Computing Power and Sample Size for the False Discovery Rate in Multiple Applications

The false discovery rate (FDR) is a widely used metric of statistical significance for genomic data analyses that involve multiple hypothesis testing. Power and sample size considerations are important in planning studies that perform these types of genomic data analyses. Here, we propose a three-re...
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Autor*in:	Yonghui Ni [verfasserIn] Anna Eames Seffernick [verfasserIn] Arzu Onar-Thomas [verfasserIn] Stanley B. Pounds [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2024

Schlagwörter:	false discovery rate power sample size multiple testing proportion of true null hypotheses

Übergeordnetes Werk:	In: Genes - MDPI AG, 2010, 15(2024), 3, p 344
Übergeordnetes Werk:	volume:15 ; year:2024 ; number:3, p 344

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/genes15030344

Katalog-ID:	DOAJ100501648

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spelling	10.3390/genes15030344 doi (DE-627)DOAJ100501648 (DE-599)DOAJ298b4d368a384ca1b6cce69fe8aa8fe3 DE-627 ger DE-627 rakwb eng QH426-470 Yonghui Ni verfasserin aut Computing Power and Sample Size for the False Discovery Rate in Multiple Applications 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The false discovery rate (FDR) is a widely used metric of statistical significance for genomic data analyses that involve multiple hypothesis testing. Power and sample size considerations are important in planning studies that perform these types of genomic data analyses. Here, we propose a three-rectangle approximation of a <i<p</i<-value histogram to derive a formula to compute the statistical power and sample size for analyses that involve the FDR. We also introduce the R package <i<FDRsamplesize2</i<, which incorporates these and other power calculation formulas to compute power for a broad variety of studies not covered by other FDR power calculation software. A few illustrative examples are provided. The <i<FDRsamplesize2</i< package is available on CRAN. false discovery rate power sample size multiple testing proportion of true null hypotheses Genetics Anna Eames Seffernick verfasserin aut Arzu Onar-Thomas verfasserin aut Stanley B. Pounds verfasserin aut In Genes MDPI AG, 2010 15(2024), 3, p 344 (DE-627)614096537 (DE-600)2527218-4 20734425 nnns volume:15 year:2024 number:3, p 344 https://doi.org/10.3390/genes15030344 kostenfrei https://doaj.org/article/298b4d368a384ca1b6cce69fe8aa8fe3 kostenfrei https://www.mdpi.com/2073-4425/15/3/344 kostenfrei https://doaj.org/toc/2073-4425 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2024 3, p 344
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allfieldsGer	10.3390/genes15030344 doi (DE-627)DOAJ100501648 (DE-599)DOAJ298b4d368a384ca1b6cce69fe8aa8fe3 DE-627 ger DE-627 rakwb eng QH426-470 Yonghui Ni verfasserin aut Computing Power and Sample Size for the False Discovery Rate in Multiple Applications 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The false discovery rate (FDR) is a widely used metric of statistical significance for genomic data analyses that involve multiple hypothesis testing. Power and sample size considerations are important in planning studies that perform these types of genomic data analyses. Here, we propose a three-rectangle approximation of a <i<p</i<-value histogram to derive a formula to compute the statistical power and sample size for analyses that involve the FDR. We also introduce the R package <i<FDRsamplesize2</i<, which incorporates these and other power calculation formulas to compute power for a broad variety of studies not covered by other FDR power calculation software. A few illustrative examples are provided. The <i<FDRsamplesize2</i< package is available on CRAN. false discovery rate power sample size multiple testing proportion of true null hypotheses Genetics Anna Eames Seffernick verfasserin aut Arzu Onar-Thomas verfasserin aut Stanley B. Pounds verfasserin aut In Genes MDPI AG, 2010 15(2024), 3, p 344 (DE-627)614096537 (DE-600)2527218-4 20734425 nnns volume:15 year:2024 number:3, p 344 https://doi.org/10.3390/genes15030344 kostenfrei https://doaj.org/article/298b4d368a384ca1b6cce69fe8aa8fe3 kostenfrei https://www.mdpi.com/2073-4425/15/3/344 kostenfrei https://doaj.org/toc/2073-4425 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2024 3, p 344
allfieldsSound	10.3390/genes15030344 doi (DE-627)DOAJ100501648 (DE-599)DOAJ298b4d368a384ca1b6cce69fe8aa8fe3 DE-627 ger DE-627 rakwb eng QH426-470 Yonghui Ni verfasserin aut Computing Power and Sample Size for the False Discovery Rate in Multiple Applications 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The false discovery rate (FDR) is a widely used metric of statistical significance for genomic data analyses that involve multiple hypothesis testing. Power and sample size considerations are important in planning studies that perform these types of genomic data analyses. Here, we propose a three-rectangle approximation of a <i<p</i<-value histogram to derive a formula to compute the statistical power and sample size for analyses that involve the FDR. We also introduce the R package <i<FDRsamplesize2</i<, which incorporates these and other power calculation formulas to compute power for a broad variety of studies not covered by other FDR power calculation software. A few illustrative examples are provided. The <i<FDRsamplesize2</i< package is available on CRAN. false discovery rate power sample size multiple testing proportion of true null hypotheses Genetics Anna Eames Seffernick verfasserin aut Arzu Onar-Thomas verfasserin aut Stanley B. Pounds verfasserin aut In Genes MDPI AG, 2010 15(2024), 3, p 344 (DE-627)614096537 (DE-600)2527218-4 20734425 nnns volume:15 year:2024 number:3, p 344 https://doi.org/10.3390/genes15030344 kostenfrei https://doaj.org/article/298b4d368a384ca1b6cce69fe8aa8fe3 kostenfrei https://www.mdpi.com/2073-4425/15/3/344 kostenfrei https://doaj.org/toc/2073-4425 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2024 3, p 344
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callnumber-first	Q - Science
author	Yonghui Ni
spellingShingle	Yonghui Ni misc QH426-470 misc false discovery rate misc power misc sample size misc multiple testing misc proportion of true null hypotheses misc Genetics Computing Power and Sample Size for the False Discovery Rate in Multiple Applications
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topic_title	QH426-470 Computing Power and Sample Size for the False Discovery Rate in Multiple Applications false discovery rate power sample size multiple testing proportion of true null hypotheses
topic	misc QH426-470 misc false discovery rate misc power misc sample size misc multiple testing misc proportion of true null hypotheses misc Genetics
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title	Computing Power and Sample Size for the False Discovery Rate in Multiple Applications
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title_sort	computing power and sample size for the false discovery rate in multiple applications
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title_auth	Computing Power and Sample Size for the False Discovery Rate in Multiple Applications
abstract	The false discovery rate (FDR) is a widely used metric of statistical significance for genomic data analyses that involve multiple hypothesis testing. Power and sample size considerations are important in planning studies that perform these types of genomic data analyses. Here, we propose a three-rectangle approximation of a <i<p</i<-value histogram to derive a formula to compute the statistical power and sample size for analyses that involve the FDR. We also introduce the R package <i<FDRsamplesize2</i<, which incorporates these and other power calculation formulas to compute power for a broad variety of studies not covered by other FDR power calculation software. A few illustrative examples are provided. The <i<FDRsamplesize2</i< package is available on CRAN.
abstractGer	The false discovery rate (FDR) is a widely used metric of statistical significance for genomic data analyses that involve multiple hypothesis testing. Power and sample size considerations are important in planning studies that perform these types of genomic data analyses. Here, we propose a three-rectangle approximation of a <i<p</i<-value histogram to derive a formula to compute the statistical power and sample size for analyses that involve the FDR. We also introduce the R package <i<FDRsamplesize2</i<, which incorporates these and other power calculation formulas to compute power for a broad variety of studies not covered by other FDR power calculation software. A few illustrative examples are provided. The <i<FDRsamplesize2</i< package is available on CRAN.
abstract_unstemmed	The false discovery rate (FDR) is a widely used metric of statistical significance for genomic data analyses that involve multiple hypothesis testing. Power and sample size considerations are important in planning studies that perform these types of genomic data analyses. Here, we propose a three-rectangle approximation of a <i<p</i<-value histogram to derive a formula to compute the statistical power and sample size for analyses that involve the FDR. We also introduce the R package <i<FDRsamplesize2</i<, which incorporates these and other power calculation formulas to compute power for a broad variety of studies not covered by other FDR power calculation software. A few illustrative examples are provided. The <i<FDRsamplesize2</i< package is available on CRAN.
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title_short	Computing Power and Sample Size for the False Discovery Rate in Multiple Applications
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