dtangle: accurate and fast cell-type deconvolution

Abstract

MotivationUnderstanding cell type composition is important to understanding many biological processes. Furthermore, in gene expression studies cell type composition can confound differential expression analysis (DEA). To aid understanding cell type composition, methods of estimating (deconvolving) cell type proportions from gene expression data have been developed.\n\nResultsWe propose dtangle, a new cell-type deconvolution method. dtangle works on a range of DNA microarray and bulk RNA-seq platforms. It estimates cell-type proportions using publicly available, often cross-platform, reference data. To comprehensively evaluate dtangle, we assemble ten benchmark data sets. Here, dtangle is competitive with published deconvolution methods, is robust to selection of tuning parameters and is quicker than other methods. As a case study, we investigate the human immune response to Lyme disease. dtangles estimates reveal a temporal trend consistent with previous findings and are important covariates for DEA across disease status.\n\nAvailabilitydtangle is on CRAN (cran.r-project.org/package=dtangle) or github (dtangle.github.io).\n\nContactgjhunt@umich.edu