Bayesian log-normal deconvolution for enhanced in silico microdissection of bulk gene expression data.

ABSTRACT: Deconvolution of bulk gene expression profiles into the cellular components is pivotal to portraying tissue's complex cellular make-up, such as the tumor microenvironment. However, the inherently variable nature of gene expression requires a comprehensive statistical model and reliable prior knowledge of individual cell types that can be obtained from single-cell RNA sequencing. We introduce BLADE (Bayesian Log-normAl Deconvolution), a unified Bayesian framework to estimate both cellular composition and gene expression profiles for each cell type. Unlike previous comprehensive statistical approaches, BLADE can handle > 20 types of cells due to the efficient variational inference. Throughout an intensive evaluation with > 700 simulated and real datasets, BLADE demonstrated enhanced robustne

SUBMITTER: Andrade Barbosa B 

PROVIDER: S-EPMC8528834 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature