Computational Analysis of Cell-to-cell Heterogeneity in Single-cell RNA-sequencing Data Reveals Hidden Subpopulations of Cells

Overview

Journal Nat Biotechnol

Specialty Biotechnology

Date 2015 Jan 20

PMID 25599176

Citations 558

Authors

Florian Buettner

Kedar N Natarajan

F Paolo Casale

Valentina Proserpio

Antonio Scialdone

Fabian J Theis

Sarah A Teichmann

John C Marioni

Oliver Stegle

Affiliations

Soon will be listed here.

Abstract

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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