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

Type 2 diabetes (T2D) is a common multi-factorial disease that is primarily ac-counted to ineffective insulin action in lowering blood glucose level and later escalates to impaired insu-lin 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 tis-sues have been conducted in past but due to inherent noise in microarray data and heterogeneity in dis-ease etiology; reproduction of prioritized pathways/genes is very low across various studies.

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.

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.

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

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