Semi-CAM: A semi-supervised deconvolution method for bulk transcriptomic data with partial marker gene information.

ABSTRACT: Deconvolution of bulk transcriptomics data from mixed cell populations is vital to identify the cellular mechanism of complex diseases. Existing deconvolution approaches can be divided into two major groups: supervised and unsupervised methods. Supervised deconvolution methods use cell type-specific prior information including cell proportions, reference cell type-specific gene signatures, or marker genes for each cell type, which may not be available in practice. Unsupervised methods, such as non-negative matrix factorization (NMF) and Convex Analysis of Mixtures (CAM), in contrast, completely disregard prior information and thus are not efficient for data with partial cell type-specific information. In this paper, we propose a semi-supervised deconvolution method, semi-CAM, that extends

SUBMITTER: Dong L 

PROVIDER: S-EPMC7096458 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature