May 19, 2007

Re-sampling strategy to improve the estimation of number of null hypotheses in FDR control under strong correlation structures

BMC Bioinformatics
Xin Lu, David L Perkins


When conducting multiple hypothesis tests, it is important to control the number of false positives, or the False Discovery Rate (FDR). However, there is a tradeoff between controlling FDR and maximizing power. Several methods have been proposed, such as the q-value method, to estimate the proportion of true null hypothesis among the tested hypotheses, and use this estimation in the control of FDR. These methods usually depend on the assumption that the test statistics are independent (or only weakly correlated). However, many types of data, for example microarray data, often contain large scale correlation structures. Our objective was to develop methods to control the FDR while maintaining a greater level of power in highly correlated datasets by improving the estimation of the proportion of null hypotheses. We showed that when strong correlation exists among the data, which is common in microarray datasets, the estimation of the proportion of null hypotheses could be highly variable resulting in a high level of variation in the FDR. Therefore, we developed a re-sampling strategy to reduce the variation by breaking the correlations between gene expression values, then using a conservative strategy of selecting the upper quart...Continue Reading

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Mentioned in this Paper

Microarray Analysis
Estrogen receptor alpha, human
Nucleic Acid Hybridization Procedure
Gene Expression
ESR1 gene
Data Interpretation, Statistical
Sample Size
Mimic brand of tebufenozide
Estrogen Receptors
False Positive Reactions

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