2011Chongqing Yike Daxue xuebaoRequires access

False discovery rate and its extension and application

Huashuo Zhao

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Abstract

Objective:To introduce the multiple hypothesis testing based on the false discovery rate and its extension to q value and local false discovery rate.Methods:False discovery rate,q value and local false discovery rate were applied to the analysis of differentially expressed genes concerning a prostate cancer microarray data to control and estimate the false discovery rate.Results:It identified 60 differentially expressed genes with the procedure of Benjamini and Hochberg when controlling the false discovery rate below 0.10,74 genes with q value less than 0.10 and 31 genes with local false discovery rate less than 0.10.Conclusion:We can control and estimate false discovery rate simultaneously in multiple comparisons of high-dimensional data.It can provide more information for analysis to combine false discovery rate,q value and local false discovery rate.

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What this paper is about

Objective:To introduce the multiple hypothesis testing based on the false discovery rate and its extension to q value and local false discovery rate.Methods:False discovery rate,q value and local false discovery rate were applied to the analysis of differentially expressed genes concerning a prostate cancer microarray data to control and estimate the false discovery rate.Results:It identified 60 differentially expressed genes with the procedure of Benjamini and Hochberg when controlling the false discovery rate below 0.10,74 genes with q value less than 0.10 and 31 genes with local false discovery rate less than 0.10.Conclusion:We can control and estimate false discovery rate simultaneously in multiple comparisons of high-dimensional data.It can provide more information for analysis to combine false discovery rate,q value and local false discovery rate.

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Available abstract

Objective:To introduce the multiple hypothesis testing based on the false discovery rate and its extension to q value and local false discovery rate.Methods:False discovery rate,q value and local false discovery rate were applied to the analysis of differentially expressed genes concerning a prostate cancer microarray data to control and estimate the false discovery rate.Results:It identified 60 differentially expressed genes with the procedure of Benjamini and Hochberg when controlling the false discovery rate below 0.10,74 genes with q value less than 0.10 and 31 genes with local false discovery rate less than 0.10.Conclusion:We can control and estimate false discovery rate simultaneously in multiple comparisons of high-dimensional data.It can provide more information for analysis to combine false discovery rate,q value and local false discovery rate.

Key concepts: False discovery rate, Multiple comparisons problem, False positive rate, Drug discovery, Value (mathematics), Computer science, Computational biology, Data mining

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