Accurate estimation of cell-type composition from gene expression data

Daphne Tsoucas (), Rui Dong, Haide Chen, Qian Zhu, Guoji Guo and Guo-Cheng Yuan ()
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Daphne Tsoucas: Dana-Farber Cancer Institute
Rui Dong: Dana-Farber Cancer Institute
Haide Chen: Zhejiang University School of Medicine
Qian Zhu: Dana-Farber Cancer Institute
Guoji Guo: Zhejiang University School of Medicine
Guo-Cheng Yuan: Dana-Farber Cancer Institute

Nature Communications, 2019, vol. 10, issue 1, 1-9

Abstract: Abstract The rapid development of single-cell transcriptomic technologies has helped uncover the cellular heterogeneity within cell populations. However, bulk RNA-seq continues to be the main workhorse for quantifying gene expression levels due to technical simplicity and low cost. To most effectively extract information from bulk data given the new knowledge gained from single-cell methods, we have developed a novel algorithm to estimate the cell-type composition of bulk data from a single-cell RNA-seq-derived cell-type signature. Comparison with existing methods using various real RNA-seq data sets indicates that our new approach is more accurate and comprehensive than previous methods, especially for the estimation of rare cell types. More importantly, our method can detect cell-type composition changes in response to external perturbations, thereby providing a valuable, cost-effective method for dissecting the cell-type-specific effects of drug treatments or condition changes. As such, our method is applicable to a wide range of biological and clinical investigations.

Date: 2019
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DOI: 10.1038/s41467-019-10802-z

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