Functional grouping of yeast genes via biclustering microarray data.

Biclustering algorithm on Gibbs sampling strategy is a recruit in the field of the analysis of gene expression data of microarray experiments. Its feasibility and validity still need to be researched not only for synthetic datasets but also for real datasets. Here we investigated a biclustering algorithm on a microarray dataset of Yeast genome through building a database for storing microarray datasets and MIPS data, and running the scripts on Matlab platform to discover gene patterns. In contrast with standard clusterings that reveal genes behaving similarly over all the conditions, biclustering groups genes over only a subset of conditions for which those genes have a sharp probability distribution. It has the key advantage of providing a transparent probabilistic interpretation of the biclusters. Its basic strategy of Gibbs sampling does not suffer from the problem of local minima that often characterizes expectation maximization, so that the patterns should be more global and accurate. Also we tested it with the known explanation of genes in MIPS, objectively to demonstrate the effectiveness and deficiencies of biclustering approach, and the functions of a few unknown ORFs in some bicluster can be deduced in the present research. In addition, the result of similarity searching in Blast-Search can be an assistant evidence for its effectivity.