A PSO-Based Approach for Pathway Marker Identification From Gene Expression Data

In this article, a new and robust pathway activity inference scheme is proposed from gene expression data using Particle Swarm Optimization (PSO). From microarray gene expression data, the corresponding pathway information of the genes are collected from a public database. For identifying the pathway markers, the expression values of each pathway consisting of genes, termed as pathway activity, are summarized. To measure the goodness of a pathway activity vector, t-score is widely used in the existing literature. The weakness of existing techniques for inferring pathway activity is that they intend to consider all the member genes of a pathway. But in reality, all the member genes may not be significant to the corresponding pathway. Therefore, those genes, which are responsible in the corresponding pathway, should be included only. Motivated by this, in the proposed method, using PSO, important genes with respect to each pathway are identified. The objective is to maximize the average t-score. For the pathway activities inferred from different percentage of significant pathways, the average absolute t-scores are plotted. In addition, the top 50% pathway markers are evaluated using 10-fold cross validation and its performance is compared with that of other existing techniques. Biological relevance of the results is also studied.