An effective method for network module extraction from microarray data

Abstract

BackgroundThe development of high-throughput Microarray technologies has provided various opportunities to systematically characterize diverse types of computational biological networks. Co-expression network have become popular in the analysis of microarray data, such as for detecting functional gene modules.ResultsThis paper presents a method to build a co-expression network (CEN) and to detect network modules from the built network. We use an effective gene expression similarity measure called NMRS (Normalized mean residue similarity) to construct the CEN. We have tested our method on five publicly available benchmark microarray datasets. The network modules extracted by our algorithm have been biologically validated in terms of Q value and p value.ConclusionsOur results show that the technique is capable of detecting biologically significant network modules from the co-expression network. Biologist can use this technique to find groups of genes with similar functionality based on their expression information.

Construction and Comparison of Gene Co-expression Networks Based on Immunity Microarray Data from Arabidopsis, Rice, Soybean, Tomato and Cassava

“…) and NMRS (s [8]. These measures take values in the same interval [0,1], where 0 indicates non-dependence between expression profiles, and 1 indicates total dependence or maximum similarity.…”

“…In this work, we concentrate on shortcomings of current approaches and propose an enhanced novel methodology to overcome them. We compared the performance of APCC with Mutual Information Coefficient (MIC) [4] and the Normalized Mean Residue Similarity (NMRS) [8], and chose the metric that better detects linear and non-linear correlations. To characterize the GCNs, we added new nontopological variables, such as tolerance to pathogen attacks and assortativity coefficients related to functional annotations.…”

MIPCE: An MI-based protein complex extraction technique

“…These modules encompass groups of genes or proteins involved in common elementary biological functions as found in Collins et al (2007). Revealing modular structures in biological networks Mahanta et al (2012) will help us understand how cells function. To cope with the ever-increasing volume and complexity of protein interaction data, many methods which are based on modelling the PPI data with graphs have been developed for analysing the structure of PPI networks.…”

A Preview on Subspace Clustering of High Dimensional Data

“…However, the high dimensionality of microarray and text data makes the task of pattern discovery difficult for traditional clustering algorithms and for this reason subspace clustering techniques can be used to uncover the complex relationships found in data in these areas. The work proposed in [6] has been extended to trace correlation among genes over a subspace of samples, represented by a co-expression network [13].…”

“…Generally co-expressed genes, which are members of the same clusters, are expected to have similar functions. A method is presented in [6] to build a gene co-expression network (CEN), which is an undirected graph of nodes representing genes, connected by an edge if the corresponding gene pairs are significantly co-expressed. A gene expression similarity measure called NMRS (Normalized mean residue similarity) is used to construct the CEN, which is used to detect network modules from the built network.…”