scvi-tools
/

tabula-sapiens-heart-scvi

@@ -49,14 +49,24 @@ space might still be useful for analysis.
 **Cell-wise Coefficient of Variation**:
-Not provided by uploader
 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**:
-Not provided by uploader
 </details>
@@ -71,7 +81,14 @@ cell-type.
 **Differential expression**:
-Not provided by uploader
 </details>
@@ -121,15 +138,15 @@ Arguments passed to setup_anndata of the original model:
 <summary><strong>Data Registry</strong></summary>
 Registry elements for AnnData manager:
-|[1m [0m[1m  Registry Key   [0m[1m [0m|[1m [0m[1m        scvi-tools Location         [0m[1m [0m|
 |-------------------|--------------------------------------|
-|[94m [0m[94m        X        [0m[94m [0m|[35m [0m[35m              adata.X               [0m[35m [0m|
-|[94m [0m[94m      batch      [0m[94m [0m|[35m [0m[35m      adata.obs['_scvi_batch']      [0m[35m [0m|
-|[94m [0m[94m     labels      [0m[94m [0m|[35m [0m[35m     adata.obs['_scvi_labels']      [0m[35m [0m|
-|[94m [0m[94m   latent_qzm    [0m[94m [0m|[35m [0m[35m   adata.obsm['scvi_latent_qzm']    [0m[35m [0m|
-|[94m [0m[94m   latent_qzv    [0m[94m [0m|[35m [0m[35m   adata.obsm['scvi_latent_qzv']    [0m[35m [0m|
-|[94m [0m[94m   minify_type   [0m[94m [0m|[35m [0m[35madata.uns['_scvi_adata_minify_type'][0m[35m [0m|
-|[94m [0m[94mobserved_lib_size[0m[94m [0m|[35m [0m[35m   adata.obs['observed_lib_size']   [0m[35m [0m|
 - **Data is Minified**: False
@@ -138,16 +155,16 @@ Registry elements for AnnData manager:
 <details>
 <summary><strong>Summary Statistics</strong></summary>
-|[1m [0m[1m    Summary Stat Key    [0m[1m [0m|[1m [0m[1mValue[0m[1m [0m|
 |--------------------------|-------|
-|[94m [0m[94m        n_batch         [0m[94m [0m|[35m [0m[35m  2  [0m[35m [0m|
-|[94m [0m[94m        n_cells         [0m[94m [0m|[35m [0m[35m11505[0m[35m [0m|
-|[94m [0m[94mn_extra_categorical_covs[0m[94m [0m|[35m [0m[35m  0  [0m[35m [0m|
-|[94m [0m[94mn_extra_continuous_covs [0m[94m [0m|[35m [0m[35m  0  [0m[35m [0m|
-|[94m [0m[94m        n_labels        [0m[94m [0m|[35m [0m[35m  6  [0m[35m [0m|
-|[94m [0m[94m      n_latent_qzm      [0m[94m [0m|[35m [0m[35m 20  [0m[35m [0m|
-|[94m [0m[94m      n_latent_qzv      [0m[94m [0m|[35m [0m[35m 20  [0m[35m [0m|
-|[94m [0m[94m         n_vars         [0m[94m [0m|[35m [0m[35m3000 [0m[35m [0m|
 </details>

 **Cell-wise Coefficient of Variation**:
+| Metric                  | Training Value | Validation Value |
+|-------------------------|----------------|------------------|
+| Mean Absolute Error | 0.97  | 1.06           |
+| Pearson Correlation | 0.80  | 0.76  |
+| Spearman Correlation | 0.81 | 0.79  |
+| R² (R-Squared) | 0.55  | 0.45      |
 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 | 14.44   |
+| Pearson Correlation | 0.71  |
+| Spearman Correlation | 0.73 |
+| R² (R-Squared) | -1.12  |
 </details>
 **Differential expression**:
+| Index | gene_f1 | lfc_mae | lfc_pearson | lfc_spearman | roc_auc | pr_auc | n_cells |
+| --- | --- | --- | --- | --- | --- | --- | --- |
+| cardiac muscle cell | 0.95 | 0.48 | 0.81 | 0.98 | 0.40 | 0.93 | 7205.00 |
+| cardiac endothelial cell | 0.98 | 1.15 | 0.63 | 0.94 | 0.31 | 0.90 | 2665.00 |
+| hepatocyte | 0.88 | 0.76 | 0.69 | 0.96 | 0.64 | 0.94 | 1089.00 |
+| fibroblast of cardiac tissue | 0.87 | 3.11 | 0.65 | 0.82 | 0.27 | 0.80 | 250.00 |
+| smooth muscle cell | 0.88 | 3.64 | 0.60 | 0.71 | 0.24 | 0.81 | 222.00 |
+| macrophage | 0.72 | 4.45 | 0.56 | 0.62 | 0.35 | 0.71 | 74.00 |
 </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['scvi_latent_qzm']     |
+|    latent_qzv     |    adata.obsm['scvi_latent_qzv']     |
+|    minify_type    | adata.uns['_scvi_adata_minify_type'] |
+| observed_lib_size |    adata.obs['observed_lib_size']    |
 - **Data is Minified**: False
 <details>
 <summary><strong>Summary Statistics</strong></summary>
+|     Summary Stat Key     | Value |
 |--------------------------|-------|
+|         n_batch          |   2   |
+|         n_cells          | 11505 |
+| n_extra_categorical_covs |   0   |
+| n_extra_continuous_covs  |   0   |
+|         n_labels         |   6   |
+|       n_latent_qzm       |  20   |
+|       n_latent_qzv       |  20   |
+|          n_vars          | 3000  |
 </details>