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Review
. 2018 Jun 1;34(11):1969-1979.
doi: 10.1093/bioinformatics/bty019.

Computational deconvolution of transcriptomics data from mixed cell populations

Francisco Avila Cobos  1   2   3 , Jo Vandesompele  1   2   3 , Pieter Mestdagh  1   2   3 , Katleen De Preter  1   2   3
Affiliations
Review

Computational deconvolution of transcriptomics data from mixed cell populations

Francisco Avila Cobos et al. Bioinformatics. .

Abstract

Summary: Gene expression analyses of bulk tissues often ignore cell type composition as an important confounding factor, resulting in a loss of signal from lowly abundant cell types. In this review, we highlight the importance and value of computational deconvolution methods to infer the abundance of different cell types and/or cell type-specific expression profiles in heterogeneous samples without performing physical cell sorting. We also explain the various deconvolution scenarios, the mathematical approaches used to solve them and the effect of data processing and different confounding factors on the accuracy of the deconvolution results.

Contact: katleen.depreter@ugent.be.

Supplementary information: Supplementary data are available at Bioinformatics online.

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