ScVI is a variational inference model for single-cell RNA-seq data that can learn an underlying
latent space, integrate technical batches and impute dropouts.
The learned low-dimensional latent representation of the data can be used for visualization and
clustering.
scVI takes as input a scRNA-seq gene expression matrix with cells and genes.
We provide an extensive
user guide.
- See our original manuscript for further details of the model:
scVI manuscript.
- See our manuscript on scvi-hub how
to leverage pre-trained models.
This model can be used for fine tuning on new data using our Arches framework:
Arches tutorial.
Model Description
Combined single cell and single nuclei RNA-Seq data of 485K cardiac cells with annotations.
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 | 0.91 | 0.99 |
| Pearson Correlation | 0.78 | 0.74 |
| Spearman Correlation | 0.80 | 0.77 |
| R² (R-Squared) | 0.33 | 0.21 |
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 | 1.36 |
| Pearson Correlation | 0.97 |
| Spearman Correlation | 0.99 |
| R² (R-Squared) | 0.93 |
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 |
|---|
| Ventricular_Cardiomyocyte | 0.98 | 0.74 | 0.81 | 0.98 | 0.60 | 0.55 | 5307.00 |
| Endothelial | 0.97 | 0.71 | 0.79 | 0.95 | 0.35 | 0.38 | 4109.00 |
| Pericytes | 0.97 | 1.01 | 0.62 | 0.92 | 0.30 | 0.34 | 3204.00 |
| Fibroblast | 0.97 | 0.87 | 0.79 | 0.97 | 0.33 | 0.37 | 2446.00 |
| Atrial_Cardiomyocyte | 0.95 | 1.22 | 0.78 | 0.94 | 0.32 | 0.36 | 1009.00 |
| Myeloid | 0.95 | 1.69 | 0.66 | 0.93 | 0.07 | 0.10 | 957.00 |
| Lymphoid | 0.93 | 2.26 | 0.67 | 0.88 | 0.04 | 0.06 | 653.00 |
| Smooth_muscle_cells | 0.95 | 2.18 | 0.68 | 0.85 | 0.22 | 0.22 | 641.00 |
| Neuronal | 0.82 | 4.16 | 0.58 | 0.63 | 0.29 | 0.08 | 153.00 |
| Adipocytes | 0.92 | 3.07 | 0.69 | 0.84 | 0.24 | 0.15 | 145.00 |
| Mesothelial | 0.63 | 6.98 | 0.52 | 0.52 | 0.29 | 0.06 | 17.00 |
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:
1{
2 "n_hidden": 128,
3 "n_latent": 10,
4 "n_layers": 1,
5 "dropout_rate": 0.1,
6 "dispersion": "gene",
7 "gene_likelihood": "zinb",
8 "use_observed_lib_size": true,
9 "latent_distribution": "normal"
10}
Setup Data Arguments
Arguments passed to setup_anndata of the original model:
1{
2 "layer": "counts",
3 "batch_key": null,
4 "labels_key": null,
5 "size_factor_key": null,
6 "categorical_covariate_keys": [
7 "cell_source",
8 "donor"
9 ],
10 "continuous_covariate_keys": [
11 "percent_mito",
12 "percent_ribo"
13 ]
14}
Data Registry
Registry elements for AnnData manager:
| Registry Key | scvi-tools Location |
|---|
| X | adata.layers['counts'] |
| batch | adata.obs['_scvi_batch'] |
| extra_categorical_covs | adata.obsm['_scvi_extra_categorical_covs'] |
| extra_continuous_covs | adata.obsm['_scvi_extra_continuous_covs'] |
| labels | adata.obs['_scvi_labels'] |
| latent_qzm | adata.obsm['scvi_latent_qzm'] |
| latent_qzv | adata.obsm['scvi_latent_qzv'] |
| minify_type | adata.uns['_scvi_adata_minify_type'] |
| observed_lib_size | adata.obs['observed_lib_size'] |
Summary Statistics
| Summary Stat Key | Value |
|---|
| n_batch | 1 |
| n_cells | 18641 |
| n_extra_categorical_covs | 2 |
| n_extra_continuous_covs | 2 |
| n_labels | 1 |
| n_latent_qzm | 10 |
| n_latent_qzv | 10 |
| n_vars | 1200 |
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: Not provided by uploader
References
Kazumasa Kanemaru, James Cranley, Daniele Muraro, Antonio M. A. Miranda, Siew Yen Ho, Anna Wilbrey-Clark, Jan Patrick Pett, Krzysztof Polanski, Laura Richardson, Monika Litvinukova, Natsuhiko Kumasaka, Yue Qin, Zuzanna Jablonska, Claudia I. Semprich, Lukas Mach, Monika Dabrowska, Nathan Richoz, Liam Bolt, Lira Mamanova, Rakeshlal Kapuge, Sam N. Barnett, Shani Perera, Carlos Talavera-López, Ilaria Mulas, Krishnaa T. Mahbubani, Liz Tuck, Lu Wang, Margaret M. Huang, Martin Prete, Sophie Pritchard, John Dark, Kourosh Saeb-Parsy, Minal Patel, Menna R. Clatworthy, Norbert Hübner, Rasheda A. Chowdhury, Michela Noseda & Sarah A. Teichmann. Spatially resolved multiomics of human cardiac niches. Nature, July 2023. doi:10.1038/s41586-023-06311-1.
