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Authors: Irene Castro-Conde and Jacobo de Uña-Álvarez

Affiliation: University of Vigo and Facultad de Económicas, Spain

ISBN: 978-989-8565-35-8

Keyword(s): Correlated Tests, False Discovery Rate, Gene Expression Levels, Monte Carlo Simulations, Multitesting Procedures.

Related Ontology Subjects/Areas/Topics: Agents ; Algorithms and Software Tools ; Artificial Intelligence ; Bioinformatics ; Biomedical Engineering ; Enterprise Information Systems ; Genomics and Proteomics ; Information Systems Analysis and Specification ; Methodologies and Technologies ; Operational Research ; Simulation

Abstract: In a recent paper (de Uña-Álvarez, 2012, Statistical Applications in Genetics and Molecular Biology Vol. 11, Iss. 3, Article 14) a correction of SGoF multitesting method for possibly dependent tests was introduced. This correction enhanced the field of applications of SGoF methodology, initially restricted to the independent setting, to make decisions on which genes are differently expressed in group comparison when the gene expression levels are correlated. In this work we investigate through an intensive Monte Carlo simulation study the performance of that correction, called BB-SGoF (from Beta-Binomial), in practical settings. In the simulations, gene expression levels are correlated inside a number of blocks, while the blocks are independent. Different number of blocks, within-block correlation values, proportion of true effects, and effect levels are considered. The allocation of the true effects is taken to be random. False discovery rate, power, and conservativeness of the metho d with respect to the number of existing effects with p-values below the given significance threshold are computed along the Monte Carlo trials. Comparison to the classical Benjamini-Hochberg adjustment is provided. Conclusions from the simulation study and practical recommendations are reported. (More)

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Paper citation in several formats:
Castro-Conde, I.; Castro-Conde, I.; de Uña-Álvarez, J. and de Uña-Álvarez, J. (2013). Performance of Beta-Binomial SGoF Multitesting Method for Dependent Gene Expression Levels - A Simulation Study.In Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms - Volume 1: BIOINFORMATICS, (BIOSTEC 2013) ISBN 978-989-8565-35-8, pages 93-97. DOI: 10.5220/0004191100930097

@conference{bioinformatics13,
author={Irene Castro{-}Conde. and Irene Castro{-}Conde. and Jacobo de Uña{-}Álvarez. and Jacobo de Uña{-}Álvarez.},
title={Performance of Beta-Binomial SGoF Multitesting Method for Dependent Gene Expression Levels - A Simulation Study},
booktitle={Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms - Volume 1: BIOINFORMATICS, (BIOSTEC 2013)},
year={2013},
pages={93-97},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004191100930097},
isbn={978-989-8565-35-8},
}

TY - CONF

JO - Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms - Volume 1: BIOINFORMATICS, (BIOSTEC 2013)
TI - Performance of Beta-Binomial SGoF Multitesting Method for Dependent Gene Expression Levels - A Simulation Study
SN - 978-989-8565-35-8
AU - Castro-Conde, I.
AU - Castro-Conde, I.
AU - de Uña-Álvarez, J.
AU - de Uña-Álvarez, J.
PY - 2013
SP - 93
EP - 97
DO - 10.5220/0004191100930097

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