ArticlePLoS genetics2017
Visualizing the structure of RNA-seq expression data using grade of membership models.
Article in PLoS genetics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 106 papers, 1 of them a synthesis that pooled it.
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Who cites it
106 citing papers in PubMed, 1 synthesis or guideline pooled it, 181 citations in OpenAlex.
- What are the applications of single-cell RNA sequencing in cancer research: a systematic review.Journal of experimental & clinical cancer research : CR · 2021Pooled it
- PRISM-GEP: Viewing single-cell expression through the lens of topic modeling.Bioinformatics (Oxford, England) · 2026Article
- A tonsil organoid model reveals Epstein-Barr virus-infected germinal center B cell states during primary infection.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Systematic clustering alignment and feature characterization for single-cell omics using ACE-OF-Clust.bioRxiv : the preprint server for biology · 2026Article
- Histone deacetylases and cell-cycle regulators orchestrate cell-identity transitions during Arabidopsis root regeneration.Molecular plant · 2026Article
- Connecting polygenic disease risk to cell states and regulatory programs through single-cell chromatin accessibility.bioRxiv : the preprint server for biology · 2026Article
- Detection of a sequence feature for recursive splicing.bioRxiv : the preprint server for biology · 2026Article
- Distinct contributions of Etv2Cell reports · 2026Article
- Non-negative matrix factorization algorithms generally improve topic model fits.Statistics and computing · 2026Article
- Estimating Genome-Wide Phylogenies Using Probabilistic Topic Modeling.Systematic biology · 2025Article
- PD-1 regulates tumor-infiltrating CD8+ T cells in both a cell-intrinsic and a cell-extrinsic fashion.The Journal of experimental medicine · 2025Article
- Unsupervised Machine Learning Reveals Temporal Components of Gene Expression in HeLa Cells Following Release from Cell Cycle Arrest.International journal of molecular sciences · 2025Article
- Mapping disease critical spatially variable gene programs by integrating spatial transcriptomics with human genetics.bioRxiv : the preprint server for biology · 2025Article
- Progenitor neighborhoods function as transient niches to sustain olfactory neurogenesis.Stem cell reports · 2025Article
- Latent Class Analysis with Arbitrary-Distribution Responses.Entropy (Basel, Switzerland) · 2025Article
- Building digital histology models of transcriptional tumor programs with generative deep learning for pathology-based precision medicine.Genome medicine · 2025Article
- Oxygen-induced stress reveals context-specific gene regulatory effects in human brain organoids.Genome research · 2025Article
- Novel cancer subtyping method guided by tumor-normal sample in latent space of transcriptomic variational autoencoder.Scientific reports · 2025Article
- Exploring the latent space of transcriptomic data with topic modeling.NAR genomics and bioinformatics · 2025Article
- Single-Cell Analyses Reveal a Functionally Heterogeneous Exhausted CD8+ T-cell Subpopulation That Is Correlated with Response to Checkpoint Therapy in Melanoma.Cancer research · 2025Article
46 more citing papers are in PubMed but not listed here.
Corrections and comments
- Erratum issued
Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
Grade of membership models, also known as "admixture models", "topic models" or "Latent Dirichlet Allocation", are a generalization of cluster models that allow each sample to have membership in multiple clusters. These models are widely used in population genetics to model admixed individuals who have ancestry from multiple "populations", and in natural language processing to model documents having words from multiple "topics". Here we illustrate the potential for these models to cluster samples of RNA-seq gene expression data, measured on either bulk samples or single cells. We also provide methods to help interpret the clusters, by identifying genes that are distinctively expressed in each cluster. By applying these methods to several example RNA-seq applications we demonstrate their utility in identifying and summarizing structure and heterogeneity. Applied to data from the GTEx project on 53 human tissues, the approach highlights similarities among biologically-related tissues and identifies distinctively-expressed genes that recapitulate known biology. Applied to single-cell expression data from mouse preimplantation embryos, the approach highlights both discrete and continuous variation through early embryonic development stages, and highlights genes involved in a variety of relevant processes-from germ cell development, through compaction and morula formation, to the formation of inner cell mass and trophoblast at the blastocyst stage. The methods are implemented in the Bioconductor package CountClust.
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Registered trials
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.