Intrinsic-overlapping co-expression module detection with application to Alzheimer's Disease. (December 2018)

• Record Type: Journal Article
• Authors: Manners, Hazel Nicolette; Roy, Swarup; Kalita, Jugal K.
• Is Part Of: Computational biology and chemistry. Volume 77(2018)
• Page Start: 373
• Page End: 389
• Publication Date: 2018-12
• Subjects: Functional Module -- Clustering -- Overlapping -- Intrinsic -- Co-expression -- Alzheimer's Disease (AD); Chemistry -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; Biochemistry -- Data processing; Biology -- Data processing; Molecular biology -- Data processing; Periodicals; Electronic journals; 542.85
• DOI: 10.1016/j.compbiolchem.2018.10.014
• Languages: English
• ISSNs: 1476-9271

Abstract

Graphical abstract: Highlights: A new association measure is proposed to create weighted gene co-expression network. The new association measure is compared to other existing correlation measures and gene network inference methods. A new density-based algorithm called CluViaN is proposed that can detect intrinsic as well as overlapping modules from gene co-expression network. CluViaN is compared with other existing network module detection techniques by evaluating in terms of gene ontology, p-values and topological statistics. Alzheimer's disease (AD) datasets are studied and module extraction is performed using CluViaN. Ranking of modules responsible for Alzheimer's disease as well as identifying central hub genes that play a significant role in Alzheimer's disease is performed. Abstract: Genes interact with each other and may cause perturbation in the molecular pathways leading to complex diseases. Often, instead of any single gene, a subset of genes interact, forming a network, to share common biological functions. Such a subnetwork is called a functional module or motif. Identifying such modules and central key genes in them, that may be responsible for a disease, may help design patient-specific drugs. In this study, we consider the neurodegenerative Alzheimer's Disease (AD) and identify potentially responsible genes from functional motif analysis. We start from the hypothesis that central genes in genetic modules are more relevant to a disease that is under investigation and identify hub genes from the modules as potential marker genes. Motifs or modules are often non-exclusive or overlapping in nature. Moreover, they sometimes show intrinsic or hierarchical distributions with overlapping functional roles. To the best of our knowledge, no prior work handles both the situations in an integrated way. We propose a non-exclusive clustering approach, CluViaN (Clustering Via Network) that can detect intrinsic as well as overlapping modules from gene co-expression networks constructed using microarray expression profiles. We compare our method with existing methods to evaluate the quality of modules extracted. CluViaN reports the presence of intrinsic and overlapping motifs in different species not reported by any other research. We further apply our method to extract significant AD specific modules using CluViaN and rank them based the number of genes from a module involved in the disease pathways. Finally, top central genes are identified by topological analysis of the modules. We use two different AD phenotype data for experimentation. We observe that central genes, namely PSEN1, APP, NDUFB2, NDUFA1, UQCR10, PPP3R1 and a few more, play significant roles in the AD. Interestingly, our experiments also find a hub gene, PML, which has recently been reported to play a role in plasticity, circadian rhythms and the response to proteins which can cause neurodegenerative disorders. MUC4, another hub gene that we find experimentally is yet to be investigated for its potential role in AD. A software implementation of CluViaN in Java is available for download at https://sites.google.com/site/swarupnehu/publications/resources/CluViaN Software.rar.