Literature DB >> 19628502

Moderated effect size and P-value combinations for microarray meta-analyses.

Guillemette Marot1, Jean-Louis Foulley, Claus-Dieter Mayer, Florence Jaffrézic.   

Abstract

MOTIVATION: With the proliferation of microarray experiments and their availability in the public domain, the use of meta-analysis methods to combine results from different studies increases. In microarray experiments, where the sample size is often limited, meta-analysis offers the possibility to considerably increase the statistical power and give more accurate results.
RESULTS: A moderated effect size combination method was proposed and compared with other meta-analysis approaches. All methods were applied to real publicly available datasets on prostate cancer, and were compared in an extensive simulation study for various amounts of inter-study variability. Although the proposed moderated effect size combination improved already existing effect size approaches, the P-value combination was found to provide a better sensitivity and a better gene ranking than the other meta-analysis methods, while effect size methods were more conservative. AVAILABILITY: An R package metaMA is available on the CRAN.

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Mesh:

Year:  2009        PMID: 19628502     DOI: 10.1093/bioinformatics/btp444

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


  75 in total

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Review 3.  Reuse of public genome-wide gene expression data.

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Journal:  Am J Med Genet B Neuropsychiatr Genet       Date:  2017-07-10       Impact factor: 3.568

6.  Downstream sequence-dependent RNA cleavage and pausing by RNA polymerase I.

Authors:  Catherine E Scull; Andrew M Clarke; Aaron L Lucius; David Alan Schneider
Journal:  J Biol Chem       Date:  2019-12-16       Impact factor: 5.157

7.  A simple and robust method for partially matched samples using the p-values pooling approach.

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9.  Should we abandon the t-test in the analysis of gene expression microarray data: a comparison of variance modeling strategies.

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10.  Meta-analysis of Transcriptomic Data Reveals Pathophysiological Modules Involved with Atrial Fibrillation.

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Journal:  Mol Diagn Ther       Date:  2020-10-23       Impact factor: 4.074

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