Abstract
BackgroundThousands of genes in a genomewide data set are tested against some null hypothesis, for detecting differentially expressed genes in microarray experiments. The expected proportion of false positive genes in a set of genes, called the False Discovery Rate (FDR), has been proposed to measure the statistical significance of this set. Various procedures exist for controlling the FDR. However the threshold (generally 5%) is arbitrary and a specific measure associated with each gene would be worthwhile.ResultsUsing process intensity estimation methods, we define and give estimates of the local FDR, which may be considered as the probability for a gene to be a false positive. After a global assessment rule controlling the false positive error, the local FDR is a valuable guideline for deciding wether a gene is differentially expressed. The interest of the method is illustrated on three well known data sets. A R routine for computing local FDR estimates from p-values is available at .ConclusionsThe local FDR associated with each gene measures the probability that it is a false positive. It gives the opportunity to compute the FDR of any given group of clones (of the same gene) or genes pertaining to the same regulation network or the same chromosomic region.
Highlights
Thousands of genes in a genomewide data set are tested against some null hypothesis, for detecting differentially expressed genes in microarray experiments
Let the local False Discovery Rate (FDR) be the probability that a given gene is not differentially expressed
A raw local FDR estimate is defined in a first step
Summary
Thousands of genes in a genomewide data set are tested against some null hypothesis, for detecting differentially expressed genes in microarray experiments. When thousands of genes in a microarray data set are evaluated simultaneously by fold changes or significance tests approach, multiple testing problems immediately arise and lead to many false positive genes. In this 'one-by-one gene' approach the probability of detecting false positives rises sharply. The False Discovery Rate (FDR), is defined as the expected fraction of false rejections among those hypotheses rejected In their seminal paper Benjamini & Hochberg [1] provided a distribution free procedure (BH) for choosing a threshold on p-values that guarantees that the FDR is less than a target level α. The same paper demonstrated that (page number not for citation purposes)
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