The Experts below are selected from a list of 327 Experts worldwide ranked by ideXlab platform
Lei Sun - One of the best experts on this subject based on the ideXlab platform.
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The multiplicity problem in linkage analysis of gene expression data - the power of differentiating cis- and trans-acting regulators.
BMC proceedings, 2007Co-Authors: Baisong Huang, Jagadish Rangrej, Andrew D Paterson, Lei SunAbstract:In this report, we focused on the multiplicity issue in Problem 1 of Genetic Analysis Workshop 15. We investigated and compared the performance of the stratified false-discovery rate control method with the traditional aggregated approach, in an application to genome-wide linkage analyses of single-nucleotide polymorphism-to-gene expression data. We showed the importance of utilizing the available map information and demonstrated the power gained by conducting false-discovery rate control separately for cis and trans regulators under three different frameworks: fixed Rejection Region, fixed false-discovery rate, and fixed number of Rejections.
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The multiplicity problem in linkage analysis of gene expression data – the power of differentiating cis- and trans-acting regulators
BMC Proceedings, 2007Co-Authors: Baisong Huang, Jagadish Rangrej, Andrew D Paterson, Lei SunAbstract:In this report, we focused on the multiplicity issue in Problem 1 of Genetic Analysis Workshop 15. We investigated and compared the performance of the stratified false-discovery rate control method with the traditional aggregated approach, in an application to genome-wide linkage analyses of single-nucleotide polymorphism-to-gene expression data. We showed the importance of utilizing the available map information and demonstrated the power gained by conducting false-discovery rate control separately for cis and trans regulators under three different frameworks: fixed Rejection Region, fixed false-discovery rate, and fixed number of Rejections.
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Stratified false discovery control for large‐scale hypothesis testing with application to genome‐wide association studies
Genetic epidemiology, 2006Co-Authors: Lei Sun, Andrew D Paterson, Radu V. Craiu, Shelley B. BullAbstract:The multiplicity problem has become increasingly important in genetic studies as the capacity for high-throughput genotyping has increased. The control of False Discovery Rate (FDR) (Benjamini and Hochberg. [1995] J. R. Stat. Soc. Ser. B 57:289-300) has been adopted to address the problems of false positive control and low power inherent in high-volume genome-wide linkage and association studies. In many genetic studies, there is often a natural stratification of the m hypotheses to be tested. Given the FDR framework and the presence of such stratification, we investigate the performance of a stratified false discovery control approach (i.e. control or estimate FDR separately for each stratum) and compare it to the aggregated method (i.e. consider all hypotheses in a single stratum). Under the fixed Rejection Region framework (i.e. reject all hypotheses with unadjusted p-values less than a pre-specified level and then estimate FDR), we demonstrate that the aggregated FDR is a weighted average of the stratum-specific FDRs. Under the fixed FDR framework (i.e. reject as many hypotheses as possible and meanwhile control FDR at a pre-specified level), we specify a condition necessary for the expected total number of true positives under the stratified FDR method to be equal to or greater than that obtained from the aggregated FDR method. Application to a recent Genome-Wide Association (GWA) study by Maraganore et al. ([2005] Am. J. Hum. Genet. 77:685-693) illustrates the potential advantages of control or estimation of FDR by stratum. Our analyses also show that controlling FDR at a low rate, e.g. 5% or 10%, may not be feasible for some GWA studies.
Gregory Nuel - One of the best experts on this subject based on the ideXlab platform.
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Kerfdr: a semi-parametric kernel-based approach to local false discovery rate estimation
BMC bioinformatics, 2009Co-Authors: Mickael Guedj, Stéphane Robin, Alain Celisse, Gregory NuelAbstract:Background The use of current high-throughput genetic, genomic and post-genomic data leads to the simultaneous evaluation of a large number of statistical hypothesis and, at the same time, to the multiple-testing problem. As an alternative to the too conservative Family-Wise Error-Rate (FWER), the False Discovery Rate (FDR) has appeared for the last ten years as more appropriate to handle this problem. However one drawback of FDR is related to a given Rejection Region for the considered statistics, attributing the same value to those that are close to the boundary and those that are not. As a result, the local FDR has been recently proposed to quantify the specific probability for a given null hypothesis to be true.
