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Integrated inference of cellular compositions and gene expression programs by deconvolution
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DOI：10.1186/s13619-026-00299-5.png)
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Abstract
En 中文
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.
Keywords:
Bulk RNA-seq
Spatial transcriptomics
Cell-type deconvolution
Gene expression deconvolution
BayesPrism-DWLS
Journal
IF:
4.7
Papers:
233
Citations:
654
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Cited Papers