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

Abstract

Real-world clinical samples are often admixtures of signal mosaics from multiple pure cell types. Using computational tools, bulk transcriptomics can be deconvoluted to solve for the abundance of constituent cell types. However, existing deconvolution methods are conditioned on the assumption that the whole study population is served by a single reference panel, which ignores person-to-person heterogeneity. Here we presentimply, a novel algorithm to deconvolute cell type proportions using personalized reference panels.implycan borrow information across repeatedly measured samples for each subject, and obtain precise cell type proportion estimations. Simulation studies demonstrate reduced bias in cell type abundance estimation compared with existing methods. Real data analyses on large longitudinal consortia show more realistic deconvolution results that align with biological facts. Our results suggest that disparities in cell type proportions are associated with several disease phenotypes in type 1 diabetes and Parkin-son’s disease. Our proposed toolimplyis available through the R/Bioconductor packageISLETathttps://bioconductor.org/packages/ISLET/.