Title:System Level Meta-analysis of Microarray Datasets for Elucidation of Diabetes Mellitus Pathobiology
Volume: 18
Issue: 3
Author(s): Aditya Saxena*, Kumar Sachin and Ashok Kumar Bhatia
Affiliation:
- Department of Biotechnology, Institute of Applied Sciences & Humanities, GLA University, Mathura P.O. Box: 281 406, Mathura,India
Keywords:
Type 2 Diabetes, Insulin-signaling, Microarray, Meta-analysis, Bioconductor, Gene-set analysis.
Abstract: 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.
