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README.md

@@ -6,103 +6,21 @@ tags:
 - genomics
 - single-cell
 - model_cls_name:SCVI
-- scvi_version:1.2.0
+- scvi_version:1.2.1
 - anndata_version:0.11.1
 - modality:rna
-- tissue:synthetic
 - annotated:False
 ---
 
-
-ScVI is a variational inference model for single-cell RNA-seq data that can learn an underlying
-latent space, integrate technical batches and impute dropouts.
-The learned low-dimensional latent representation of the data can be used for visualization and
-clustering.
-
-scVI takes as input a scRNA-seq gene expression matrix with cells and genes.
-We provide an extensive [user guide](https://docs.scvi-tools.org/en/1.2.0/user_guide/models/scvi.html).
-
-- See our original manuscript for further details of the model:
-[scVI manuscript](https://www.nature.com/articles/s41592-018-0229-2).
-- See our manuscript on [scvi-hub](https://www.biorxiv.org/content/10.1101/2024.03.01.582887v2) how
-to leverage pre-trained models.
-
-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).
-
-
-# Model Description
+# Description
 
 scVI model trained on synthetic IID data and uploaded with the full training data.
 
-# 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 | 0.62  | 0.65           |
-| Pearson Correlation | 0.04  | -0.22  |
-| Spearman Correlation | 0.10 | -0.22  |
-| R² (R-Squared) | -20.13  | -16.61      |
-
-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 | 0.69   |
-| Pearson Correlation | -0.04  |
-| Spearman Correlation | 0.04 |
-| R² (R-Squared) | -1.55  |
-
-</details>
+# Model properties
 
-<details>
-<summary><strong>Differential expression metric</strong></summary>
+Many model properties are in the model tags. Some more are listed below.
 
-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 |
-| --- | --- | --- | --- | --- | --- | --- | --- |
-| 0 | 0.00 | 0.76 | -0.05 | -0.05 | 0.53 | 0.25 | 70.00 |
-| 1 | 0.10 | 0.80 | -0.07 | -0.04 | 0.48 | 0.28 | 61.00 |
-| 2 | 0.20 | 0.77 | 0.01 | 0.01 | 0.57 | 0.28 | 60.00 |
-| 3 | 0.20 | 0.88 | 0.10 | 0.12 | 0.60 | 0.41 | 46.00 |
-| 4 | 0.10 | 0.86 | 0.03 | 0.03 | 0.46 | 0.29 | 45.00 |
-| 5 | 0.00 | 0.94 | -0.05 | -0.13 | 0.37 | 0.27 | 41.00 |
-| 6 | 0.10 | 0.96 | -0.07 | 0.03 | 0.36 | 0.21 | 31.00 |
-| 7 | 0.10 | 1.06 | -0.06 | -0.10 | 0.45 | 0.04 | 24.00 |
-| 8 | 0.00 | 1.16 | -0.02 | -0.06 | 0.40 | 0.12 | 22.00 |
-
-</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:
+**model_init_params**:
 ```json
 {
     "n_hidden": 128,
@@ -115,12 +33,7 @@ These provide the settings to setup the original model:
 }
 ```
 
-</details>
-
-<details>
-<summary><strong>Setup Data Arguments</strong></summary>
-
-Arguments passed to setup_anndata of the original model:
+**model_setup_anndata_args**:
 ```json
 {
     "layer": null,
@@ -132,53 +45,44 @@ Arguments passed to setup_anndata of the original model:
 }
 ```
 
-</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']            |
-
-- **Data is Minified**: False
-
-</details>
+**model_summary_stats**:
+|     Summary Stat Key     | Value |
+|--------------------------|-------|
+|         n_batch          |   1   |
+|         n_cells          |  400  |
+| n_extra_categorical_covs |   0   |
+| n_extra_continuous_covs  |   0   |
+|         n_labels         |   1   |
+|          n_vars          |  100  |
 
-<details>
-<summary><strong>Summary Statistics</strong></summary>
+**model_data_registry**:
+| Registry Key |    scvi-tools Location    |
+|--------------|---------------------------|
+|      X       |          adata.X          |
+|    batch     | adata.obs['_scvi_batch']  |
+|    labels    | adata.obs['_scvi_labels'] |
 
-| Summary Stat Key          | Value |
-|--------------------------|-------|
-| n_batch                   | 1 |
-| n_cells                   | 400 |
-| n_extra_categorical_covs  | 0 |
-| n_extra_continuous_covs   | 0 |
-| n_labels                  | 1 |
-| n_vars                    | 100 |
+**model_parent_module**: scvi.model
 
-</details>
+**data_is_minified**: False
 
+# Training data
 
-<details>
-<summary><strong>Training</strong></summary>
+This is an optional link to where the training data is stored if it is too large
+to host on the huggingface Model hub.
 
 <!-- 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. -->
-**Training data url**: Not provided by uploader
 
-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 data url: N/A
 
-**Training Code URL**: Not provided by uploader
+# Training code
 
-</details>
+This is an optional link to the code used to train the model.
 
+Training code url: N/A
 
 # References
 
-scvi-tools team
+To be added...