MCbiclust: a novel algorithm to discover large-scale functionally related gene sets from massive transcriptomics data collections.

ABSTRACT: The potential to understand fundamental biological processes from gene expression data has grown in parallel with the recent explosion of the size of data collections. However, to exploit this potential, novel analytical methods are required, capable of discovering large co-regulated gene networks. We found current methods limited in the size of correlated gene sets they could discover within biologically heterogeneous data collections, hampering the identification of multi-gene controlled fundamental cellular processes such as energy metabolism, organelle biogenesis and stress responses. Here we describe a novel biclustering algorithm called Massively Correlated Biclustering (MCbiclust) that selects samples and genes from large datasets with maximal correlated gene expression, allowing re

SUBMITTER: Bentham RB 

PROVIDER: S-EPMC5587796 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature