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

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2024年4月29日

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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

摘要

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/.