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@@ -54,10 +54,10 @@ space might still be useful for analysis.
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  | Metric | Training Value | Validation Value |
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  |-------------------------|----------------|------------------|
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- | Mean Absolute Error | 2.12 | 2.08 |
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- | Pearson Correlation | 0.48 | 0.52 |
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- | Spearman Correlation | 0.41 | 0.48 |
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- | R² (R-Squared) | -0.36 | -0.49 |
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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.
@@ -66,9 +66,9 @@ 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 | 8.46 |
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- | Pearson Correlation | 0.56 |
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- | Spearman Correlation | 0.55 |
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  | R² (R-Squared) | -1.14 |
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  </details>
@@ -86,18 +86,18 @@ cell-type.
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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 | 0.94 | 1.23 | 0.60 | 0.93 | 0.26 | 0.76 | 1379.00 |
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- | monocyte | 0.85 | 2.02 | 0.58 | 0.85 | 0.23 | 0.68 | 605.00 |
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- | endothelial cell of hepatic sinusoid | 0.76 | 1.91 | 0.63 | 0.88 | 0.54 | 0.82 | 341.00 |
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- | mature NK T cell | 0.81 | 3.00 | 0.60 | 0.87 | 0.48 | 0.83 | 231.00 |
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- | neutrophil | 0.43 | 4.35 | 0.46 | 0.75 | 0.16 | 0.51 | 81.00 |
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- | fibroblast | 0.66 | 2.72 | 0.66 | 0.84 | 0.54 | 0.77 | 70.00 |
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- | hepatocyte | 0.68 | 4.20 | 0.62 | 0.84 | 0.40 | 0.81 | 67.00 |
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- | liver dendritic cell | 0.57 | 5.17 | 0.55 | 0.49 | 0.33 | 0.47 | 34.00 |
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- | T cell | 0.41 | 8.87 | 0.34 | 0.47 | 0.32 | 0.48 | 20.00 |
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- | plasma cell | 0.53 | 6.28 | 0.53 | 0.50 | 0.32 | 0.54 | 19.00 |
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- | intrahepatic cholangiocyte | 0.51 | 5.19 | 0.62 | 0.74 | 0.40 | 0.58 | 11.00 |
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- | erythrocyte | 0.09 | 7.92 | 0.35 | 0.32 | 0.23 | 0.81 | 2.00 |
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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**: Not provided by uploader
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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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