Insights from deconvolution of cell subtype proportions enhance the interpretation of functional genomic data

Abstract

Cell subtype proportional differences between samples significantly contribute to variation of functional genomic properties such as gene expression or DNA methylation. Current analytical approaches typically deal with cell subtype proportion influences as a nuisance variable to be eliminated. Here we demonstrate how harvesting information about cell subtype proportions from functional genomics data provides insights into the cellular events in human phenotypes. We note a striking concordance between cell subtype proportions estimated from orthogonal genomewide assays, and demonstrate the potential for single-cell RNA-seq data to be used in tissues for which reference cell subtype functional genomic datasets are not available. Taken together, our . CC-BY-NC-ND 4.0 International license peer-reviewed) is the author/funder. It is made available under aThe copyright holder for this preprint (which was not . http://dx.doi.org/10.1101/254441 doi: bioRxiv preprint first posted online Jan. 26, 2018; results confirm the importance of estimating cell subtype proportions when testing a model of cellular reprogramming in human phenotypic association studies, and the value of simultaneously testing for systematic cell subtype proportional alterations as a separate phenotypic association, gaining extra insights from functional genomic studies.