Detection of differentially expressed genes using feature selection approach from RNA-seq

With the advance of next generation sequencing technology, RNA-seq is widely being used for transcriptomics as an alternative for microarray. RNA-seq has a dynamic range of applications such as gene expression quantification, alternative splicing identification, and novel transcript discovery. Generally, the primary aim of RNA-seq analysis is to detect differentially expressed genes in different biological conditions. From the data mining point of view, discovering differentially expressed genes can be seen as a feature selection problem that identifies most significant genes for discriminating different biological conditions. Feature selection methods for differential analysis in microarray data are well established in the literature but there are few studies on feature selection in RNA-seq experiments. In this paper, we propose to apply feature selection method in data mining for differential expression analysis. Symmetrical uncertainty is used to rank the genes and significant genes are selected based on a pre-defined relevance threshold. To evaluate the proposed method, we conducted a simulation study to assess the performance in terms of the true and false positive rates. The experimental results demonstrated that feature selection strategy can be applied for differential analysis in RNA-seq and outperformed the existing statistical approaches.