Several studies have explored gene coexpression changes in cancer, revealing known cancer genes that were top-ranked among coexpression changes but not necessary (separately) among differentially expressed genes.
The gene coexpression changes between different conditions indicate gene regulatory pathways and networks associated with disease.
Stage-specific coexpression modules were selected by the gene coexpression network analysis (WGCNA) which is an unsupervised clustering method to group genes which have coexpression patterns into distinct modules [ 13].
Differential and comparative gene coexpression analyses suggest a change in coexpression relationships and regulators controlling common and/or specific biological processes.
In contrast, our two methods, differential coexpression profile (DCp) and differential coexpression enrichment (DCe), are designed based on the exact coexpression changes of gene pairs, and thus can differentiate significant coexpression changes from relatively trivial ones, and identify coexpression reversal between positive and negative (Yu,H. et al., submitted for publication).
In this article, we present a novel approach that assesses multivariate changes in the gene coexpression network between two conditions.
Pairs of genes, represented as nodes, that show coordinated changes in expression across lines are linked by edges to form evolutionary gene coexpression networks.
To obtain the biological meaning of the correlation changes, we observed the coexpression changes of the Arabidopsis genes involved in photosystem II (PS-II) and in glycerolipid metabolism.
Thus, we checked the coexpression changes of PPL2 and the genes in the Ndh complex.
The search for functional noncoding sequences can be guided by bioinformatic analyses combining evolutionary conservation, gene coexpression, and combinations of overrepresented short-sequence motifs.
