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README.md
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| Metric | Training Value | Validation Value |
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|-------------------------|----------------|------------------|
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| Mean Absolute Error | 2.12 | 2.
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| Pearson Correlation | 0.
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| Spearman Correlation | 0.
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| R² (R-Squared) | -0.
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The gene-wise coefficient of variation summarizes how well variation between different genes is
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preserved by the generated model expression. This value is usually quite high.
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| Metric | Training Value |
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|-------------------------|----------------|
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| Mean Absolute Error | 8.
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| Pearson Correlation | 0.
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| Spearman Correlation | 0.
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| R² (R-Squared) | -1.14 |
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</details>
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| Index | gene_f1 | lfc_mae | lfc_pearson | lfc_spearman | roc_auc | pr_auc | n_cells |
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| --- | --- | --- | --- | --- | --- | --- | --- |
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| macrophage | 0.
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| monocyte | 0.85 | 2.
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| endothelial cell of hepatic sinusoid | 0.
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| mature NK T cell | 0.
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| neutrophil | 0.
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| fibroblast | 0.
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| hepatocyte | 0.
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| liver dendritic cell | 0.
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| T cell | 0.
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| plasma cell | 0.
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| intrahepatic cholangiocyte | 0.
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| erythrocyte | 0.
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</details>
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@@ -192,7 +192,7 @@ scvi-tools documentation for details. -->
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If provided by the original uploader, for those interested in understanding or replicating the
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training process, the code is available at the link below.
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**Training Code URL**:
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</details>
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| Metric | Training Value | Validation Value |
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|-------------------------|----------------|------------------|
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| Mean Absolute Error | 2.12 | 2.22 |
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| Pearson Correlation | 0.50 | 0.54 |
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| Spearman Correlation | 0.40 | 0.38 |
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| R² (R-Squared) | -0.24 | -0.29 |
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The gene-wise coefficient of variation summarizes how well variation between different genes is
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preserved by the generated model expression. This value is usually quite high.
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| Metric | Training Value |
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|-------------------------|----------------|
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| Mean Absolute Error | 8.48 |
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| Pearson Correlation | 0.55 |
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| Spearman Correlation | 0.56 |
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| R² (R-Squared) | -1.14 |
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</details>
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| Index | gene_f1 | lfc_mae | lfc_pearson | lfc_spearman | roc_auc | pr_auc | n_cells |
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| --- | --- | --- | --- | --- | --- | --- | --- |
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| macrophage | 0.93 | 1.21 | 0.62 | 0.94 | 0.25 | 0.75 | 1379.00 |
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| monocyte | 0.85 | 2.11 | 0.56 | 0.84 | 0.24 | 0.68 | 605.00 |
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| endothelial cell of hepatic sinusoid | 0.75 | 1.94 | 0.62 | 0.89 | 0.54 | 0.82 | 341.00 |
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| mature NK T cell | 0.82 | 2.83 | 0.64 | 0.88 | 0.47 | 0.83 | 231.00 |
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| neutrophil | 0.44 | 4.37 | 0.45 | 0.76 | 0.16 | 0.51 | 81.00 |
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| fibroblast | 0.70 | 2.75 | 0.66 | 0.83 | 0.54 | 0.77 | 70.00 |
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| hepatocyte | 0.67 | 4.15 | 0.62 | 0.84 | 0.40 | 0.82 | 67.00 |
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| liver dendritic cell | 0.55 | 5.52 | 0.51 | 0.44 | 0.36 | 0.48 | 34.00 |
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| T cell | 0.46 | 8.35 | 0.40 | 0.51 | 0.31 | 0.48 | 20.00 |
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| plasma cell | 0.54 | 6.31 | 0.53 | 0.48 | 0.34 | 0.55 | 19.00 |
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| intrahepatic cholangiocyte | 0.54 | 4.95 | 0.64 | 0.74 | 0.39 | 0.58 | 11.00 |
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| erythrocyte | 0.24 | 8.16 | 0.33 | 0.29 | 0.23 | 0.80 | 2.00 |
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</details>
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If provided by the original uploader, for those interested in understanding or replicating the
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training process, the code is available at the link below.
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**Training Code URL**: https://github.com/YosefLab/scvi-hub-models/blob/main/src/scvi_hub_models/TS_train_all_tissues.ipynb
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</details>
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