Integrated inference of cellular compositions and gene expression programs by deconvolution

Abstract

While computational deconvolution is routinely used to estimate cell-type proportions from tissue mixtures, reconstructing cell-type-specific transcriptomes at single-sample resolution remains a fundamentally underdetermined algorithmic challenge. Consequently, accurate single-sample, gene-level inference is rarely achieved by existing tools. Here, we systematically benchmarked multiple deconvolution approaches across diverse biological contexts using both pseudo-bulk mixtures and real bulk RNA-seq datasets derived from multiple tissues. Evaluating the critical computational limitations in these models, we developed BayesPrism-DWLS, a framework that enables integrated inference of cell-type proportions and cell-type-specific expression at single-sample resolution. Applied to mouse colon bulk RNA-seq and spatial transcriptomics, BayesPrism-DWLS revealed cell-type-specific genes and pathways that were undetectable at the bulk or spot level. Therefore, this framework provides a robust, high-resolution tool for dissecting cell heterogeneity and supports mechanistic studies informed by cell-type-specific transcriptional programs using cost-effective sequencing data.