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System Level Meta-analysis of Microarray Datasets for Elucidation of Diabetes Mellitus Pathobiology

[ Vol. 18 , Issue. 3 ]


Aditya Saxena*, Kumar Sachin and Ashok Kumar Bhatia   Pages 298 - 304 ( 7 )


Background: Type 2 diabetes (T2D) is a common multi-factorial disease that is primarily accounted to ineffective insulin action in lowering blood glucose level and later escalates to impaired insulin secretion by pancreatic β cells. Deregulation in insulin signaling to its target organs is attributed to this disease phenotype. Various genome-wide microarray studies from multiple insulin responsive tissues have been conducted in past but due to inherent noise in microarray data and heterogeneity in disease etiology; reproduction of prioritized pathways/genes is very low across various studies.

Objective: In this study, we aim to identify consensus signaling and metabolic pathways through system level meta-analysis of multiple expression-sets to elucidate T2D pathobiology.

Method: We used ‘R’, an open source statistical environment, which is routinely used for Microarray data analysis particularly using special sets of packages available at Bioconductor. We primarily focused on gene-set analysis methods to elucidate various aspects of T2D.

Result: Literature-based evidences have shown the success of our approach in exploring various known aspects of diabetes pathophysiology.

Conclusion: Our study stressed the need to develop novel bioinformatics workflows to advance our understanding further in insulin signaling.


Type 2 Diabetes, Insulin-signaling, Microarray, Meta-analysis, Bioconductor, Gene-set analysis.


Department of Biotechnology, Institute of Applied Sciences & Humanities, GLA University, Mathura P.O. Box: 281 406, Mathura, Department of Biochemistry and Biotechnology, S.B.S. (PG) Institute of Biomedical Sciences & Research, Dehradun (U.K.), Uttarakhand Technical University, Dehradun (U.K.)

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