Identifying differentially expressed genes using false discovery rate controlling procedures.
BACKGROUND
DNA microarrays have recently been used for the purpose of monitoring expression levels of thousands of genes simultaneously and identifying those genes that are differentially expressed. The probability that a false identification (type I error) is committed can increase sharply when the number of tested genes gets large. Correlation between the test statistics attributed to gene co-regulation and dependency in the measurement errors of the gene expression levels further complicates the problem. In this paper we address this very large multiplicity problem by adopting the false discovery rate (FDR) controlling approach. In order to address the dependency problem, we present three resampling-based FDR controlling procedures, that account for the test statistics distribution, and compare their performance to that of the naïve application of the linear step-up procedure in Benjamini and Hochberg (1995). The procedures are studied using simulated microarray data, and their performance is examined relative to their ease of implementation.


RESULTS
Comparative simulation analysis shows that all four FDR controlling procedures control the FDR at the desired level, and retain substantially more power then the family-wise error rate controlling procedures. In terms of power, using resampling of the marginal distribution of each test statistics substantially improves the performance over the naïve one. The highest power is achieved, at the expense of a more sophisticated algorithm, by the resampling-based procedures that resample the joint distribution of the test statistics and estimate the level of FDR control.


BACKGROUND
An R program that adjusts p-values using FDR controlling procedures is freely available over the Internet at www.math.tau.ac.il/~ybenja.
Reiner
Anat
A
Department of Statistics and Operations Research, The Sackler Faculty of Exact Sciences, Tel-Aviv University, Tel-Aviv 69978, Israel. anatr@post.tau.ac.il
Yekutieli
Daniel
D
Benjamini
Yoav
Y
D000465:Algorithms; D000818:Animals; D008076:Cholesterol, HDL / Q000235:genetics / Q000378:metabolism; D003198:Computer Simulation; D018076:DNA, Complementary / Q000235:genetics; D020869:Gene Expression Profiling / Q000379:methods*; D005786:Gene Expression Regulation; D008099:Liver / Q000378:metabolism; D051379:Mice; D008810:Mice, Inbred C57BL; D008957:Models, Genetic; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D015203:Reproducibility of Results; D018401:Sample Size
D008076:Cholesterol, HDL; D018076:DNA, Complementary