Mathematical modelling of transcriptional heterogeneity identifies novel markers and subpopulations in complex tissues.

ABSTRACT: Tissue heterogeneity is both a major confounding factor and an underexploited information source. While a handful of reports have demonstrated the potential of supervised computational methods to deconvolute tissue heterogeneity, these approaches require a priori information on the marker genes or composition of known subpopulations. To address the critical problem of the absence of validated marker genes for many (including novel) subpopulations, we describe convex analysis of mixtures (CAM), a fully unsupervised in silico method, for identifying subpopulation marker genes directly from the original mixed gene expressions in scatter space that can improve molecular analyses in many biological contexts. Validated with predesigned mixtures, CAM on the gene expression data from peripheral le

SUBMITTER: Wang N 

PROVIDER: S-EPMC4703969 | biostudies-literature | 2016 Jan

REPOSITORIES: biostudies-literature