ARTdeConv: adaptive regularized tri-factor non-negative matrix factorization for cell type deconvolution.

ABSTRACT: Accurate deconvolution of cell types from bulk gene expression is crucial for understanding cellular compositions and uncovering cell-type specific differential expression and physiological states of diseased tissues. Existing deconvolution methods have limitations, such as requiring complete cellular gene expression signatures or neglecting partial biological information. Moreover, these methods often overlook varying cell-type messenger RNA amounts, leading to biased proportion estimates. Additionally, they do not effectively utilize valuable reference information from external studies, such as means and ranges of population cell-type proportions. To address these challenges, we introduce an adaptive regularized tri-factor non-negative matrix factorization approach for deconvolution (ART

SUBMITTER: Liu T 

PROVIDER: S-EPMC12034106 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature