Journal or Book Title
The field of microarray data analysis is shifting emphasis from methods for identifying differentially expressed genes to methods for identifying differentially expressed gene categories. The latter approaches utilize a priori information about genes to group genes into categories and enhance the interpretation of experiments aimed at identifying expression differences across treatments. While almost all of the existing approaches for identifying differentially expressed gene categories are practically useful, they suffer from a variety of drawbacks. Perhaps most notably, many popular tools are based exclusively on gene-specific statistics that cannot detect many types of multivariate expression change. We have developed a nonparametric multivariate method for identifying gene categories whose multivariate expression distribution differs across two or more conditions. We illustrate our approach and compare its performance to several existing procedures via the analysis of a real data set and a unique data-based simulation study designed to capture the challenges and complexities of practical data analysis. We show that our method has good power for differentiating between differentially expressed and nondifferentially expressed gene categories, and we utilize a resampling based strategy for controling the false discovery rate when testing multiple categories.
This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Dan Nettleton, Justin Recknor an dJames M. Reecy
Nettleton, Dan; Recknor, Justin; and Reecy, James M., "Identification of Differentially Expressed Gene Categories in Microarray Studies Using Nonparametric Multivariate Analysis" (2008). Animal Science Publications. 133.