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--- |
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library_name: scvi-tools |
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license: cc-by-4.0 |
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tags: |
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- biology |
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- genomics |
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- single-cell |
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- model_cls_name:SCVI |
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- scvi_version:1.2.0 |
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- anndata_version:0.11.1 |
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- modality:rna |
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- tissue:various |
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- annotated:True |
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--- |
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ScVI is a variational inference model for single-cell RNA-seq data that can learn an underlying |
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latent space, integrate technical batches and impute dropouts. |
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The learned low-dimensional latent representation of the data can be used for visualization and |
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clustering. |
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scVI takes as input a scRNA-seq gene expression matrix with cells and genes. |
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We provide an extensive [user guide](https://docs.scvi-tools.org/en/1.2.0/user_guide/models/scvi.html). |
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- See our original manuscript for further details of the model: |
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[scVI manuscript](https://www.nature.com/articles/s41592-018-0229-2). |
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- See our manuscript on [scvi-hub](https://www.biorxiv.org/content/10.1101/2024.03.01.582887v2) how |
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to leverage pre-trained models. |
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This model can be used for fine tuning on new data using our Arches framework: |
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[Arches tutorial](https://docs.scvi-tools.org/en/1.0.0/tutorials/notebooks/scarches_scvi_tools.html). |
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# Model Description |
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Tabula Sapiens is a benchmark, first-draft human cell atlas of nearly 500,000 cells from 24 organs of 15 normal human subjects. |
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# Metrics |
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We provide here key performance metrics for the uploaded model, if provided by the data uploader. |
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<details> |
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<summary><strong>Coefficient of variation</strong></summary> |
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The cell-wise coefficient of variation summarizes how well variation between different cells is |
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preserved by the generated model expression. Below a squared Pearson correlation coefficient of 0.4 |
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, we would recommend not to use generated data for downstream analysis, while the generated latent |
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space might still be useful for analysis. |
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**Cell-wise Coefficient of Variation**: |
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| Metric | Training Value | Validation Value | |
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|-------------------------|----------------|------------------| |
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| Mean Absolute Error | 2.44 | 2.57 | |
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| Pearson Correlation | 0.72 | 0.67 | |
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| Spearman Correlation | 0.66 | 0.62 | |
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| R² (R-Squared) | 0.07 | -0.08 | |
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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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**Gene-wise Coefficient of Variation**: |
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| Metric | Training Value | |
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|-------------------------|----------------| |
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| Mean Absolute Error | 17.51 | |
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| Pearson Correlation | 0.58 | |
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| Spearman Correlation | 0.60 | |
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| R² (R-Squared) | -2.35 | |
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</details> |
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<details> |
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<summary><strong>Differential expression metric</strong></summary> |
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The differential expression metric provides a summary of the differential expression analysis |
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between cell types or input clusters. We provide here the F1-score, Pearson Correlation |
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Coefficient of Log-Foldchanges, Spearman Correlation Coefficient, and Area Under the Precision |
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Recall Curve (AUPRC) for the differential expression analysis using Wilcoxon Rank Sum test for each |
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cell-type. |
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**Differential expression**: |
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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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| luminal epithelial cell of mammary gland | 0.91 | 0.61 | 0.80 | 0.97 | 0.47 | 0.91 | 5378.00 | |
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| T cell | 0.93 | 2.10 | 0.57 | 0.82 | 0.19 | 0.83 | 2324.00 | |
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| fibroblast of breast | 0.92 | 1.19 | 0.69 | 0.93 | 0.43 | 0.89 | 2066.00 | |
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| basal cell | 0.91 | 3.17 | 0.58 | 0.75 | 0.33 | 0.72 | 476.00 | |
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| vein endothelial cell | 0.85 | 3.44 | 0.63 | 0.78 | 0.39 | 0.79 | 263.00 | |
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| macrophage | 0.77 | 3.77 | 0.59 | 0.73 | 0.34 | 0.79 | 219.00 | |
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| plasma cell | 0.77 | 3.64 | 0.55 | 0.69 | 0.20 | 0.86 | 194.00 | |
