README.md

metadata

library_name: scvi-tools
license: cc-by-4.0
tags:
  - biology
  - genomics
  - single-cell
  - model_cls_name:TOTALVI
  - scvi_version:1.2.0
  - anndata_version:0.11.1
  - modality:rna
  - modality:protein
  - tissue:thymus
  - annotated:True

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:

Modality: protein

Metric	Training Value	Validation Value
Mean Absolute Error	0.32	0.33
Pearson Correlation	0.52	0.51
Spearman Correlation	0.49	0.49
R² (R-Squared)	-0.01	-0.01

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:

Modality: protein

Metric	Training Value
Mean Absolute Error	0.32
Pearson Correlation	0.87
Spearman Correlation	0.95
R² (R-Squared)	0.16

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:

Modality: protein

Index	gene_f1	lfc_mae	lfc_pearson	lfc_spearman	roc_auc	pr_auc	n_cells
DP (Q2)	0.91	0.09	0.99	0.98	0.50	0.99	10864.00
DP (Sig.)	0.91	0.09	0.97	0.93	0.21	0.93	9824.00
DP (Q1)	1.00	0.08	0.99	0.98	0.61	0.98	8556.00
Mature CD4	0.91	0.13	0.99	0.98	0.57	0.98	6525.00
Immature CD8	0.82	0.08	0.98	0.96	0.35	0.95	5686.00
DP (P)	1.00	0.12	0.98	0.92	0.52	0.92	5593.00
Immature CD4	1.00	0.10	0.99	0.94	0.32	0.94	5164.00
Mature CD8	0.91	0.13	0.99	0.97	0.40	0.96	4234.00
DN	0.82	0.14	0.99	0.94	0.57	0.92	2395.00
GD T	0.82	0.13	0.99	0.95	0.39	0.93	2279.00
Treg	0.91	0.12	0.98	0.98	0.44	0.95	1966.00
Neg. sel. (2)	0.91	0.10	0.99	0.97	0.25	0.90	1560.00
Dying	0.82	0.13	0.93	0.91	0.52	0.93	1552.00
Neg. sel. (1)	0.82	0.13	0.97	0.95	0.27	0.87	1206.00
Mature cycling	0.73	0.17	0.97	0.94	0.27	0.89	992.00
Interferon sig.	0.91	0.09	0.94	0.78	0.15	0.91	984.00
NKT	0.82	0.18	0.95	0.95	0.56	0.93	928.00
Myeloid	1.00	0.18	0.97	0.93	0.66	0.97	908.00
Doublet	0.55	0.35	0.60	0.46	0.81	0.99	677.00
B	0.73	0.60	0.93	0.81	0.40	0.78	106.00
Erythrocyte	0.55	0.74	0.79	0.69	0.50	0.59	43.00

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:

{
    "n_latent": 20,
    "gene_dispersion": "gene",
    "protein_dispersion": "protein",
    "gene_likelihood": "nb",
    "latent_distribution": "normal",
    "empirical_protein_background_prior": null,
    "override_missing_proteins": false
}

Setup Data Arguments

Arguments passed to setup_anndata of the original model:

{
    "rna_layer": "counts",
    "protein_layer": null,
    "batch_key": "sample_id",
    "size_factor_key": null,
    "categorical_covariate_keys": null,
    "continuous_covariate_keys": null,
    "modalities": {
        "rna_layer": "rna",
        "protein_layer": "protein",
        "batch_key": "rna"
    }
}

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  

• Data is Minified: False

Summary Statistics

  Summary Stat Key  	 Value 
  n_batch  	  17  
  n_cells  	 72042 
 n_extra_categorical_covs 	  0  
 n_extra_continuous_covs  	  0  
  n_labels  	  1  
  n_latent_qzm  	  20  
  n_latent_qzv  	  20  
  n_proteins  	  111  
  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.

Training Code URL: https://github.com/YosefLab/Thymus_CITE-seq/blob/main/totalVI_AllData/totalVI_thymus111.ipynb

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.