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

Overview

Journal Genome Med

Publisher Biomed Central

Specialty Genetics

Date 2024 Apr 29

PMID 38685057

Authors

Guanqun Meng

Yue Pan

Wen Tang

Lijun Zhang

Ying Cui

Fredrick R Schumacher

Ming Wang

Rui Wang

Sijia He

Jeffrey Krischer

Qian Li

Hao Feng

Affiliations

Soon will be listed here.

Abstract

Using computational tools, bulk transcriptomics can be deconvoluted to estimate 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, ignoring person-to-person heterogeneity. Here, we present imply, a novel algorithm to deconvolute cell type proportions using personalized reference panels. Simulation studies demonstrate reduced bias compared with existing methods. Real data analyses on longitudinal consortia show disparities in cell type proportions are associated with several disease phenotypes in Type 1 diabetes and Parkinson's disease. imply is available through the R/Bioconductor package ISLET at https://bioconductor.org/packages/ISLET/ .

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