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Adaptive Group-combined P-values Test for Two-sample Location Problem with Applications to Microarray Data.

Shenghu Zhang1,2, Jiayan Zhu3,4, Zhengbang Li5

  • 1School of Mathematics and Information Science, Jiangxi Normal University, Nanchang, 330022, China.

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|May 27, 2018
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This study introduces a new adaptive group p-values combination test for comparing two groups in high-dimensional data. The proposed method effectively controls statistical errors and improves power, even when data is not normally distributed.

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Area of Science:

  • Statistics
  • Biostatistics
  • High-dimensional data analysis

Background:

  • High-dimensional data analysis presents challenges for traditional statistical tests.
  • Existing methods often fail when data dimension exceeds sample size or lacks normality.
  • Type I error rates can be compromised in high-dimensional settings.

Purpose of the Study:

  • To propose a novel statistical test for the two-sample location problem in high-dimensional data.
  • To develop a test robust to high dimensionality and non-normal distributions.
  • To address limitations of existing tests in challenging data scenarios.

Main Methods:

  • An adaptive group p-values combination test is proposed.
  • The method combines p-values adaptively to enhance robustness.
  • The test is designed to be effective regardless of data distribution or dimension-sample size relationship.

Main Results:

  • Simulation studies demonstrate correct control of Type I error rates.
  • The proposed test shows superior performance compared to existing methods in most scenarios.
  • The method was successfully applied to Ageing Human Brain Microarray data.

Conclusions:

  • The adaptive group p-values combination test is a powerful and robust tool for high-dimensional two-sample location problems.
  • This approach offers a reliable solution for complex biological and statistical data.
  • The method provides accurate error control and enhanced statistical power.