BLEND: Probabilistic Cellular Deconvolution with Automated Reference Selection.

ABSTRACT: Cellular deconvolution aims to estimate cell type fractions from bulk transcriptomic and other omics data. Most existing deconvolution methods fail to account for the heterogeneity in cell type-specific (CTS) expression across bulk samples, ignore discrepancies between CTS expression in bulk and cell type reference data, and provide no guidance on cell type reference selection or integration. To address these issues, we introduce BLEND, a hierarchical Bayesian method that leverages multiple reference datasets. BLEND learns the most suitable references for each bulk sample by exploring the convex hulls of references and employs a "bag-of-words" representation for bulk count data for deconvolution. To speed up the computation, we provide an efficient EM algorithm for parameter estimation. No

SUBMITTER: Huang P 

PROVIDER: S-EPMC11326155 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature