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01-12-2019 | Review Article | Issue 1/2019

Network Modeling Analysis in Health Informatics and Bioinformatics 1/2019

Qualitative assessment of functional module detectors on microarray and RNASeq data

Journal:
Network Modeling Analysis in Health Informatics and Bioinformatics > Issue 1/2019
Authors:
Monica Jha, Pietro. H. Guzzi, Swarup Roy

Abstract

A set of correlated and co-expressed genes, often referred as a functional module, play a synergistic role during any disease or any biological activities. Genes participating in a common module may cause clinically similar diseases and share a common genetic origin of their associated disease phenotypes. Identifying such modules may be helpful in system-level understanding of biological and cellular processes or pathophysiologic basis of associated diseases. As a result detecting such functional modules is an active research issue in the area of computational biology. Some techniques have been proposed so far to find functional modules based on gene co-regulation or co-expression data. These methods are broadly categorized into non-network based gene expression clustering techniques and network-based methods that extract modules from gene co-expression networks using expression data sources. We survey main approaches for obtaining modules, and we evaluate their performance regarding finding biologically significant gene modules in light of both microarray and RNASeq data. No prior effort, other than independent assessment, has been made so far to evaluate their performances in an integrated way in the light of both microarray and RNASeq data. We assess the significance of the modules in terms of gene ontology and pathway analysis. We select a few of the best performers to access their capability in finding disease-specific modules. Our comparison reveals that no single algorithm is a winner in all respects. Moreover, performances vary widely with microarray and RNASeq data. Relatively, biclustering performs better, when we consider microarray expression data, but fails to perform well in case of RNASeq data. Network-based techniques work better in RNASeq.

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