Gene-set profiles
Abstract
We present a method to visualize gene co-expression from microarray data by plotting profiles of dissimilarity within gene-sets of biological pathways. A gene co-expression network is created by computing the correlation between each gene pair in a gene-set. We transform the networks into scale-free networks in order to calculate the dissimilarity weights that are used to create our profiles. Our approach further distinguishes between gene pairs consisting of both, one, or no statistically significant genes. We find that the shapes and density of the profiles provide useful information for identification of disease gene biomarkers. Our results provide a means of visualizing the overall distribution of gene dissimilarity for each gene-set, as well as how gene dissimilarity is linked to the mutual significance of gene pairs within a gene-set.
Authors: Saila Shama, Philip Lu, Narumol Doungpan, Asawin Meechai, Jonathan H. Chan
Published in: International Symposium on Visual Information Communication and Interaction (VINCI) (2017)