Biclustering using Parallel Fuzzy Approach for Analysis of Microarray Gene Expression Data

摘要

Biclusters are required to analyzing gene expression patterns of genes comparing rows in expression profiles and analyzing expression profiles of samples by comparing columns in gene expression matrix. In the process of biclustering we need to cluster genes and samples. The algorithm presented in this paper is based upon the two-way clustering approach in which the genes and samples are clustered using parallel fuzzy C-means clustering using message passing interface, we call it MFCM. MFCM applied for clustering on genes and samples which maximize membership function values of the data set. It is a parallelized rework of a parallel fuzzy two-way clustering algorithm for microarray gene expression data [9], to study the efficiency and parallelization improvement of the algorithm. The algorithm uses gene entropy measure to filter the clustered data to find biclusters. The method is able to get highly correlated biclusters of the gene expression dataset.We have implemented the algorithm of fuzzy c-means in MATLAB parallel computing platform using MATLABMPI (Message Passing Version of MATLAB). This approach is used to find biclusters of gene expression matrices. The biclustering method is also parallelized to reduce the gene centers with lower entropy filter function. By this function we choose the gene cluster centers with minimum entropy. The algorithm is tested on well-known cell cycle of the budding yeast S. cerevisiae by Cho et al. and Tavazoi et.al data sets, breast cancer subtypes Basal A, Basal B and Leukemia from Golub et al.