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| endothelial cell of artery | 0.79 | 3.99 | 0.62 | 0.73 | 0.40 | 0.76 | 168.00 | |
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| pericyte | 0.78 | 4.85 | 0.57 | 0.61 | 0.37 | 0.71 | 106.00 | |
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| vascular associated smooth muscle cell | 0.78 | 5.37 | 0.55 | 0.55 | 0.38 | 0.71 | 71.00 | |
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| B cell | 0.51 | 7.88 | 0.40 | 0.37 | 0.35 | 0.73 | 43.00 | |
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| endothelial cell of lymphatic vessel | 0.55 | 6.35 | 0.52 | 0.45 | 0.40 | 0.70 | 32.00 | |
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| mature NK T cell | 0.47 | 6.09 | 0.44 | 0.44 | 0.30 | 0.78 | 19.00 | |
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| mast cell | 0.27 | 7.18 | 0.38 | 0.32 | 0.39 | 0.75 | 16.00 | |
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</details> |
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# Model Properties |
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We provide here key parameters used to setup and train the model. |
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<details> |
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<summary><strong>Model Parameters</strong></summary> |
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These provide the settings to setup the original model: |
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```json |
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{ |
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"n_hidden": 128, |
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"n_latent": 20, |
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"n_layers": 3, |
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"dropout_rate": 0.05, |
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"dispersion": "gene", |
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"gene_likelihood": "nb", |
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"latent_distribution": "normal", |
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"use_batch_norm": "none", |
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"use_layer_norm": "both", |
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"encode_covariates": true |
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} |
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``` |
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</details> |
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<details> |
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<summary><strong>Setup Data Arguments</strong></summary> |
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Arguments passed to setup_anndata of the original model: |
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```json |
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{ |
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"layer": null, |
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"batch_key": "donor_assay", |
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"labels_key": "cell_ontology_class", |
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"size_factor_key": null, |
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"categorical_covariate_keys": null, |
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"continuous_covariate_keys": null |
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} |
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``` |
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</details> |
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<details> |
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<summary><strong>Data Registry</strong></summary> |
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Registry elements for AnnData manager: |
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| Registry Key | scvi-tools Location | |
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|-------------------|--------------------------------------| |
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| X | adata.X | |
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| batch | adata.obs['_scvi_batch'] | |
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| labels | adata.obs['_scvi_labels'] | |
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| latent_qzm | adata.obsm['scvi_latent_qzm'] | |
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| latent_qzv | adata.obsm['scvi_latent_qzv'] | |
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| minify_type | adata.uns['_scvi_adata_minify_type'] | |
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| observed_lib_size | adata.obs['observed_lib_size'] | |
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- **Data is Minified**: False |
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</details> |
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<details> |
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<summary><strong>Summary Statistics</strong></summary> |
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| Summary Stat Key | Value | |
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|--------------------------|-------| |
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| n_batch | 2 | |
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| n_cells | 11375 | |
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| n_extra_categorical_covs | 0 | |
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| n_extra_continuous_covs | 0 | |
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| n_labels | 14 | |
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| n_latent_qzm | 20 | |
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| n_latent_qzv | 20 | |
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| n_vars | 3000 | |
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</details> |
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<details> |
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<summary><strong>Training</strong></summary> |
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<!-- If your model is not uploaded with any data (e.g., minified data) on the Model Hub, then make |
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sure to provide this field if you want users to be able to access your training data. See the |
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scvi-tools documentation for details. --> |
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**Training data url**: Not provided by uploader |
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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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# References |
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The Tabula Sapiens Consortium. The Tabula Sapiens: A multiple-organ, single-cell transcriptomic atlas of humans. Science, May 2022. doi:10.1126/science.abl4896 |
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