Identification of genomic markers correlated with sensitivity in solid tumors to Dasatinib using sparse principal components

Background: Differential analysis techniques are commonly used to offer scientists a dimension reduction procedure and an interpretable gateway to variable selection, especially when confronting high-dimensional genomic data. Huang et al. used a gene expression profile of breast cancer cell lines to identify genomic markers which are highly correlated with in vitro sensitivity of a drug Dasatinib. They considered three statistical methods to identify differentially expressed genes and finally used the results from the intersection. But the statistical methods that are used in the paper are not sufficient to select the genomic markers.
Methods: In this paper we used three alternative statistical methods to select a combined list of genomic markers and compared the genes that were proposed by Huang et al. We then proposed to use sparse principal component analysis (PCA) to identify a final list of genomic markers. The sparse PCA incorporates correlation into account among the genes and helps to draw a successful genomic markers discovery.
Results: We present a new and a small set of genomic markers to separate out the groups of patients effectively who are sensitive to the drug Dasatinib. The analysis procedure will also encourage scientists in identifying genomic markers that can help to separate out two groups.

This is an Accepted Manuscript of an article published by Taylor & Francis in Journal of Applied Statistics on 12/02/16, available online: http://www.tandfonline.com/10.1080/02664763.2016.1142941