BEDwARS: a robust Bayesian approach to bulk gene expression deconvolution with noisy reference signatures

Abstract

Differential gene expression in bulk transcriptomics data can reflect change of transcript abundance within a cell type and/or change in the proportions of cell types. Expression deconvolution methods can help differentiate these scenarios. BEDwARS is a Bayesian deconvolution method designed to address differences between reference signatures of cell types and corresponding true signatures underlying bulk transcriptomic profiles. BEDwARS is more robust to noisy reference signatures and outperforms leading in-class methods for estimating cell type proportions and signatures. Application of BEDwARS to dihydropyridine dehydrogenase deficiency identified the possible involvement of ciliopathy and impaired translational control in the etiology of the disorder.

imply: improving cell-type deconvolution accuracy using personalized reference profiles

“…This assumption ignores person-to-person heterogeneity for CTS gene expression, and deviates from the biological fact that the gene expression profile could vary, even for one purified cell type, depending on environmental influences, age, sex, subject’s health status, and treatment paradigms [ 1 , 8 , 15 , 18 , 23 , 27 , 28 , 40 , 48 ]. Mismatched reference signatures can impact the deconvolution accuracy [ 22 , 47 ]. The problem is even exacerbated when handling longitudinally observed and repeatedly-measured data, when intra-subject samples share information and inter-subject heterogeneities are relatively strong.…”