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Estimation and control of multiple testing error rates for microarray studies

Stanley B. Pounds
2006 Briefings in Bioinformatics  
His research focuses on developing and improving statistical methods for the analysis of microarray gene expression data, with special emphasis on methods that estimate or control multiple testing error  ...  In recent years, many innovative statistical methods have been developed to estimate or control various error rates for microarray studies.  ...  Acknowledgements The author wishes to thank colleagues Cheng Cheng, Wei Liu and Wenjian Yang and two anonymous reviewers for their helpful suggestions that improved the quality of this article.  ... 
doi:10.1093/bib/bbk002 pmid:16761362 fatcat:42y3ph4gmzcbbax4dztlx2qnyu

Design and Analysis of Microarray Experiments for Pharmacogenomics [chapter]

Jason Hsu, Youlan Rao, Yoonkyung Lee, Jane Chang, Kristin Bergsteinsdottir, Magnus Magnusson, Tao Wang, Eirikur Steingrimsson
2009 Chapman & Hall/CRC Biostatistics Series  
DMS-0505519 and a grant from the Icelandic Science and Technology Council.  ...  sample, and noise separately, and that marker genes can be selected for the validation study by multiple testing with a properly controlled error rate.  ...  Multiple tests conducted in this fashion control error rates conditionally, conditional on the subjects and the samples. Therefore, they control error rates unconditionally as well.  ... 
doi:10.1201/9781584889854-c7 fatcat:avvumqzsi5devaqtxcacj7d6ni

Parallel multiplicity and error discovery rate (EDR) in microarray experiments

Wayne Xu, Clay J Carter
2010 BMC Bioinformatics  
This study proposes a new view of multiplicity in microarray experiments and the EDR provides an alternative multiple test method for Type I error control in microarray experiments.  ...  Parallel multiple tests use only the negative genes that parallel the positive genes to control the error rate; while simultaneous multiple tests use the total unchanged gene number for error estimates  ...  The authors would like to thank Hackstadt Amber J. for kindly providing a portion of simulation source code.  ... 
doi:10.1186/1471-2105-11-465 pmid:20846437 pmcid:PMC2955048 fatcat:zn6wutc3rzcqvknwf7ibkp3nr4

Identifying differentially expressed genes using false discovery rate controlling procedures

A. Reiner, D. Yekutieli, Y. Benjamini
2003 Bioinformatics  
The procedures are studied using simulated microarray data, and their performance is examined relative to their ease of implementation.  ...  In this paper we address this very large multiplicity problem by adopting the false discovery rate (FDR) controlling approach.  ...  ACKNOWLEDGEMENTS This research has been partially supported by the F.I.R.S.T. grant from the Israeli Academy of Sciences and Humanities.  ... 
doi:10.1093/bioinformatics/btf877 pmid:12584122 fatcat:fntoihkblzgktjbui4my5b5ssu

Multivariate hierarchical Bayesian model for differential gene expression analysis in microarray experiments

Hongya Zhao, Kwok-Leung Chan, Lee-Ming Cheng, Hong Yan
2008 BMC Bioinformatics  
Results: Motivated by the complicated error relations in microarray data, we propose a multivariate hierarchical Bayesian framework for data analysis in the replicated microarray experiments.  ...  Simulation studies show that the proposed approach presents better operating characteristics and lower false discovery rate (FDR) than existing methods, especially when the correlation coefficient is large  ...  Wong, R.N.S. and Dr. Yue, P.Y.K. from the department of biology of Hong Kong Baptist University for providing us the microarray data.  ... 
doi:10.1186/1471-2105-9-s1-s9 pmid:18315862 pmcid:PMC2259410 fatcat:7t6fwscrh5erxhnzkol3o5vrna

Sample size for detecting differentially expressed genes in microarray experiments

Caimiao Wei, Jiangning Li, Roger E Bumgarner
2004 BMC Genomics  
We apply the same normalization algorithm and estimate the variance of gene expression for a variety of cDNA data sets (humans, inbred mice and rats) comparing two conditions.  ...  In addition, experimental design, technical variability and data pre-processing play a role in the power of the statistical tests in microarrays.  ...  Acknowledgements Roger Bumgarner receives funding from the following grants: NHBLI-5R01HL072370 and 1P50HL07399, NIDDK-5U24DK058813, NIEHS-1U19ES011387 and NIAID-1R21AI052028 and 5P01AI052106.  ... 
doi:10.1186/1471-2164-5-87 pmid:15533245 pmcid:PMC533874 fatcat:vzpeodgjhjds7kqth2vqmf47im

Sample Size Calculation for Microarray Studies with Survival Endpoints

Sin-Ho Jung
2013 Journal of computer science and systems biology  
The most popular multiple testing methods used for gene discovery in microarray studies are to control the false discovery rate or the family wise error rate.  ...  In this case, we need to adjust the false positivity in such discovery procedure for multiplicity of the genes using a multiple testing method.  ...  The proposed methods can be used for other types of highthroughput biomarker study, such as genome wide association studies or gene sequencing studies, with minor modifications.  ... 
doi:10.4172/jcsb.1000114 fatcat:beckhpaftvbkbcmurmkus44x7e

Optimal alpha reduces error rates in gene expression studies: a meta-analysis approach

