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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Acknowledgements
We thank S. Anders and A. Baud for helpful discussions. We also thank the Sanger-EBI Single Cell Centre for technical support. We acknowledge support of the European Research Council (Starting grant no. 260507 thSWITCH to S.A.T., Starting Grant LatentCauses to F.J.T., Marie Curie FP7 fellowship 253524 to O.S.), the Sanger-EBI Single Cell Centre (K.N.N. & A.S.) and the European Molecular Biology Organization (short-term fellowship to F.B.).
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F.B. developed the method, performed the analysis and wrote the paper. K.N.N. performed the mESC experiments and contributed to the analysis. F.P.C. and A.S. contributed to method development and analysis. V.P., S.A.T. and F.J.T. helped interpret the biological results. S.A.T. and V.P. designed the mouse TH2 differentiation experiment. J.C.M. and O.S. designed and supervised this study, contributed to the method development and wrote the paper.
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Supplementary information
Supplementary Text and Figures
Supplementary Figures 1–22, Supplementary Tables 3, 6, 8–9 and Supplementary Notes (PDF 31436 kb)
Supplementary Table 1
List of genes annotated from cell cycle, either from GO or from CycleBase. (XLSX 61 kb)
Supplementary Table 2
List of contribution of all variance components for the T-cell data. (XLSX 484 kb)
Supplementary Table 4
List of significantly differentially expressed genes between identified cell sub clusters. (XLSX 47 kb)
Supplementary Table 5
Manually curated list of 122 Th2 signature genes. (XLSX 33 kb)
Supplementary Table 7
List of genes with more than 5% of the variance explained by interaction between cell cycle and differentiation. (XLSX 34 kb)
Supplementary Data 1
Corrected and uncorrected expression values for T-cell data. (XLSX 9175 kb)
Supplementary Data 2
Corrected and uncorrected expression values for the newly generated mouse ESC data. (XLSX 37981 kb)
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Buettner, F., Natarajan, K., Casale, F. et al. Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells. Nat Biotechnol 33, 155–160 (2015). https://doi.org/10.1038/nbt.3102
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DOI: https://doi.org/10.1038/nbt.3102
