Raking and Selection of Differentially Expressed Genes from Microarray Data

This paper presents adaptive algorithms for ranking and selecting differentially expressed genes from microarray data. A ranking method originally proposed in [1] is adapted and supplemented with Hausdorff distancebased ranking method to improve the performance of the ranking algorithm. A weighted fusion scheme is developed to fuse the ‘mean’ and the Hausdorff distance-based ranking methods to develop a robust ranking method. The normalized consistency measure is used as the weight for the fusion of ranking methods. An adaptive subspace iteration (ASI) based selection algorithm is then applied on top ranked genes to select highly differentially expressed genes. To illustrate the utility of the proposed algorithms, a number of empirical analyses were conducted on both the simulated (400 simulated microarray dataset) and real microarray datasets (colon cancer dataset, gastric cancer dataset). From the empirical analysis it was observed that the proposed unified approach is robust against initialization and yields consistent selection of differentially expressed genes. Key-Words: Adaptive Sub-space Iteration, Clustering, Ranking, Differentially Expressed Genes and Microarray Data Analysis.