ArticleBioinformatics (Oxford, England)2020
Interpretable factor models of single-cell RNA-seq via variational autoencoders.
Article in Bioinformatics (Oxford, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 106 papers.
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106 citing papers in PubMed.
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- TRAP5 Inhibition Targeting Scar-Associated Macrophages Ameliorates Acute Kidney Injury to Chronic Kidney Disease Transition.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Interpretation, extrapolation and perturbation of single cells.Nature reviews. Genetics · 2026Review
- IST: an ontology-guided attention-based autoencoder for interpretable analysis of single-cell transcriptomic data.Scientific reports · 2026Article
- Patches: A Representation Learning Framework for Decoding Shared and Condition-Specific Transcriptional Programs in Wound Healing.bioRxiv : the preprint server for biology · 2026Article
- Genome-scale functional mapping of the mammalian whole brain with in vivo Perturb-seq.bioRxiv : the preprint server for biology · 2026Article
- Representation learning of single-cell RNA-seq data.RNA (New York, N.Y.) · 2026Review
- Pooled single-cell screen in colorectal cancer defines transcriptional modules linked to oncogenes.Molecular systems biology · 2026Article
- Partially shared multi-modal embedding learns holistic representation of cell state.Nature computational science · 2026Article
- scRDiT: Generating Single-cell RNA-seq Data by Diffusion Transformers and Accelerating Sampling.Interdisciplinary sciences, computational life sciences · 2026Article
- CanSig Benchmarks Methods for Reproducible Cancer Cell State Discovery from Single-Cell Transcriptomic Data.Cancer research · 2026Article
- Age-dependent immune responses and resident cell dynamics in young mice following pneumonia.iScience · 2026Article
- Integrative Learning of Disentangled Representations from Single-Cell RNA-Sequencing Datasets.Computational and structural biotechnology journal · 2026Article
- Predicting oil contamination in water using machine learning on microbial compositions.PloS one · 2026Article
- scRegulate: single-cell regulatory-embedded variational inference of transcription factor activity from gene expression.Bioinformatics (Oxford, England) · 2025Article
46 more citing papers are in PubMed but not listed here.
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4 authors.
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Abstract
motivationSingle-cell RNA-seq makes possible the investigation of variability in gene expression among cells, and dependence of variation on cell type. Statistical inference methods for such analyses must be scalable, and ideally interpretable.
resultsWe present an approach based on a modification of a recently published highly scalable variational autoencoder framework that provides interpretability without sacrificing much accuracy. We demonstrate that our approach enables identification of gene programs in massive datasets. Our strategy, namely the learning of factor models with the auto-encoding variational Bayes framework, is not domain specific and may be useful for other applications. AVAILABILITY AND IMPLEMENTATION: The factor model is available in the scVI package hosted at https://github.com/YosefLab/scVI/. CONTACT: v@nxn.se. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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