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README.md

@@ -1,27 +1,121 @@
 ---
-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:Lymph_Node
+- 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 | 2.86  | 2.90           |
+| Pearson Correlation | 0.25  | 0.25  |
+| Spearman Correlation | 0.14 | 0.15  |
+| R² (R-Squared) | -2.34  | -2.78      |
+
+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 | 40.28   |
+| Pearson Correlation | 0.56  |
+| Spearman Correlation | 0.60 |
+| R² (R-Squared) | -4.27  |
+
+</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 |
+| --- | --- | --- | --- | --- | --- | --- | --- |
+| B cell | 0.90 | 0.78 | 0.62 | 0.93 | 0.30 | 0.87 | 15249.00 |
+| effector CD4-positive, alpha-beta T cell | 0.81 | 1.30 | 0.50 | 0.87 | 0.28 | 0.77 | 6908.00 |
+| effector CD8-positive, alpha-beta T cell | 0.83 | 1.30 | 0.58 | 0.86 | 0.27 | 0.77 | 5860.00 |
+| T cell | 0.72 | 1.77 | 0.52 | 0.80 | 0.26 | 0.71 | 3848.00 |
+| type I NK T cell | 0.75 | 1.61 | 0.54 | 0.82 | 0.30 | 0.71 | 3758.00 |
+| plasma cell | 0.85 | 1.56 | 0.61 | 0.89 | 0.41 | 0.91 | 2426.00 |
+| innate lymphoid cell | 0.79 | 1.98 | 0.42 | 0.66 | 0.26 | 0.58 | 2052.00 |
+| macrophage | 0.82 | 2.01 | 0.65 | 0.88 | 0.48 | 0.89 | 1086.00 |
+| regulatory T cell | 0.81 | 2.94 | 0.57 | 0.72 | 0.41 | 0.72 | 881.00 |
+| mature NK T cell | 0.81 | 4.31 | 0.49 | 0.56 | 0.43 | 0.66 | 437.00 |
+| classical monocyte | 0.87 | 4.23 | 0.61 | 0.68 | 0.48 | 0.76 | 161.00 |
+| endothelial cell | 0.74 | 3.83 | 0.59 | 0.75 | 0.60 | 0.86 | 143.00 |
+| intermediate monocyte | 0.80 | 3.95 | 0.63 | 0.71 | 0.51 | 0.79 | 134.00 |
+| mast cell | 0.78 | 5.96 | 0.51 | 0.45 | 0.45 | 0.77 | 119.00 |
+| stromal cell | 0.82 | 3.90 | 0.68 | 0.78 | 0.55 | 0.86 | 111.00 |
+| neutrophil | 0.82 | 5.76 | 0.55 | 0.52 | 0.42 | 0.84 | 107.00 |
+| CD1c-positive myeloid dendritic cell | 0.75 | 4.37 | 0.63 | 0.68 | 0.52 | 0.79 | 68.00 |
+| CD141-positive myeloid dendritic cell | 0.64 | 4.92 | 0.59 | 0.58 | 0.48 | 0.75 | 48.00 |
+| hematopoietic stem cell | 0.49 | 5.59 | 0.52 | 0.40 | 0.45 | 0.66 | 34.00 |
+| non-classical monocyte | 0.65 | 5.82 | 0.53 | 0.49 | 0.41 | 0.69 | 31.00 |
+| erythrocyte | 0.58 | 6.44 | 0.41 | 0.35 | 0.47 | 0.83 | 24.00 |
+| mature conventional dendritic cell | 0.43 | 6.40 | 0.49 | 0.39 | 0.37 | 0.60 | 17.00 |
+
+</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 +126,63 @@ 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      | 43502 |
-|     n_labels     |  22   |
-|      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      | 43502 |
+|     n_labels     |  22   |
+|      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/Lymph_Node_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