title: Finding differentially expressed genes in high dimensional data: Rank based test statistic via a distance measure.
abstract: We present a rank-based test statistic for the identification of differentially expressed genes using a distance measure. The proposed test statistic is highly robust against extreme values and does not assume the distribution of parent population. Simulation studies show that the proposed test is more powerful than some of the commonly used methods, such as paired t-test, Wilcoxon signed rank test, and significance analysis of microarray (SAM) under certain non-normal distributions. The asymptotic distribution of the test statistic, and the p-value function are discussed. The application of proposed method is shown using a real-life data set.
authors.affiliation: Department of Mathematics, University of Mississippi, USA skmathur@olemiss.edu.
authors.forename: Sunil
authors.initials: S
authors.lastname: Mathur
authors.affiliation: Department of Chemical Engineering, University of Mississippi, USA.
authors.forename: Ajit
authors.initials: A
authors.lastname: Sadana
mesh_terms: D015870:Gene Expression / Q000235:genetics; D020022:Genetic Predisposition to Disease / Q000235:genetics; D059014:High-Throughput Nucleotide Sequencing; D006801:Humans; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis; D013223:Statistics as Topic; D018709:Statistics, Nonparametric