Robust estimation of the false discovery rate

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Robust estimation of the false discovery rate

MOTIVATION Presently available methods that use p-values to estimate or control the false discovery rate (FDR) implicitly assume that p-values are continuously distributed and based on two-sided tests. Therefore, it is difficult to reliably estimate the FDR when p-values are discrete or based on one-sided tests. RESULTS A simple and robust method to estimate the FDR is proposed. The proposed ...

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Improving false discovery rate estimation

MOTIVATION Recent attempts to account for multiple testing in the analysis of microarray data have focused on controlling the false discovery rate (FDR). However, rigorous control of the FDR at a preselected level is often impractical. Consequently, it has been suggested to use the q-value as an estimate of the proportion of false discoveries among a set of significant findings. However, such a...

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False Discovery Rate Estimation in Proteomics.

With the advancement in proteomics separation techniques and improvements in mass analyzers, the data generated in a mass-spectrometry based proteomics experiment is rising exponentially. Such voluminous datasets necessitate automated computational tools for high-throughput data analysis and appropriate statistical control. The data is searched using one or more of the several popular database ...

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The effect of correlation in false discovery rate estimation.

The objective of this paper is to quantify the effect of correlation in false discovery rate analysis. Specifically, we derive approximations for the mean, variance, distribution and quantiles of the standard false discovery rate estimator for arbitrarily correlated data. This is achieved using a negative binomial model for the number of false discoveries, where the parameters are found empiric...

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Estimation of false discovery rate using sequential permutation p-values.

We consider the problem of testing each of m null hypotheses with a sequential permutation procedure in which the number of draws from the permutation distribution of each test statistic is a random variable. Each sequential permutation p-value has a null distribution that is nonuniform on a discrete support. We show how to use a collection of such p-values to estimate the number of true null h...

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ژورنال

عنوان ژورنال: Bioinformatics

سال: 2006

ISSN: 1367-4803,1460-2059

DOI: 10.1093/bioinformatics/btl328