ArticleNature methods2021
Joint probabilistic modeling of single-cell multi-omic data with totalVI.
Article in Nature methods, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 352 papers, 1 of them a synthesis that pooled it.
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Who cites it
352 citing papers in PubMed, 1 synthesis or guideline pooled it, 555 citations in OpenAlex.
- Deep learning in single-cell and spatial transcriptomics data analysis: advances and challenges from a data science perspective.Briefings in bioinformatics · 2025Pooled it
- Flexible Integration of Structurally Diverse Spatial Multi-Omics With AgaeSMO.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- scURL: uncertainty-sharpened representation learning for single-cell multi-omics clustering.Bioinformatics (Oxford, England) · 2026Article
- The dual-enhanced graph learning framework DePass allows paired data integration in single-cell and spatial multiomics.Nature cell biology · 2026Article
- CytoVI: deep generative modeling of antibody-based single cell data.Nature methods · 2026Article
- Single-cell and spatial RNA sequencing in prostate cancer.Nature reviews. Urology · 2026Review
- Barrier immune memory is programmed by intestinal epithelial cell presentation of cytosol-delivered bacterial antigens.Nature immunology · 2026Article
- ArchMap is a web-based platform for reference-based analysis of single-cell datasets.Nature genetics · 2026Article
- Unifying multimodal single-cell data with a mixture-of-experts β-variational autoencoder framework.Genome research · 2026Article
- ConvexGating infers gating strategies from clusters in single cell cytometry data.Nature communications · 2026Article
- A Gene-Program Architecture of Mouse T cells.bioRxiv : the preprint server for biology · 2026Article
- Interpretable multi-omics integration across mixed-order tensors with MANTRA.Molecular systems biology · 2026Article
- Single-Cell Level Characterization of B Cell Depletion and Repopulation Following Rituximab in Systemic Lupus Erythematosus.Arthritis & rheumatology (Hoboken, N.J.) · 2026Article
- DPAS-Graph: adaptive spatial-feature relation learning for spatial RNA-to-protein prediction and virtual protein profiling.Briefings in bioinformatics · 2026Article
- Scalable, generalizable and uncertainty-aware integration of spatial multiomics across diverse modalities and platforms with SCIGMA.Nature genetics · 2026Article
- scMOG: A graph neural network method for regulatory relationship-preserving single-cell multi-omics integration.Quantitative biology (Beijing, China) · 2026Article
- Continual integration of single-cell multimodal data with MIRACLE.Nature computational science · 2026Article
- SpaMOAL is a deep learning method that enables accurate spatial domain identification from multi-omics data.PLoS biology · 2026Article
- MultiSpaceNet: graph-based joint representation learning for paired spatial transcriptome-proteome data.Bioinformatics (Oxford, England) · 2026Article
- mmVelo: a deep generative model for estimating cell state-dependent dynamics across multiple modalities.Bioinformatics (Oxford, England) · 2026Article
292 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
7 authors at 5 institutions in 1 country.
Funding
Abstract
The paired measurement of RNA and surface proteins in single cells with cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) is a promising approach to connect transcriptional variation with cell phenotypes and functions. However, combining these paired views into a unified representation of cell state is made challenging by the unique technical characteristics of each measurement. Here we present Total Variational Inference (totalVI; https://scvi-tools.org ), a framework for end-to-end joint analysis of CITE-seq data that probabilistically represents the data as a composite of biological and technical factors, including protein background and batch effects. To evaluate totalVI's performance, we profiled immune cells from murine spleen and lymph nodes with CITE-seq, measuring over 100 surface proteins. We demonstrate that totalVI provides a cohesive solution for common analysis tasks such as dimensionality reduction, the integration of datasets with different measured proteins, estimation of correlations between molecules and differential expression testing.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.