J. F. Mudge, C. J. Martyniuk, J. E. Houlahan
2017 BMC Bioinformatics  
Results: A meta-analysis of 242 microarray studies extracted from the peer-reviewed literature found that current practices for setting statistical thresholds led to very high Type II error rates.  ...  Transcriptomic approaches (microarray and RNA-seq) have been a tremendous advance for molecular science in all disciplines, but they have made interpretation of hypothesis testing more difficult because  ...  Acknowledgements Lillian Fanjoy for compiling studies. Funding Natural Sciences and Engineering Research Council of Canada (JH and CJM).  ... 
doi:10.1186/s12859-017-1728-3 pmid:28637422 pmcid:PMC5480162 fatcat:4vmrgvyg45ftboiawfczjphkjq

Improving the power for detecting overlapping genes from multiple DNA microarray-derived gene lists

Xutao Deng, Jun Xu, Charles Wang
2008 BMC Bioinformatics  
However this simple operation of intersecting multiple gene lists, known as the Intersection-Union Tests (IUTs), is performed without knowing the incurred changes in Type 1 error rate and can lead to loss  ...  RIUT is proved to be a more powerful alternative for performing IUTs in identifying overlapping genes from multiple gene lists derived from microarray gene expression profiling.  ...  Acknowledgements This article has been published as part of BMC Bioinformatics Volume 9 Supplement 6, 2008: Symposium of Computations in Bioinformatics and Bioscience (SCBB07).  ... 
doi:10.1186/1471-2105-9-s6-s14 pmid:18541049 pmcid:PMC2423437 fatcat:pkey3al64jcqffcykgk4z3rs7m

Power and Stability Properties of Resampling-Based Multiple Testing Procedures with Applications to Gene Oncology Studies

Dongmei Li, Timothy D. Dye
2013 Computational and Mathematical Methods in Medicine  
Our study focuses on investigating the power and stability of seven resampling-based multiple testing procedures frequently used in high-throughput data analysis for small sample size data through simulations  ...  Resampling-based multiple testing procedures are widely used in genomic studies to identify differentially expressed genes and to conduct genome-wide association studies.  ...  Moreno for permission of using his microarray data. This work was supported in part by the National Institute of Minority Health and Health Disparities Awards U54MD007584 (J.  ... 
doi:10.1155/2013/610297 pmid:24348741 pmcid:PMC3853148 fatcat:xl3xwvhcmjdmrdsc7uuhu26f7m

Statistical issues with microarrays: processing and analysis

Robert Nadon, Jennifer Shoemaker
2002 Trends in Genetics  
The single-step Bonferroni correction is the bestknown procedure for controlling the false-positive rate when multiple tests are conducted [46] .  ...  For example, it is not necessary to control the false-positive rate in the same way for all probes in a study.  ... 
doi:10.1016/s0168-9525(02)02665-3 pmid:12047952 fatcat:hf66i4yf5vby3ccgyivqzenwve

Multiple Hypothesis Testing in Microarray Experiments

Sandrine Dudoit, Juliet Popper Shaffer, Jennifer C. Block
2003 Statistical Science  
This article discusses different approaches to multiple hypothesis testing in the context of microarray experiments and compares the procedures on microarray and simulated datasets.  ...  The biological question of differential expression can be restated as a problem in multiple hypothesis testing: the simultaneous test for each gene of the null hypothesis of no association between the  ...  Special problems arising from the multiplicity aspect include defining an appropriate Type I error rate and devising powerful multiple testing procedures which control this error rate and account for the  ... 
doi:10.1214/ss/1056397487 fatcat:qu7qoag6dffevhbsazdg74haba

An omnibus test for differential distribution analysis of microbiome microarray data

Xiang Lin, Jie Zhang, Zhi Wei, Turki Turki
2021 IEEE Access  
In this study, we introduce a novel method, zero-inflated gamma omnibus test (ZIG), to specifically test the continuous and zero-inflated microarray data.  ...  We found that ZIG has significantly higher power and similar or lower false positive rate than other methods in the tests of simulated data.  ...  Both single and multiple probes simulations showed a generally good false positive control for ZIG omnibus test.  ... 
doi:10.1109/access.2021.3093045 fatcat:u4cxrqxhvva57daq7e4q2uj5we

Comparison of methods for the proportion of true null hypotheses in microarray studie

Joonsung Kang
2020 Communications for Statistical Applications and Methods  
A traditional multiple testing rate, family-wise error rate is too conservative and old to control type I error in multiple testing setups; however, false discovery rate (FDR) has received significant  ...  We consider estimating the proportion of true null hypotheses in multiple testing problems.  ...  Acknowledgements This work was supported by the Research Institute of Natural Science of Gangneung-Wonju National University.  ... 
doi:10.29220/csam.2020.27.1.141 fatcat:4miadpckwfa2jinu7atw2donsq

Sequential interim analyses of survival data in DNA microarray experiments

Andreas Leha, Tim Beißbarth, Klaus Jung
2011 BMC Bioinformatics  
While many methods correcting the multiple testing bias introduced by interim analyses have been proposed for studies of one single feature, there are still open questions about interim analyses of multiple  ...  Therefore, we examine false discovery rates and power rates in microarray experiments performed during interim analyses of survival studies.  ...  We thank the reviewers for their constructive comments. Authors' contributions KJ formulated the problem and the study design.  ... 
doi:10.1186/1471-2105-12-127 pmid:21527044 pmcid:PMC3098786 fatcat:mxjuj2lhrnb7zf6cxv7mci2a5e
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