Identification and Evaluation of Functional Modules in Gene Co-expression Networks

Abstract

Abstract. Identifying gene functional modules is an important step towards elucidating gene functions at a global scale. In this paper, we introduce a simple method to construct gene co-expression networks from microarray data, and then propose an efficient spectral clustering algorithm to identify natural communities, which are relatively densely connected sub-graphs, in the network. To assess the effectiveness of our approach and its advantage over existing methods, we develop a novel method to measure the agreement between the gene communities and the modular structures in other reference networks, including protein-protein interaction networks, transcriptional regulatory networks, and gene networks derived from gene annotations. We evaluate the proposed methods on two large-scale gene expression data in budding yeast and Arabidopsis thaliana. The results show that the clusters identified by our method are functionally more coherent than the clusters from several standard clustering algorithms, such as k-means, self-organizing maps, and spectral clustering, and have high agreement to the modular structures in the reference networks.

Variations in the transcriptome of Alzheimer's disease reveal molecular networks involved in cardiovascular diseases

“…Therefore, organizing genes into modules or a modular approach that is based on criteria such as co-expression or co-regulation helps in comparing results across studies and obtaining a global overview of the disease pathogenesis. In this paper, we perform a transcriptome-based study by combining the analysis of co-expressed gene networks and the identification of functional modules and cis -regulatory elements in differentially expressed genes to elucidate the biological processes involved in AD [ 2 - 4 ]. We first construct modules of highly correlated genes (that is, those with high similarity in their expression profiles), and then identify statistically significant regulatory cis -elements (motifs) present in the genes.…”

The Eminence of Co-Expressed Ties in Schizophrenia Network Communities

“…Numerous computational techniques have been adopted in previous studies for identifying the functional network modules from biological entities [16,17]. Some of the popular ones include greedy algorithms, network propagation techniques and co-clustering methodologies [18].…”

Identifying Functional Modules Using MST-Based Weighted Gene Co-Expression Networks

“…Genes inside the same functional module may share the same biological functions [7][8][9][10]. The functional module of gene co-expression network is defined as a 'densely connected sub-network' in which genes inside the sub-network may share the same functions or involve in the same biochemical pathways [8,9].…”

“…Genes inside the same functional module may share the same biological functions [7][8][9][10]. The functional module of gene co-expression network is defined as a 'densely connected sub-network' in which genes inside the sub-network may share the same functions or involve in the same biochemical pathways [8,9]. Previous methods for identifying a set of functional modules from gene coexpression network include the combination of hierarchical clustering and dendrogram pruning [10,11], and graph partitioning [9,12,13].…”

“…However, large scale gene expression data usually render the dendrogram pruning result to be too complex, thus affecting the robustness of relationship identification in gene coexpression sub-networks. Ruan and co-workers further developed a method for identifying sub-network based on graph partitioning algorithm [9]. Even though graph partitioning algorithm is able to identify potential functional modules, their results may suffer from spurious and noisy connections that present in a gene co-expression network.…”