ArticleMolecular systems biology2021
Probabilistic harmonization and annotation of single-cell transcriptomics data with deep generative models.
Article in Molecular systems biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 454 papers.
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454 citing papers in PubMed.
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- Multimodal learning enables chat-based exploration of single-cell data.Nature biotechnology · 2026Article
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- Barrier immune memory is programmed by intestinal epithelial cell presentation of cytosol-delivered bacterial antigens.Nature immunology · 2026Article
- Senescence-directed nanotherapy ameliorates fibrosis and overcomes immune exclusion in cancer.Science (New York, N.Y.) · 2026Article
- Senescent Cell Heterogeneity: Tissue-Dependent Signatures and Age Dynamics Revealed by Human scRNA-Seq Data.International journal of molecular sciences · 2026Article
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- Cell-based therapies of autoimmune diseases in the context of artificial intelligence development.Clinical and experimental medicine · 2026Review
- Plasticity of human microglia and brain perivascular macrophages in aging and Alzheimer's disease.Nature genetics · 2026Article
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394 more citing papers are in PubMed but not listed here.
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Authors and funding
6 authors.
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Abstract
As the number of single-cell transcriptomics datasets grows, the natural next step is to integrate the accumulating data to achieve a common ontology of cell types and states. However, it is not straightforward to compare gene expression levels across datasets and to automatically assign cell type labels in a new dataset based on existing annotations. In this manuscript, we demonstrate that our previously developed method, scVI, provides an effective and fully probabilistic approach for joint representation and analysis of scRNA-seq data, while accounting for uncertainty caused by biological and measurement noise. We also introduce single-cell ANnotation using Variational Inference (scANVI), a semi-supervised variant of scVI designed to leverage existing cell state annotations. We demonstrate that scVI and scANVI compare favorably to state-of-the-art methods for data integration and cell state annotation in terms of accuracy, scalability, and adaptability to challenging settings. In contrast to existing methods, scVI and scANVI integrate multiple datasets with a single generative model that can be directly used for downstream tasks, such as differential expression. Both methods are easily accessible through scvi-tools.
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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.