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.Keywords: Type 2 Diabetes, Insulin-signaling, Microarray, Meta-analysis, Bioconductor, Gene-set analysis.
Current Genomics
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.Export Options
About this article
Cite this article as:
Saxena Aditya*, Sachin Kumar and Bhatia Kumar Ashok, System Level Meta-analysis of Microarray Datasets for Elucidation of Diabetes Mellitus Pathobiology, Current Genomics 2017; 18 (3) . https://dx.doi.org/10.2174/1389202918666170105093339
DOI https://dx.doi.org/10.2174/1389202918666170105093339 |
Print ISSN 1389-2029 |
Publisher Name Bentham Science Publisher |
Online ISSN 1875-5488 |
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