Microarray technology is a very powerful tool for medical and biological research which allows the monitoring of expression levels of thousands of genes simultaneously. Performing microarray experiments and getting the results is not the end but just the beginning. Microarray experiments generate overwhelmingly large amount of data. In order to make sense out of these data one needs to use sophisticated statistical software and tools. Various sources have developed many software packages for analyzing microarray data. We also have developed some advanced statistical analysis methods in house. It has been amply proven that the analysis results provide in depth understanding on gene regulations.
Microarray data can be analyzed using several approaches based on research goals. The basic approach of microarray data analysis is the identification of differentially expressed genes. Clustering analysis is used widely to identify clusters of genes with correlated patterns of expression. Gene ontology analysis and pathway analysis can classify differentially expressed genes into different categories based on the GO terms and to identify enriched categories and overrepresented pathways. Classification methods have proven very useful to identify patterns of gene expression that can be correlated with diagnostic classification and for classifying genes according to their functional role. Based on clustering analysis results and other information, data can be interpreted with respect to biological pathways. Certainly, there are other analyses can be done based on experimental design and the scientific questions you are going to address.