ACCURATE ESTIMATION OF RARE CELL-TYPE FRACTIONS FROM TISSUE OMICS DATA VIA HIERARCHICAL DECONVOLUTION.

ABSTRACT: Bulk transcriptomics in tissue samples reflects the average expression levels across different cell types and is highly influenced by cellular fractions. As such, it is critical to estimate cellular fractions to both deconfound differential expression analyses and infer cell type-specific differential expression. Since experimentally counting cells is infeasible in most tissues and studies, in silico cellular deconvolution methods have been developed as an alternative. However, existing methods are designed for tissues consisting of clearly distinguishable cell types and have difficulties estimating highly correlated or rare cell types. To address this challenge, we propose hierarchical deconvolution (HiDecon) that uses single-cell RNA sequencing references and a hierarchical cell-t

SUBMITTER: Huang P 

PROVIDER: S-EPMC12530111 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature