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Buettner, F. ; Natarajan, K.N.* ; Casale, F.P.* ; Proserpio, V.* ; Scialdone, A.* ; Theis, F.J. ; Teichmann, S.A.* ; Marioni, J.C.* ; Stegle, O.*

Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells.

Nat. Biotechnol. 33, 155-160 (2015)
Verlagsversion DOI
Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
Recent technical developments have enabled the transcriptomes of hundreds of cells to be assayed in an unbiased manner, opening up the possibility that new subpopulations of cells can be found. However, the effects of potential confounding factors, such as the cell cycle, on the heterogeneity of gene expression and therefore on the ability to robustly identify subpopulations remain unclear. We present and validate a computational approach that uses latent variable models to account for such hidden factors. We show that our single-cell latent variable model (scLVM) allows the identification of otherwise undetectable subpopulations of cells that correspond to different stages during the differentiation of naive T cells into T helper 2 cells. Our approach can be used not only to identify cellular subpopulations but also to tease apart different sources of gene expression heterogeneity in single-cell transcriptomes.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Embryonic Stem-cells; Gene-expression; Fate Decisions; Seq Analysis; Models; Noise; Transcriptomics; Mechanisms; Landscape; Cycle
ISSN (print) / ISBN 0733-222X
e-ISSN 1546-1696
Zeitschrift Nature Biotechnology
Quellenangaben Band: 33, Heft: 2, Seiten: 155-160 Artikelnummer: , Supplement: ,
Verlag Nature Publishing Group
Verlagsort New York, NY
Begutachtungsstatus Peer reviewed