Variance-based Feature Selection for Classification of Cancer Subtypes Using Gene Expression Data

Classification in cancer has traditionally relied on feature selection by differential expression as a first step, where genes are selected according to the strength of evidence for a consistent difference in expression level between classes. However, recent work has shown that many genes also differ in the variance of their gene expression between disease states, and in particular between cancers of different types, prognosis, or stages of development. Features selected based on increased variance in cancer or differences in variance between tumours of differing prognosis have been used to successfully predict tumour progression or prognosis within the same cancer type, and to classify cancer subtypes in cases where there is an overall increase in variance in one class over the other. Here, we apply feature selection by differential variance to the more general problem of classification of cancer subtypes. We show that classifiers using features selected by differential variance are able to distinguish between clinically relevant cancer subtypes, that these classifiers perform as well as classifiers based on features selected by differential expression, and that combining the two approaches often gives better classification results than either feature selection method alone.