LandScape: a simple method to aggregate p-values and other stochastic variables without a priori grouping

Wiuf Carsten (), Schaumburg-Müller Pallesen Jonatan, Foldager Leslie and Grove Jakob
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Wiuf Carsten: Department of Mathematical Science, University of Copenhagen, 2100 Copenhagen, Denmark
Schaumburg-Müller Pallesen Jonatan: Department of Biomedicine, Aarhus University, 8000 Aarhus, Denmark iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Denmark iSEQ, Centre for Integrative Sequencing, Aarhus University, 8000 Aarhus, Denmark Bioinformatics Research Centre, Aarhus University, 8000 Aarhus, Denmark
Foldager Leslie: iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Denmark iSEQ, Centre for Integrative Sequencing, Aarhus University, 8000 Aarhus, Denmark Bioinformatics Research Centre, Aarhus University, 8000 Aarhus, Denmark Department of Animal Science, Aarhus University, 8830 Tjele, Denmark Translational Neuropsychiatric Unit, Department of Clinical Medicine, Aarhus University, 8000 Aarhus, Denmark
Grove Jakob: Department of Biomedicine, Aarhus University, 8000 Aarhus, Denmark iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Denmark iSEQ, Centre for Integrative Sequencing, Aarhus University, 8000 Aarhus, Denmark Bioinformatics Research Centre, Aarhus University, 8000 Aarhus, Denmark

Statistical Applications in Genetics and Molecular Biology, 2016, vol. 15, issue 4, 349-361

Abstract: In many areas of science it is custom to perform many, potentially millions, of tests simultaneously. To gain statistical power it is common to group tests based on a priori criteria such as predefined regions or by sliding windows. However, it is not straightforward to choose grouping criteria and the results might depend on the chosen criteria. Methods that summarize, or aggregate, test statistics or p-values, without relying on a priori criteria, are therefore desirable. We present a simple method to aggregate a sequence of stochastic variables, such as test statistics or p-values, into fewer variables without assuming a priori defined groups. We provide different ways to evaluate the significance of the aggregated variables based on theoretical considerations and resampling techniques, and show that under certain assumptions the FWER is controlled in the strong sense. Validity of the method was demonstrated using simulations and real data analyses. Our method may be a useful supplement to standard procedures relying on evaluation of test statistics individually. Moreover, by being agnostic and not relying on predefined selected regions, it might be a practical alternative to conventionally used methods of aggregation of p-values over regions. The method is implemented in Python and freely available online (through GitHub, see the Supplementary information).

Keywords: association mapping; genome scan; multiple testing; random walk (search for similar items in EconPapers)
Date: 2016
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DOI: 10.1515/sagmb-2015-0085

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