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

Genome Med. 2024 Apr 29;16(1):65. doi: 10.1186/s13073-024-01338-z.

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/ .

Keywords: Admixed samples; Bulk RNA-seq; Cell-type-specific; Deconvolution; Personalized reference.

Publication types

  • Research Support, Non-U.S. Gov't
  • Research Support, N.I.H., Extramural

MeSH terms

  • Algorithms*
  • Computational Biology / methods
  • Diabetes Mellitus, Type 1 / genetics
  • Gene Expression Profiling / methods
  • Humans
  • Parkinson Disease* / genetics
  • Precision Medicine / methods
  • Software
  • Transcriptome