Literature DB >> 15845654

Sample size for FDR-control in microarray data analysis.

Sin-Ho Jung1.   

Abstract

We consider identifying differentially expressing genes between two patient groups using microarray experiment. We propose a sample size calculation method for a specified number of true rejections while controlling the false discovery rate at a desired level. Input parameters for the sample size calculation include the allocation proportion in each group, the number of genes in each array, the number of differentially expressing genes and the effect sizes among the differentially expressing genes. We have a closed-form sample size formula if the projected effect sizes are equal among differentially expressing genes. Otherwise, our method requires a numerical method to solve an equation. Simulation studies are conducted to show that the calculated sample sizes are accurate in practical settings. The proposed method is demonstrated with a real study.

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Year:  2005        PMID: 15845654     DOI: 10.1093/bioinformatics/bti456

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  40 in total

Review 1.  Statistics and bioinformatics in nutritional sciences: analysis of complex data in the era of systems biology.

Authors:  Wenjiang J Fu; Arnold J Stromberg; Kert Viele; Raymond J Carroll; Guoyao Wu
Journal:  J Nutr Biochem       Date:  2010-03-16       Impact factor: 6.048

Review 2.  A review of statistical methods for expression quantitative trait loci mapping.

Authors:  Christina Kendziorski; Ping Wang
Journal:  Mamm Genome       Date:  2006-06-12       Impact factor: 2.957

Review 3.  Laser capture sampling and analytical issues in proteomics.

Authors:  Howard B Gutstein; Jeffrey S Morris
Journal:  Expert Rev Proteomics       Date:  2007-10       Impact factor: 3.940

4.  Practical guidelines for assessing power and false discovery rate for a fixed sample size in microarray experiments.

Authors:  Tiejun Tong; Hongyu Zhao
Journal:  Stat Med       Date:  2008-05-20       Impact factor: 2.373

5.  A censored beta mixture model for the estimation of the proportion of non-differentially expressed genes.

Authors:  Anastasios Markitsis; Yinglei Lai
Journal:  Bioinformatics       Date:  2010-01-15       Impact factor: 6.937

6.  Exact calculations of average power for the Benjamini-Hochberg procedure.

Authors:  Deborah H Glueck; Jan Mandel; Anis Karimpour-Fard; Lawrence Hunter; Keith E Muller
Journal:  Int J Biostat       Date:  2008       Impact factor: 0.968

7.  Low Socioeconomic Status, Adverse Gene Expression Profiles, and Clinical Outcomes in Hematopoietic Stem Cell Transplant Recipients.

Authors:  Jennifer M Knight; J Douglas Rizzo; Brent R Logan; Tao Wang; Jesusa M G Arevalo; Jeffrey Ma; Steve W Cole
Journal:  Clin Cancer Res       Date:  2015-08-18       Impact factor: 12.531

8.  Power and sample size estimation in microarray studies.

Authors:  Wei-Jiun Lin; Huey-Miin Hsueh; James J Chen
Journal:  BMC Bioinformatics       Date:  2010-01-25       Impact factor: 3.169

9.  Relative power and sample size analysis on gene expression profiling data.

Authors:  M van Iterson; P A C 't Hoen; P Pedotti; G J E J Hooiveld; J T den Dunnen; G J B van Ommen; J M Boer; R X Menezes
Journal:  BMC Genomics       Date:  2009-09-17       Impact factor: 3.969

10.  A simulation-approximation approach to sample size planning for high-dimensional classification studies.

Authors:  Perry de Valpine; Hans-Marcus Bitter; Michael P S Brown; Jonathan Heller
Journal:  Biostatistics       Date:  2009-02-21       Impact factor: 5.899

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