Identifying differentially expressed genes using false discovery rate controlling procedures

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Identifying differentially expressed genes using false discovery rate controlling procedures (2003)

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by Anat Reiner , Daniel Yekutieli , Yoav Benjamini

Venue:	BIOINFORMATICS 19: 368–375
Citations:	231 - 2 self

BibTeX

@MISC{Reiner03identifyingdifferentially,
    author = {Anat Reiner and Daniel Yekutieli and Yoav Benjamini},
    title = {Identifying differentially expressed genes using false discovery rate controlling procedures},
    year = {2003}
}

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Abstract

Motivation: 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.

Keyphrases

false discovery rate test statistic linear step-up procedure sophisticated algorithm gene co-regulation dna microarrays resampling-based fdr controlling procedure na ve test statistic distribution tested gene measurement error desired level family-wise error rate dependency problem false identification expression level joint distribution simulated microarray data na ve application resampling-based procedure fdr control large multiplicity problem comparative simulation analysis marginal distribution gene expression level

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