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README.md
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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.
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- anndata_version:0.
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- modality:rna
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- tissue:various
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- annotated:True
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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/
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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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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/
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# Model Description
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**Cell-wise Coefficient of Variation**:
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|-------------------------|----------------|------------------|
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| Mean Absolute Error | 1.25 | 1.34 |
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| Pearson Correlation | 0.95 | 0.94 |
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| Spearman Correlation | 0.93 | 0.92 |
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| R² (R-Squared) | 0.89 | 0.86 |
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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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|-------------------------|----------------|
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| Mean Absolute Error | 19.41 |
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| Pearson Correlation | 0.69 |
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| Spearman Correlation | 0.77 |
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| R² (R-Squared) | -0.95 |
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</details>
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**Differential expression**:
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| --- | --- | --- | --- | --- | --- | --- | --- |
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| acinar cell of salivary gland | 0.96 | 0.60 | 0.74 | 0.96 | 0.14 | 0.94 | 10147.00 |
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| memory B cell | 0.93 | 1.22 | 0.69 | 0.92 | 0.38 | 0.88 | 2879.00 |
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| fibroblast | 0.97 | 1.06 | 0.69 | 0.92 | 0.44 | 0.88 | 2169.00 |
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| CD8-positive, alpha-beta T cell | 0.94 | 2.11 | 0.67 | 0.88 | 0.21 | 0.77 | 1979.00 |
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| macrophage | 0.87 | 1.23 | 0.69 | 0.93 | 0.48 | 0.88 | 1761.00 |
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| duct epithelial cell | 0.93 | 1.11 | 0.72 | 0.91 | 0.39 | 0.83 | 1693.00 |
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| CD4-positive helper T cell | 0.97 | 2.08 | 0.62 | 0.87 | 0.32 | 0.83 | 1221.00 |
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| naive B cell | 0.93 | 2.37 | 0.66 | 0.84 | 0.33 | 0.84 | 1120.00 |
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| basal cell | 0.87 | 1.44 | 0.71 | 0.92 | 0.55 | 0.90 | 922.00 |
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| adventitial cell | 0.91 | 2.36 | 0.65 | 0.87 | 0.37 | 0.80 | 442.00 |
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| monocyte | 0.89 | 2.59 | 0.68 | 0.83 | 0.40 | 0.80 | 426.00 |
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| plasma cell | 0.85 | 2.38 | 0.67 | 0.80 | 0.21 | 0.85 | 345.00 |
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| endothelial cell | 0.85 | 2.70 | 0.64 | 0.79 | 0.50 | 0.84 | 323.00 |
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| endothelial cell of lymphatic vessel | 0.82 | 2.51 | 0.68 | 0.83 | 0.52 | 0.84 | 320.00 |
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| mature NK T cell | 0.82 | 3.07 | 0.66 | 0.79 | 0.38 | 0.80 | 317.00 |
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| T cell | 0.73 | 3.99 | 0.51 | 0.56 | 0.30 | 0.59 | 227.00 |
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| myoepithelial cell | 0.92 | 3.63 | 0.61 | 0.68 | 0.35 | 0.68 | 196.00 |
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| CD4-positive, alpha-beta T cell | 0.46 | 5.79 | 0.50 | 0.60 | 0.21 | 0.72 | 182.00 |
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| pericyte | 0.80 | 3.19 | 0.67 | 0.72 | 0.42 | 0.75 | 146.00 |
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| B cell | 0.35 | 2.73 | 0.59 | 0.77 | 0.43 | 0.75 | 137.00 |
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| neutrophil | 0.90 | 4.36 | 0.61 | 0.59 | 0.35 | 0.77 | 115.00 |
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| ionocyte | 0.83 | 4.53 | 0.61 | 0.63 | 0.39 | 0.72 | 87.00 |
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| salivary gland cell | 0.50 | 3.99 | 0.62 | 0.68 | 0.55 | 0.81 | 45.00 |
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</details>
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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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Arguments passed to setup_anndata of the original model:
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```json
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{
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"layer":
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"batch_key": "donor_assay",
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"labels_key": "
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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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<summary><strong>Data Registry</strong></summary>
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Registry elements for AnnData manager:
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| observed_lib_size
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- **Data is Minified**: False
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<details>
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<summary><strong>Summary Statistics</strong></summary>
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| n_extra_categorical_covs
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</details>
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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:1.4.2
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- anndata_version:0.12.7
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- modality:rna
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- tissue:various
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- annotated:True
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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/stable/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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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/stable/tutorials/notebooks/scrna/scarches_scvi_tools.html).
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# Model Description
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**Cell-wise Coefficient of Variation**:
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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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Not provided by uploader
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</details>
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**Differential expression**:
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</details>
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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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"use_observed_lib_size": true,
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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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Arguments passed to setup_anndata of the original model:
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```json
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{
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"layer": "counts",
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"batch_key": "donor_assay",
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"labels_key": "cell_type",
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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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<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.layers['counts'] |
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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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<summary><strong>Summary Statistics</strong></summary>
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| Summary Stat Key | Value |
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|--------------------------|-------|
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| n_batch | 6 |
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| n_cells | 39821 |
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| n_extra_categorical_covs | 0 |
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| n_extra_continuous_covs | 0 |
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| n_labels | 31 |
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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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