title: Assessing numerical dependence in gene expression summaries with the jackknife expression difference.
abstract: Statistical methods to test for differential expression traditionally assume that each gene's expression summaries are independent across arrays. When certain preprocessing methods are used to obtain those summaries, this assumption is not necessarily true. In general, the erroneous assumption of dependence results in a loss of statistical power. We introduce a diagnostic measure of numerical dependence for gene expression summaries from any preprocessing method and discuss the relative performance of several common preprocessing methods with respect to this measure. Some common preprocessing methods introduce non-trivial levels of numerical dependence. The issue of (between-array) dependence has received little if any attention in the literature, and researchers working with gene expression data should not take such properties for granted, or they risk unnecessarily losing statistical power.
authors.affiliation: Department of Mathematics and Statistics, Center for Integrated Biosystems, Utah State University, Logan, Utah, United States of America. john.r.stevens@usu.edu
authors.forename: John R
authors.initials: JR
authors.lastname: Stevens
authors.forename: Gabriel
authors.initials: G
authors.lastname: Nicholas
mesh_terms: D000465:Algorithms; D003627:Data Interpretation, Statistical; D057225:Data Mining / Q000379:methods; D020869:Gene Expression Profiling / Q000379:methods*; D005786:Gene Expression Regulation; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods