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MCM-test: a fuzzy-set-theory-based approach to differential analysis of gene pathways
Lily R Liang1, Vinay Mandal, Yi Lu
1Department of Computer Science and Information Technology, University of the District of Columbia, Washington, DC, USA. lliang@udc.edu
Background:
Gene 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.
Results:
This 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.
Conclusion:
Our 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.
Insights
This study introduces the Multi-dimensional Cluster Misclassification test (MCM-test) to assess gene pathway significance in diseases. The MCM-test identified mitochondrial pathways as deregulated in diabetes, supporting their role in insulin resistance.
Area of Science:
- Genomics
- Systems Biology
- Bioinformatics
Background:
- Gene pathways, groups of interacting genes, are increasingly recognized for their roles in disease pathogenesis.
- Understanding gene pathway involvement is crucial for disease research beyond individual gene identification.
Purpose of the Study:
- To propose and validate an innovative fuzzy-set-theory-based approach, the Multi-dimensional Cluster Misclassification test (MCM-test).
- To measure the significance of gene pathways in the context of specific diseases.
Main Methods:
- Development of the Multi-dimensional Cluster Misclassification test (MCM-test) using fuzzy-set theory.
- Application of MCM-test to both synthetic and real-world gene expression datasets.
- Utilized KEGG pathways for analysis of a published diabetes gene expression dataset.
Main Results:
- The MCM-test successfully identified deregulated pathways in diabetes.
- Specifically, the OXPHOS pathway and other mitochondrial pathways were found to be deregulated in diabetes patients.
- Results align with existing evidence linking mitochondrial dysfunction to insulin resistance and type-2 diabetes.
Conclusions:
- The MCM-test is a viable tool for pathway-level differential analysis of gene expression data.
- This method offers a novel solution for comparing two groups of data, a fundamental task across research disciplines.
