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Multiple testing approaches for hypotheses in integrative genomics

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Abstract With the explosion in available technologies for measuring many biological phenomena on a large scale, there have been concerted efforts in a variety of biological and medical settings to perform systems biology analyses. A crucial question then becomes how to combine data across the various large‐scale data types. This article reviews the data types that can be considered and treats so‐called horizontal and vertical integration analyses. This article focuses on the use of multiple testing approaches in order to perform integrative analyses. Two questions help to clarify the class of procedures that should be used. The first deals with whether a horizontal or vertical integration is being performed. The second is if there is a priority for a given platform. Based on the answers to these questions, we review various methodologies that could be applied. This article is categorized under: Statistical Learning and Exploratory Methods of the Data Sciences > Knowledge Discovery Statistical and Graphical Methods of Data Analysis > Nonparametric Methods Applications of Computational Statistics > Genomics/Proteomics/Genetics
Distribution of gene expression weights (Figure a) and copy number expression weights (Figure b) for the prostate cancer dataset
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Applications of Computational Statistics > Genomics/Proteomics/Genetics
Statistical and Graphical Methods of Data Analysis > Nonparametric Methods
Statistical Learning and Exploratory Methods of the Data Sciences > Knowledge Discovery

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