Module extraction from subspace co-expression networks

Abstract

Most existing algorithms for co-expression network construction for the purpose of gene expression data analysis define correlation between a pair of genes over the set of all samples as an edge. In this paper, we propose a way to represent co-expression network that traces correlation among genes over subspace of samples. A method is presented for construction of such a co-expression network. A connectivity measure is also introduced to determine connectivity among genes in the proposed representation of co-expression network. The proposed connectivity measure is used with k-means clustering algorithm to extract network modules from the sub-space co-expression network. The methodology has been applied over real life gene expression datasets and the results are validated in terms of external indices such as p value and Q value.