Authors: Shreya Mathur; Sunil Mathur
Addresses: Division of Outreach and Continuing Education, University of Mississippi, Oxford, MS 38655, USA ' Division of Epidemiology, Biostatistics, and Environmental Health, School of Public Health, University of Memphis, Memphis, TN 38152, USA
Abstract: DNA microarray technology can simultaneously screen thousands of gene expression profiles, transforming how genetics is applied in medicine. However, the lack of normality in microarray data renders common statistical methods ineffective. We propose a novel statistical method which does not require stringent assumptions but is still more powerful than some of its competitors. Using both simulation studies and clinical data, we show that our novel method outperforms previous methods. The limiting distribution for the proposed test is obtained for under null and alternative hypotheses. The proposed test will help make cancer treatment and gene therapy more successful, and it may facilitate research regarding cancer vaccinations. The proposed test may also help in the development of a prediction model in genetic profiling studies built on a subset of differentially expressed genes and the clinical data to assess the accuracy of the clinical prediction.
Keywords: cancer testing; location; differentially expressed genes; type I error; cancer detection; cancer genes; DNA microarray data; gene expression data; bioinformatics; simulation; cancer treatment; gene therapy; genetic profiling.
International Journal of Bioinformatics Research and Applications, 2014 Vol.10 No.6, pp.628 - 646
Available online: 20 Oct 2014 *Full-text access for editors Access for subscribers Purchase this article Comment on this article