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Kerfdr: a semi-parametric kernel-based approach to local false discovery rate estimation
BMC Bioinformatics, 2009Co-Authors: Mickael Guedj, Stéphane Robin, Alain Celisse, Gregory NuelAbstract:Background The use of current high-throughput genetic, genomic and post-genomic data leads to the simultaneous evaluation of a large number of statistical hypothesis and, at the same time, to the multiple-testing problem. As an alternative to the too conservative Family-Wise Error-Rate (FWER), the False Discovery Rate (FDR) has appeared for the last ten years as more appropriate to handle this problem. However one drawback of FDR is related to a given Rejection Region for the considered statistics, attributing the same value to those that are close to the boundary and those that are not. As a result, the local FDR has been recently proposed to quantify the specific probability for a given null hypothesis to be true. Results In this context we present a semi-parametric approach based on kernel estimators which is applied to different high-throughput biological data such as patterns in DNA sequences, genes expression and genome-wide association studies. Conclusion The proposed method has the practical advantages, over existing approaches, to consider complex heterogeneities in the alternative hypothesis, to take into account prior information (from an expert judgment or previous studies) by allowing a semi-supervised mode, and to deal with truncated distributions such as those obtained in Monte-Carlo simulations. This method has been implemented and is available through the R package kerfdr via the CRAN or at http://stat.genopole.cnrs.fr/software/kerfdr
Mickael Guedj - One of the best experts on this subject based on the ideXlab platform.
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Kerfdr: a semi-parametric kernel-based approach to local false discovery rate estimation
BMC bioinformatics, 2009Co-Authors: Mickael Guedj, Stéphane Robin, Alain Celisse, Gregory NuelAbstract:Background The use of current high-throughput genetic, genomic and post-genomic data leads to the simultaneous evaluation of a large number of statistical hypothesis and, at the same time, to the multiple-testing problem. As an alternative to the too conservative Family-Wise Error-Rate (FWER), the False Discovery Rate (FDR) has appeared for the last ten years as more appropriate to handle this problem. However one drawback of FDR is related to a given Rejection Region for the considered statistics, attributing the same value to those that are close to the boundary and those that are not. As a result, the local FDR has been recently proposed to quantify the specific probability for a given null hypothesis to be true.
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Kerfdr: a semi-parametric kernel-based approach to local false discovery rate estimation
BMC Bioinformatics, 2009Co-Authors: Mickael Guedj, Stéphane Robin, Alain Celisse, Gregory NuelAbstract:Background The use of current high-throughput genetic, genomic and post-genomic data leads to the simultaneous evaluation of a large number of statistical hypothesis and, at the same time, to the multiple-testing problem. As an alternative to the too conservative Family-Wise Error-Rate (FWER), the False Discovery Rate (FDR) has appeared for the last ten years as more appropriate to handle this problem. However one drawback of FDR is related to a given Rejection Region for the considered statistics, attributing the same value to those that are close to the boundary and those that are not. As a result, the local FDR has been recently proposed to quantify the specific probability for a given null hypothesis to be true. Results In this context we present a semi-parametric approach based on kernel estimators which is applied to different high-throughput biological data such as patterns in DNA sequences, genes expression and genome-wide association studies. Conclusion The proposed method has the practical advantages, over existing approaches, to consider complex heterogeneities in the alternative hypothesis, to take into account prior information (from an expert judgment or previous studies) by allowing a semi-supervised mode, and to deal with truncated distributions such as those obtained in Monte-Carlo simulations. This method has been implemented and is available through the R package kerfdr via the CRAN or at http://stat.genopole.cnrs.fr/software/kerfdr
Baisong Huang - One of the best experts on this subject based on the ideXlab platform.
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The multiplicity problem in linkage analysis of gene expression data - the power of differentiating cis- and trans-acting regulators.
BMC proceedings, 2007Co-Authors: Baisong Huang, Jagadish Rangrej, Andrew D Paterson, Lei SunAbstract:In this report, we focused on the multiplicity issue in Problem 1 of Genetic Analysis Workshop 15. We investigated and compared the performance of the stratified false-discovery rate control method with the traditional aggregated approach, in an application to genome-wide linkage analyses of single-nucleotide polymorphism-to-gene expression data. We showed the importance of utilizing the available map information and demonstrated the power gained by conducting false-discovery rate control separately for cis and trans regulators under three different frameworks: fixed Rejection Region, fixed false-discovery rate, and fixed number of Rejections.
