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f-scLVM: Scalable and versatile factor analysis for single-cell RNA-seq.
Genome Biol. 18:212 (2017)
Single-cell RNA-sequencing (scRNA-seq) allows studying heterogeneity in gene expression in large cell populations. Such heterogeneity can arise due to technical or biological factors, making decomposing sources of variation difficult. We here describe f-scLVM (factorial single-cell latent variable model), a method based on factor analysis that uses pathway annotations to guide the inference of interpretable factors underpinning the heterogeneity. Our model jointly estimates the relevance of individual factors, refines gene set annotations, and infers factors without annotation. In applications to multiple scRNA-seq datasets, we find that f-scLVM robustly decomposes scRNA-seq datasets into interpretable components, thereby facilitating the identification of novel subpopulations.
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Publication type
Article: Journal article
Document type
Scientific Article
Keywords
Single-cell Rna-seq ; Sparse Factor Analysis ; Gene Set Annotations; Embryonic Stem-cells; Gene-expression; Epigenetic Heterogeneity; Unwanted Variation; Sequencing Data; Differentiation; Pathway; Growth
ISSN (print) / ISBN
1474-760X
e-ISSN
1465-6906
Journal
Genome Biology
Quellenangaben
Volume: 18,
Article Number: 212
Publisher
BioMed Central
Publishing Place
London
Non-patent literature
Publications
Reviewing status
Peer reviewed
Institute(s)
Institute of Computational Biology (ICB)