TotalVI is a variational inference model for single-cell RNA-seq as well as protein data that can
learn an underlying latent space, integrate technical batches, impute dropouts,
and predict protein expression given gene expression or missing protein data given gene expression
and protein data for a subset of proteins.
The learned low-dimensional latent representation of the data can be used for visualization and
clustering.
TotalVI takes as input a scRNA-seq gene expression and protein expression matrix with cells and
genes.
We provide an extensive
user guide.
- See our original manuscript for further details of the model:
TotalVI 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
CITE-seq to measure RNA and surface proteins in thymocytes from wild-type and T cell lineage-restricted mice to generate a comprehensive timeline of cell state for each T cell lineage.
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:
Not provided by uploader
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:
Not provided by uploader
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:
Not provided by uploader
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_latent": 20,
3 "gene_dispersion": "gene",
4 "protein_dispersion": "protein",
5 "gene_likelihood": "nb",
6 "latent_distribution": "normal",
7 "empirical_protein_background_prior": null,
8 "override_missing_proteins": false
9}
Setup Data Arguments
Arguments passed to setup_anndata of the original model:
1{
2 "rna_layer": "counts",
3 "protein_layer": null,
4 "batch_key": "donor_id",
5 "panel_key": null,
6 "size_factor_key": null,
7 "categorical_covariate_keys": null,
8 "continuous_covariate_keys": null,
9 "modalities": {
10 "rna_layer": "rna",
11 "protein_layer": "protein",
12 "batch_key": "rna"
13 }
14}
Data Registry
Registry elements for AnnData manager:
| Registry Key | scvi-tools Location |
|---|
| X | adata.mod['rna'].layers['counts'] |
| batch | adata.mod['rna'].obs['_scvi_batch'] |
| labels | adata.obs['_scvi_labels'] |
| latent_qzm | adata.obsm['totalvi_latent_qzm'] |
| latent_qzv | adata.obsm['totalvi_latent_qzv'] |
| minify_type | adata.uns['_scvi_adata_minify_type'] |
| observed_lib_size | adata.obs['observed_lib_size'] |
| proteins | adata.mod['protein'].X |
Summary Statistics
| Summary Stat Key | Value |
|---|
| n_batch | 120 |
| n_cells | 647366 |
| n_extra_categorical_covs | 0 |
| n_extra_continuous_covs | 0 |
| n_labels | 1 |
| n_latent_qzm | 20 |
| n_latent_qzv | 20 |
| n_proteins | 192 |
| n_vars | 4000 |
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.
References
Steier, Z., Aylard, D.A., McIntyre, L.L. et al. Single-cell multiomic analysis of thymocyte development reveals drivers of CD4+ T cell and CD8+ T cell lineage commitment. Nat Immunol 24, 1579–1590 (2023).
https://doi.org/10.1038/s41590-023-01584-0.
