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README.md

@@ -1,27 +1,120 @@
 ---
-license: cc-by-4.0
 library_name: scvi-tools
+license: cc-by-4.0
 tags:
 - biology
 - genomics
 - single-cell
 - model_cls_name:CondSCVI
-- scvi_version:1.1.0
-- anndata_version:0.10.3
+- scvi_version:1.2.0
+- anndata_version:0.11.1
 - modality:rna
-- tissue:Thymus
+- 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.
+CondSCVI is a variational inference model for single-cell RNA-seq data that can learn an underlying
+latent space. The predictions of the model are meant to be afterward
+used for deconvolution of a second spatial transcriptomics dataset in DestVI. DestVI predicts the
+cell-type proportions as well as cell type-specific activation state
+in the spatial data.
 
-# Model properties
+CondSCVI takes as input a scRNA-seq gene expression matrix with cells and genes as well as a
+cell-type annotation for all cells.
+We provide an extensive [user guide](https://docs.scvi-tools.org/en/1.2.0/user_guide/models/destvi.html)
+for DestVI including a description of CondSCVI.
 
-Many model properties are in the model tags. Some more are listed below.
+- See our original manuscript for further details of the model:
+[DestVI manuscript](https://www.nature.com/articles/s41587-022-01272-8).
+- See our manuscript on [scvi-hub](https://www.biorxiv.org/content/10.1101/2024.03.01.582887v2)
+how to leverage pre-trained models.
 
-**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 | 5.86  | 5.76           |
+| Pearson Correlation | 0.15  | 0.16  |
+| Spearman Correlation | 0.42 | 0.43  |
+| R² (R-Squared) | -1.15  | -1.12      |
+
+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 | 4.21   |
+| Pearson Correlation | 0.83  |
+| Spearman Correlation | 0.97 |
+| R² (R-Squared) | 0.38  |
+
+</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 |
+| --- | --- | --- | --- | --- | --- | --- | --- |
+| CD4-positive helper T cell | 0.92 | 3.45 | 0.67 | 0.81 | 0.16 | 0.88 | 3356.00 |
+| fibroblast | 0.93 | 1.48 | 0.74 | 0.95 | 0.45 | 0.92 | 3251.00 |
+| innate lymphoid cell | 0.85 | 2.47 | 0.58 | 0.75 | 0.22 | 0.83 | 2594.00 |
+| B cell | 0.82 | 3.18 | 0.53 | 0.66 | 0.22 | 0.85 | 1919.00 |
+| DN3 thymocyte | 0.85 | 3.56 | 0.63 | 0.76 | 0.18 | 0.82 | 1788.00 |
+| plasma cell | 0.80 | 2.53 | 0.62 | 0.84 | 0.30 | 0.95 | 1465.00 |
+| vein endothelial cell | 0.95 | 2.05 | 0.74 | 0.87 | 0.36 | 0.87 | 1442.00 |
+| vascular associated smooth muscle cell | 0.90 | 2.31 | 0.70 | 0.87 | 0.38 | 0.89 | 1439.00 |
+| DN1 thymic pro-T cell | 0.90 | 4.04 | 0.62 | 0.72 | 0.24 | 0.84 | 985.00 |
+| naive regulatory T cell | 0.89 | 4.09 | 0.68 | 0.75 | 0.22 | 0.84 | 761.00 |
+| CD8-positive, alpha-beta cytotoxic T cell | 0.94 | 4.09 | 0.68 | 0.74 | 0.22 | 0.85 | 501.00 |
+| T follicular helper cell | 0.84 | 4.57 | 0.63 | 0.68 | 0.24 | 0.82 | 398.00 |
+| capillary endothelial cell | 0.89 | 3.59 | 0.66 | 0.74 | 0.37 | 0.82 | 383.00 |
+| endothelial cell of artery | 0.92 | 3.59 | 0.66 | 0.76 | 0.38 | 0.84 | 355.00 |
+| macrophage | 0.88 | 3.28 | 0.66 | 0.75 | 0.37 | 0.84 | 303.00 |
+| endothelial cell of lymphatic vessel | 0.93 | 3.75 | 0.66 | 0.77 | 0.43 | 0.85 | 256.00 |
+| medullary thymic epithelial cell | 0.78 | 3.32 | 0.70 | 0.84 | 0.50 | 0.90 | 140.00 |
+| monocyte | 0.86 | 4.82 | 0.61 | 0.61 | 0.38 | 0.81 | 88.00 |
+| mature NK T cell | 0.78 | 5.80 | 0.51 | 0.46 | 0.36 | 0.69 | 80.00 |
+| erythrocyte | 0.70 | 6.54 | 0.41 | 0.35 | 0.34 | 0.89 | 39.00 |
+| DN4 thymocyte | 0.72 | 5.70 | 0.56 | 0.58 | 0.43 | 0.78 | 32.00 |
+
+</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,
@@ -32,48 +125,63 @@ 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",
-    "layer": null
+    "layer": null,
+    "batch_key": null
 }
 ```
 
-**model_summary_stats**:
-| Summary Stat Key | Value |
-|------------------|-------|
-|     n_cells      | 21575 |
-|     n_labels     |  21   |
-|      n_vars      | 4000  |
+</details>
+
+<details>
+<summary><strong>Data Registry</strong></summary>
 
-**model_data_registry**:
+Registry elements for AnnData manager:
 | Registry Key |    scvi-tools Location    |
 |--------------|---------------------------|
 |      X       |          adata.X          |
 |    labels    | adata.obs['_scvi_labels'] |
 
-**model_parent_module**: scvi.model
+- **Data is Minified**: False
+
+</details>
+
+<details>
+<summary><strong>Summary Statistics</strong></summary>
+
+| Summary Stat Key | Value |
+|------------------|-------|
+|     n_cells      | 21575 |
+|     n_labels     |  21   |
+|      n_vars      | 3000  |
 
-**data_is_minified**: False
+</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/Thymus_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