Interrelated Two-way Clustering: An Unsupervised Approach for Gene Expression Data Analysis
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This paper presents Fuzzy-Adaptive-Subspace-Iteration-based Two-way Clustering (FASIC) of microarray data for finding differentially expressed genes (DEGs) from two-sample microarray experiments. The concept of fuzzy membership is introduced to transform the hard adaptive subspace iteration (ASI) algorithm into a fuzzy-ASI algorithm to perform two-way clustering. The proposed approach follows a progressive framework to assign a relevance value to genes associated with each cluster. Subsequently, each gene cluster is scored and ranked based on its potential to provide a correct classification of the sample classes. These ranks are converted into P values using the R-test, and the significance of each gene is determined. A fivefold validation is performed on the DEGs selected using the proposed approach. Empirical analyses on a number of simulated microarray data sets are conducted to quantify the results obtained using the proposed approach. To exemplify the efficacy of the proposed approach, further analyses on different real microarray data sets are also performed.