Microarray data analysis tool using Java and R

Microarray technology has become an essential tool in functional genomics for monitoring the expression of many genes in parallel. Gene expression values obtained from microarray experiments help biologists to understand the way in which a cell responds to varying conditions (including, but not limited to development over time, response to environmental stimuli, or disease states) by analyzing the increase or decrease in the expression level of genes. We have developed web-based software that provides biologists with several statistical solutions for analyzing gene expression data. This platform independent Java servlet first performs normalization of the gene expression values in order to eliminate any systematic bias in the measured intensity values arising from the microarray process. Several normalization methods like Total Intensity Normalization, Median Normalization and Lowess Normalization have been implemented. After normalization, visualization of the experimental data can be performed using scatter plots, MA plots, RI plots and image maps of the intensity ratios. For detection of genes which are differentially expressed the software provides fold-change detection and t-test techniques. The tool also provides the users the ability to create a workflow of the different analysis tools used to study the uploaded data. All the statistical routines used in this software were developed in R called from Java code. This software is a freely available tool to statistically analyze microarray experiments.