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

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LandScape: a simple method to aggregate p--Values and other stochastic variables without a priori grouping. / Wiuf, Carsten; Pallesen, Jonatan; Foldager, Leslie; Grove, Jakob.

In: Statistical Applications in Genetics and Molecular Biology, Vol. 15, No. 4, 08.2016, p. 349-361.

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

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Wiuf, Carsten et al. "LandScape: a simple method to aggregate p--Values and other stochastic variables without a priori grouping". Statistical Applications in Genetics and Molecular Biology. 2016, 15(4). 349-361. https://doi.org/10.1515/sagmb-2015-0085

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Wiuf, Carsten ; Pallesen, Jonatan ; Foldager, Leslie ; Grove, Jakob. / LandScape: a simple method to aggregate p--Values and other stochastic variables without a priori grouping. In: Statistical Applications in Genetics and Molecular Biology. 2016 ; Vol. 15, No. 4. pp. 349-361.

Bibtex

@article{65cec91b8721425cb5fc5e10dd3acee0,
title = "LandScape: a simple method to aggregate p--Values and other stochastic variables without a priori grouping",
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 stochasticvariables, 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 considerationsand 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 implementedin Python and freely available online (through GitHub, see the Supplementary information).",
author = "Carsten Wiuf and Jonatan Pallesen and Leslie Foldager and Jakob Grove",
year = "2016",
month = "8",
doi = "10.1515/sagmb-2015-0085",
language = "English",
volume = "15",
pages = "349--361",
journal = "Statistical Applications in Genetics and Molecular Biology",
issn = "1544-6115",
publisher = "Walterde Gruyter GmbH",
number = "4",

}

RIS

TY - JOUR

T1 - LandScape: a simple method to aggregate p--Values and other stochastic variables without a priori grouping

AU - Wiuf, Carsten

AU - Pallesen, Jonatan

AU - Foldager, Leslie

AU - Grove, Jakob

PY - 2016/8

Y1 - 2016/8

N2 - 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 stochasticvariables, 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 considerationsand 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 implementedin Python and freely available online (through GitHub, see the Supplementary information).

AB - 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 stochasticvariables, 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 considerationsand 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 implementedin Python and freely available online (through GitHub, see the Supplementary information).

U2 - 10.1515/sagmb-2015-0085

DO - 10.1515/sagmb-2015-0085

M3 - Journal article

C2 - 27269897

VL - 15

SP - 349

EP - 361

JO - Statistical Applications in Genetics and Molecular Biology

JF - Statistical Applications in Genetics and Molecular Biology

SN - 1544-6115

IS - 4

ER -