Imply: Improving Cell-Type Deconvolution Accuracy Using Personalized Reference Profiles

EasyChair Preprint 11132

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 present imply, a novel algorithm to deconvolute cell type proportions using personalized reference panels. imply can 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 Parkinson’s disease. Our proposed tool imply is available through the R/Bioconductor package ISLET at https://bioconductor.org/packages/ISLET/.

Keyphrases: Admixed samples, Personalized reference, bulk RNA-seq, cell type specific, deconvolution

@booklet{EasyChair:11132,
  author    = {Guanqun Meng and Yue Pan and Wen Tang and Lijun Zhang and Ying Cui and Fredrick R. Schumacher and Ming Wang and Rui Wang and Sijia He and Jeffrey Krischer and Qian Li and Hao Feng},
  title     = {Imply: Improving Cell-Type Deconvolution Accuracy Using Personalized Reference Profiles},
  howpublished = {EasyChair Preprint 11132},
  year      = {EasyChair, 2023}}