Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells
- PMID: 25599176
- DOI: 10.1038/nbt.3102
Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells
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.
Comment in
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Kindred cells among the crowd.Nat Methods. 2015 Mar;12(3):170-1. doi: 10.1038/nmeth.3307. Nat Methods. 2015. PMID: 25879100 No abstract available.
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The contribution of cell cycle to heterogeneity in single-cell RNA-seq data.Nat Biotechnol. 2016 Jun 9;34(6):591-3. doi: 10.1038/nbt.3498. Nat Biotechnol. 2016. PMID: 27281413 Free PMC article. No abstract available.
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Reply to The contribution of cell cycle to heterogeneity in single-cell RNA-seq data.Nat Biotechnol. 2016 May 6;34(6):593-5. doi: 10.1038/nbt.3607. Nat Biotechnol. 2016. PMID: 27281414 No abstract available.
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