Estimating the effect of tissue- and blood-derived cell reference matrices on deconvolving bulk transcriptomic datasets.

ABSTRACT: Cell deconvolution is a widely used method to characterize the composition of the mixed cell population in bulk transcriptomic datasets. While tissue- and blood-derived cell reference matrices (CRMs) are commonly used, their impact on deconvolution accuracy has yet to be systematically evaluated. In this study, we developed tissue- and blood-derived CRMs using single-cell RNA sequencing (scRNA-seq) data from inflammatory bowel disease (IBD). Three publicly available blood-derived CRMs (IRIS, LM22, and ImmunoStates) were incorporated for benchmarking. Deconvolution performance was evaluated using both public bulk transcriptomic datasets and simulated pseudobulk samples by goodness-of-fit and cell fractions correlation. Two infliximab-treated bulk datasets were used to identify treatment-rel

SUBMITTER: Sun S 

PROVIDER: S-EPMC12356330 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature