Biological analysis of microarray data using orthogonal forward selection with a clustering approach

DNA microarray technology allows researchers to monitor the expression level of thousands of genes under various conditions in microarray experiments. However, high-dimensional data in microarray is a major challenge as the irrelevant genes often reduce the detection capability and increase the computation time. Many learning algorithms are not specifically developed to deal with the noisy genes, thus, incorporating them with gene selection techniques has become a necessity. In this paper, we propose a combined method of Gram-Schmidt orthogonal forward selection (OFS) and FunCluster to search for putatively co-regulated biological processes that share the co-expressed genes. There were two datasets used in this research: human white adipose tissue and human skeletal muscle. This study aimed to find a small subset of strongly correlated genes from the raw datasets to maximize the detection capability of cluster analysis. This method was found able to detect the clusters of biological categories that were overlooked in the previous research. Some clusters represented minor functions of the datasets and indicated more specific biological processes. Further, the computation time for both datasets was reduced using this proposed method, as the Gram-Schmidt OFS significantly reduced the dimensionality of the datasets.