Dissection of gene expression datasets into clinically relevant interaction signatures via high-dimensional correlation maximization.

ABSTRACT: Gene expression is controlled by many simultaneous interactions, frequently measured collectively in biology and medicine by high-throughput technologies. It is a highly challenging task to infer from these data the generating effects and cooperating genes. Here, we present an unsupervised hypothesis-generating learning concept termed signal dissection by correlation maximization (SDCM) that dissects large high-dimensional datasets into signatures. Each signature captures a particular signal pattern that was consistently observed for multiple genes and samples, likely caused by the same underlying interaction. A key difference to other methods is our flexible nonlinear signal superposition model, combined with a precise regression technique. Analyzing gene expression of diffuse large B-cel

SUBMITTER: Grau M 

PROVIDER: S-EPMC6883077 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature