--- library_name: scvi-tools license: cc-by-4.0 tags: - biology - genomics - single-cell - model_cls_name:CondSCVI - scvi_version:1.2.0 - anndata_version:0.11.1 - modality:rna - tissue:various - annotated:True --- CondSCVI is a variational inference model for single-cell RNA-seq data that can learn an underlying latent space. The predictions of the model are meant to be afterward used for deconvolution of a second spatial transcriptomics dataset in DestVI. DestVI predicts the cell-type proportions as well as cell type-specific activation state in the spatial data. CondSCVI takes as input a scRNA-seq gene expression matrix with cells and genes as well as a cell-type annotation for all cells. We provide an extensive [user guide](https://docs.scvi-tools.org/en/1.2.0/user_guide/models/destvi.html) for DestVI including a description of CondSCVI. - See our original manuscript for further details of the model: [DestVI manuscript](https://www.nature.com/articles/s41587-022-01272-8). - See our manuscript on [scvi-hub](https://www.biorxiv.org/content/10.1101/2024.03.01.582887v2) how to leverage pre-trained models. # Model Description Tabula Sapiens is a benchmark, first-draft human cell atlas of nearly 500,000 cells from 24 organs of 15 normal human subjects. # Metrics We provide here key performance metrics for the uploaded model, if provided by the data uploader.
Coefficient of variation The cell-wise coefficient of variation summarizes how well variation between different cells is preserved by the generated model expression. Below a squared Pearson correlation coefficient of 0.4 , we would recommend not to use generated data for downstream analysis, while the generated latent space might still be useful for analysis. **Cell-wise Coefficient of Variation**: | Metric | Training Value | Validation Value | |-------------------------|----------------|------------------| | Mean Absolute Error | 2.49 | 2.55 | | Pearson Correlation | 0.48 | 0.45 | | Spearman Correlation | 0.42 | 0.40 | | R² (R-Squared) | -0.42 | -0.53 | 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 | 23.66 | | Pearson Correlation | 0.75 | | Spearman Correlation | 0.86 | | R² (R-Squared) | -0.57 |
Differential expression metric The differential expression metric provides a summary of the differential expression analysis between cell types or input clusters. We provide here the F1-score, Pearson Correlation Coefficient of Log-Foldchanges, Spearman Correlation Coefficient, and Area Under the Precision Recall Curve (AUPRC) for the differential expression analysis using Wilcoxon Rank Sum test for each cell-type. **Differential expression**: | Index | gene_f1 | lfc_mae | lfc_pearson | lfc_spearman | roc_auc | pr_auc | n_cells | | --- | --- | --- | --- | --- | --- | --- | --- | | mesenchymal stem cell | 0.92 | 0.95 | 0.68 | 0.90 | 0.39 | 0.85 | 14148.00 | | skeletal muscle satellite stem cell | 0.84 | 1.80 | 0.45 | 0.76 | 0.36 | 0.78 | 4831.00 | | endothelial cell of vascular tree | 0.89 | 2.10 | 0.50 | 0.75 | 0.35 | 0.79 | 3602.00 | | capillary endothelial cell | 0.91 | 2.68 | 0.50 | 0.69 | 0.32 | 0.75 | 2015.00 | | macrophage | 0.89 | 2.71 | 0.52 | 0.74 | 0.39 | 0.82 | 1989.00 | | pericyte | 0.89 | 2.66 | 0.54 | 0.69 | 0.38 | 0.76 | 1794.00 | | CD8-positive, alpha-beta T cell | 0.81 | 5.80 | 0.48 | 0.56 | 0.29 | 0.80 | 536.00 | | T cell | 0.87 | 5.44 | 0.47 | 0.50 | 0.37 | 0.78 | 407.00 | | fast muscle cell | 0.91 | 6.66 | 0.50 | 0.48 | 0.31 | 0.90 | 266.00 | | tendon cell | 0.68 | 5.07 | 0.50 | 0.45 | 0.38 | 0.66 | 234.00 | | CD4-positive, alpha-beta T cell | 0.77 | 6.24 | 0.47 | 0.52 | 0.35 | 0.75 | 221.00 | | slow muscle cell | 0.93 | 6.14 | 0.53 | 0.50 | 0.35 | 0.89 | 163.00 | | mature NK T cell | 0.83 | 6.98 | 0.49 | 0.52 | 0.36 | 0.76 | 137.00 | | endothelial cell of lymphatic vessel | 0.48 | 6.66 | 0.38 | 0.32 | 0.41 | 0.67 | 134.00 | | endothelial cell of artery | 0.70 | 5.38 | 0.56 | 0.54 | 0.38 | 0.73 | 133.00 | | smooth muscle cell | 0.78 | 5.82 | 0.54 | 0.49 | 0.40 | 0.77 | 58.00 | | mast cell | 0.62 | 7.62 | 0.37 | 0.35 | 0.40 | 0.81 | 46.00 | | erythrocyte | 0.14 | 5.19 | 0.25 | 0.41 | 0.53 | 0.96 | 20.00 | | mesothelial cell | 0.20 | 7.73 | 0.35 | 0.25 | 0.39 | 0.72 | 12.00 |
# Model Properties We provide here key parameters used to setup and train the model.
Model Parameters These provide the settings to setup the original model: ```json { "n_hidden": 128, "n_latent": 5, "n_layers": 2, "weight_obs": false, "dropout_rate": 0.05 } ```
Setup Data Arguments Arguments passed to setup_anndata of the original model: ```json { "labels_key": "cell_ontology_class", "layer": null, "batch_key": null } ```
Data Registry Registry elements for AnnData manager: | Registry Key | scvi-tools Location | |--------------|---------------------------| | X | adata.X | | labels | adata.obs['_scvi_labels'] | - **Data is Minified**: False
Summary Statistics | Summary Stat Key | Value | |------------------|-------| | n_cells | 30746 | | n_labels | 19 | | n_vars | 3000 |
Training **Training data url**: Not provided by uploader If provided by the original uploader, for those interested in understanding or replicating the training process, the code is available at the link below. **Training Code URL**: https://github.com/YosefLab/scvi-hub-models/blob/main/src/scvi_hub_models/TS_train_all_tissues.ipynb
# References The Tabula Sapiens Consortium. The Tabula Sapiens: A multiple-organ, single-cell transcriptomic atlas of humans. Science, May 2022. doi:10.1126/science.abl4896