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Buettner, F. ; Pratanwanich, N.* ; McCarthy, D.J.* ; Marioni, J.C.* ; Stegle, O.*

f-scLVM: Scalable and versatile factor analysis for single-cell RNA-seq.

Genome Biol. 18:212 (2017)
Verlagsversion DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
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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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Korrespondenzautor
Schlagwörter 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
Zeitschrift Genome Biology
Quellenangaben Band: 18, Heft: , Seiten: , Artikelnummer: 212 Supplement: ,
Verlag BioMed Central
Verlagsort London
Nichtpatentliteratur Publikationen
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Computational Biology (ICB)