Data-driven smoothness enhanced variance ratio test to unearth responsive genes in 0-time normalized time-course microarray data

Abstract

Discovering responsive or differentially expressed genes in time-course microarray studies is an important step before further interpretation is carried out. The statistical challenge in this task is due to high prevalence of situations in which the following settings are true: (1) none or insufficiently fewer repeats; (2) 0-time or starting point reference; and, (3) undefined or unknown pattern of response. One simple and effective criterion that comes for rescue is smoothness criterion which assumes that a responsive gene exhibits a smooth pattern of response whereas a non-responsive gene exhibits a non-smooth response. Smoothness of response may be gauranteed if the expression is sufficiently sampled and it can be measured in terms of first order or serial autocorrelation of gene expression time-course using Durbin-Watson (DW) test. But, the DW-test ignores variance of the response which also plays an important role in the discovery of responsive genes while variance alone is not appropriate because of nonuniform noise variance across genes. Hence, we propose a novel Data-driven Smoothness Enhanced Variance Ratio Test (dSEVRaT) which effectively combines smoothness and variance of gene expression time-course. We demonstrate that dSEVRaT does significantly better than DW-test as well as other tests on both simulated data and real data. Further, we demonstrate that dSEVRaT can address both 0-time normalized data and the other data equally well. © Springer-Verlag Berlin Heidelberg 2007.