A Unified Framework To Find Differentially Expressed Genes from Microarray Experiments

This paper presents a unified framework for finding differentially expressed genes (DEGs) from the two-sample microarray data. The proposed framework has three interrelated modules viz. (i) two-way clustering, ii) adaptive ranking and iii) visualization. The first module uses a progressive clustering technique to functionally classify the marker genes as well as finding the DEGs and the second module yields a list of DEGs ranked based on statistical significance. A weighted scheme is employed to fuse the two-way clustering and ranking modules to find DEGs. A visualization module is added to validate the results. Empirical analyses on 50 artificially generated microarray datasets and 2 cancer datasets show that the unified framework performs better in finding DEGs when compared to reported results on the same datasets.