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Time-series Microarray Cluster Analysis Towards Mining the Effects of Treatment

This paper presents clustering analysis using NOD Mice species microarray, from publicly available data at RNA Abundance Database (RAD). The analysis was supported by Short Time-Series Expression Miner (STEM) tool. The raw data involved short records of temporal gene expression data consisting nominal (gene accession numbers) and numerical (quantitative gene expression values) scales. NOD Mice species revealed a susceptibility to development of autoimmune insulin dependent diabetes mellitus (IDDM) obtained from pancreatic islets which are experimentally analyzed using Affymetrix microarray platform for treating the Type 1 diabetes. Type 1 diabetes refers to autoimmune diseases where immune system mistakenly attacks itself and destroys its own insulin-producing cells. The immune system attacks the healthy beta cells which produce insulin in pancreas and consequently unable to balance the body sugar. From this data, we attempted to study correlations and variations effects among samples of similar time steps. Simple statistical analysis and hypotheses testing on data replications were also analyzed to select relevant features of data and eliminate redundancies of expressions prior to clustering study. The major goal in this study was to investigate the efficiencies of Cyclophosphamide treatment imposed on Type 1 diabetes based on clustering analysis.