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

• Published in: Nature biotechnology Vol. 33; no. 2; pp. 155 - 160
• Main Authors: Buettner, Florian, Natarajan, Kedar N, Casale, F Paolo, Proserpio, Valentina, Scialdone, Antonio, Theis, Fabian J, Teichmann, Sarah A, Marioni, John C, Stegle, Oliver
• Format: Journal Article
• Language: English
• Published: New York Nature Publishing Group US 01.02.2015; Nature Publishing Group
• Subjects: 38/91; 631/114/2785; 631/337/572; Agriculture; analysis; Animals; Bioinformatics; Biomedical Engineering/Biotechnology; Biomedicine; Biotechnology; Cell cycle; Cell Differentiation - genetics; Cell Lineage - genetics; Computational Biology; Computational mathematics; Gene expression; Gene Expression Regulation, Developmental; Genetic aspects; Genetic Heterogeneity; Genetic research; Heterogeneity; Life Sciences; Mice; Models, Theoretical; Mouse Embryonic Stem Cells; RNA - genetics; RNA sequencing; Sequence Analysis, RNA; Single-Cell Analysis; Subpopulations; Th2 Cells - cytology; Transcriptome - genetics
• ISSN: 1087-0156; 1546-1696; 1546-1696
• DOI: 10.1038/nbt.3102

Hidden cell sub-populations are detected by accounting for confounding variation inthe analysis of single-cell RNA-seq data. 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.