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• W2788357641 abstract "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.katleen.depreter@ugent.be.Supplementary data are available at Bioinformatics online." @default.
• W2788357641 created "2018-03-06" @default.
• W2788357641 creator A5008197456 @default.
• W2788357641 creator A5034282785 @default.
• W2788357641 creator A5058438834 @default.
• W2788357641 creator A5086580140 @default.
• W2788357641 date "2018-01-16" @default.
• W2788357641 modified "2023-10-13" @default.
• W2788357641 title "Computational deconvolution of transcriptomics data from mixed cell populations" @default.
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