minify_type    | adata.uns['_scvi_adata_minify_type'] |
+| observed_lib_size |    adata.obs['observed_lib_size']    |
+
+- **Data is Minified**: False
+
+</details>
+
+<details>
+<summary><strong>Summary Statistics</strong></summary>
+
 |     Summary Stat Key     | Value |
 |--------------------------|-------|
 |         n_batch          |   3   |
@@ -61,40 +185,27 @@ Many model properties are in the model tags. Some more are listed below.
 |         n_labels         |  22   |
 |       n_latent_qzm       |  20   |
 |       n_latent_qzv       |  20   |
-|          n_vars          | 4000  |
-
-**model_data_registry**:
-|   Registry Key    |          scvi-tools Location           |
-|-------------------|----------------------------------------|
-|         X         |                adata.X                 |
-|       batch       |        adata.obs['_scvi_batch']        |
-|      labels       |       adata.obs['_scvi_labels']        |
-|    latent_qzm     |    adata.obsm['_scanvi_latent_qzm']    |
-|    latent_qzv     |    adata.obsm['_scanvi_latent_qzv']    |
-|    minify_type    |  adata.uns['_scvi_adata_minify_type']  |
-| observed_lib_size | adata.obs['_scanvi_observed_lib_size'] |
-
-**model_parent_module**: scvi.model
+|          n_vars          | 3000  |
 
-**data_is_minified**: True
+</details>
 
-# Training data
 
-This is an optional link to where the training data is stored if it is too large
-to host on the huggingface Model hub.
+<details>
+<summary><strong>Training</strong></summary>
 
 <!-- If your model is not uploaded with any data (e.g., minified data) on the Model Hub, then make
-sure to provide this field if you want users to be able to access your training data. See the scvi-tools
-documentation for details. -->
+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**: Not provided by uploader
 
-Training data url: https://zenodo.org/records/7608635/files/Skin_training_data.h5ad
+If provided by the original uploader, for those interested in understanding or replicating the
+training process, the code is available at the link below.
 
-# Training code
+**Training Code URL**: https://github.com/YosefLab/scvi-hub-models/blob/main/src/scvi_hub_models/TS_train_all_tissues.ipynb
 
-This is an optional link to the code used to train the model.
+</details>
 
-Training code url: N/A
 
 # References
 
-The Tabula Sapiens Consortium. The Tabula Sapiens: A multiple-organ, single-cell transcriptomic atlas of humans. Science, May 2022. doi:10.1126/science.abl4896
+The Tabula Sapiens Consortium. The Tabula Sapiens: A multiple-organ, single-cell transcriptomic atlas of humans. Science, May 2022. doi:10.1126/science.abl4896