Study of Informative Gene Selection for Tissue Classification Based on Tumor Gene Expression Profiles

TLDR: The authors analyze the Multi-Class tumor gene expression profile dataset, which contains 218 tumor samples spanning 14 common tumor types, as well as 90 normal tissue samples, to find a small subset of genes for distinguishing tumor from normal tissues.

Abstract: Informative gene selection is of great importance in the analysis of microarray expression data because of its huge dimensionality and relatively small samples, and also provides a systemic and promising way to reveal the gene expression patterns of tumors with large scale gene expression profiles. In this paper, the authors analyze the Multi-Class tumor gene expression profile dataset, which contains 218 tumor samples spanning 14 common tumor types, as well as 90 normal tissue samples, to find a small subset of genes for distinguishing tumor from normal tissues. First, a Relief-based feature selection algorithm is applied to create candidate feature subsets and the one with the best classification performance is selected as the informative gene subset for classification. Then, a sensitivity analysis method based on the classifier of support vector machine with RBF kernel is employed to eliminate the redundant genes. As a result, 52 informative genes are selected as markers for making distinctions between different tumor tissues and their normal counterparts, and their expressions are analyzed to explore the tumor gene expression patterns. At the end of this paper, several methods for informative gene selection are also analyzed and compared to validate the feasibility and effectiveness of the method employed in this work.