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The multiplicity problem in linkage analysis of gene expression data – the power of differentiating cis- and trans-acting regulators
BMC Proceedings, 2007Co-Authors: Baisong Huang, Jagadish Rangrej, Andrew D Paterson, Lei SunAbstract:In this report, we focused on the multiplicity issue in Problem 1 of Genetic Analysis Workshop 15. We investigated and compared the performance of the stratified false-discovery rate control method with the traditional aggregated approach, in an application to genome-wide linkage analyses of single-nucleotide polymorphism-to-gene expression data. We showed the importance of utilizing the available map information and demonstrated the power gained by conducting false-discovery rate control separately for cis and trans regulators under three different frameworks: fixed Rejection Region, fixed false-discovery rate, and fixed number of Rejections.
Andrew D Paterson - One of the best experts on this subject based on the ideXlab platform.
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The multiplicity problem in linkage analysis of gene expression data - the power of differentiating cis- and trans-acting regulators.
BMC proceedings, 2007Co-Authors: Baisong Huang, Jagadish Rangrej, Andrew D Paterson, Lei SunAbstract:In this report, we focused on the multiplicity issue in Problem 1 of Genetic Analysis Workshop 15. We investigated and compared the performance of the stratified false-discovery rate control method with the traditional aggregated approach, in an application to genome-wide linkage analyses of single-nucleotide polymorphism-to-gene expression data. We showed the importance of utilizing the available map information and demonstrated the power gained by conducting false-discovery rate control separately for cis and trans regulators under three different frameworks: fixed Rejection Region, fixed false-discovery rate, and fixed number of Rejections.
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The multiplicity problem in linkage analysis of gene expression data – the power of differentiating cis- and trans-acting regulators
BMC Proceedings, 2007Co-Authors: Baisong Huang, Jagadish Rangrej, Andrew D Paterson, Lei SunAbstract:In this report, we focused on the multiplicity issue in Problem 1 of Genetic Analysis Workshop 15. We investigated and compared the performance of the stratified false-discovery rate control method with the traditional aggregated approach, in an application to genome-wide linkage analyses of single-nucleotide polymorphism-to-gene expression data. We showed the importance of utilizing the available map information and demonstrated the power gained by conducting false-discovery rate control separately for cis and trans regulators under three different frameworks: fixed Rejection Region, fixed false-discovery rate, and fixed number of Rejections.
-
Stratified false discovery control for large‐scale hypothesis testing with application to genome‐wide association studies
Genetic epidemiology, 2006Co-Authors: Lei Sun, Andrew D Paterson, Radu V. Craiu, Shelley B. BullAbstract:The multiplicity problem has become increasingly important in genetic studies as the capacity for high-throughput genotyping has increased. The control of False Discovery Rate (FDR) (Benjamini and Hochberg. [1995] J. R. Stat. Soc. Ser. B 57:289-300) has been adopted to address the problems of false positive control and low power inherent in high-volume genome-wide linkage and association studies. In many genetic studies, there is often a natural stratification of the m hypotheses to be tested. Given the FDR framework and the presence of such stratification, we investigate the performance of a stratified false discovery control approach (i.e. control or estimate FDR separately for each stratum) and compare it to the aggregated method (i.e. consider all hypotheses in a single stratum). Under the fixed Rejection Region framework (i.e. reject all hypotheses with unadjusted p-values less than a pre-specified level and then estimate FDR), we demonstrate that the aggregated FDR is a weighted average of the stratum-specific FDRs. Under the fixed FDR framework (i.e. reject as many hypotheses as possible and meanwhile control FDR at a pre-specified level), we specify a condition necessary for the expected total number of true positives under the stratified FDR method to be equal to or greater than that obtained from the aggregated FDR method. Application to a recent Genome-Wide Association (GWA) study by Maraganore et al. ([2005] Am. J. Hum. Genet. 77:685-693) illustrates the potential advantages of control or estimation of FDR by stratum. Our analyses also show that controlling FDR at a low rate, e.g. 5% or 10%, may not be feasible for some GWA studies.