Abstract
BackgroundGene pathway can be defined as a group of genes that interact with each other to perform some biological processes. Along with the efforts to identify the individual genes that play vital roles in a particular disease, there is a growing interest in identifying the roles of gene pathways in such diseases.ResultsThis paper proposes an innovative fuzzy-set-theory-based approach, Multi-dimensional Cluster Misclassification test (MCM-test), to measure the significance of gene pathways in a particular disease. Experiments have been conducted on both synthetic data and real world data. Results on published diabetes gene expression dataset and a list of predefined pathways from KEGG identified OXPHOS pathway involved in oxidative phosphorylation in mitochondria and other mitochondrial related pathways to be deregulated in diabetes patients. Our results support the previously supported notion that mitochondrial dysfunction is an important event in insulin resistance and type-2 diabetes.ConclusionOur experiments results suggest that MCM-test can be successfully used in pathway level differential analysis of gene expression datasets. This approach also provides a new solution to the general problem of measuring the difference between two groups of data, which is one of the most essential problems in most areas of research.
Highlights
Gene pathway can be defined as a group of genes that interact with each other to perform some biological processes
We propose an innovative fuzzy-set-theorybased approach for differential analysis of gene pathways and apply it on identifying significant pathways for diabetes
We used the MCM-test on the real world diabetes dataset analyzed by Tomfohr et al [5] and gene set enrichment analysis (GSEA) [3]
Summary
Gene pathway can be defined as a group of genes that interact with each other to perform some biological processes. Current microarray technologies conduct simultaneous studies of gene expression data of thousands of genes under different conditions In most of these studies, expression data are analyzed using various standard statistical methods to identify a list of genes responsible for a particular condition. In these approaches, interplay among genes is not taken into account. Along with the efforts to identify the individual genes that play vital roles in a particular disease, there is a growing interest in identifying the roles of different pathways in such diseases.
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