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Joint testing and false discovery rate control in high-dimensional multivariate regression.

Yin Xia1, T Tony Cai2, Hongzhe Li3

  • 1Department of Statistics, School of Management, Fudan University, Shanghai, China.

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|February 26, 2019
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Summary
This summary is machine-generated.

This study introduces a new statistical method for joint testing in high-dimensional multivariate regression, enhancing the detection of genetic and genomic associations across multiple responses. The approach offers improved power for identifying relevant covariates in complex biological datasets.

Keywords:
Bias-corrected group lassoError rate controlMultiple phenotypesRow-wise multiple testing

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Area of Science:

  • Genomics
  • Statistical Genetics
  • Bioinformatics

Background:

  • High-dimensional multivariate regression is crucial in genomic and genetic research.
  • Identifying covariates associated with multiple responses is a key challenge.
  • Existing methods may lack power in detecting these complex associations.

Purpose of the Study:

  • To develop simultaneous testing methods for regression coefficients across multiple responses.
  • To control the false discovery rate (FDR) in high-dimensional settings.
  • To enhance the identification of covariates linked to multiple biological outcomes.

Main Methods:

  • Utilizes inverse regression and bias-corrected group lasso estimates.
  • Develops a novel test statistic with an asymptotic chi-squared null distribution.
  • Implements a row-wise multiple testing procedure for covariate identification.

Main Results:

  • The proposed method asymptotically controls the false discovery proportion and false discovery rate.
  • Simulations show increased power compared to entrywise testing.
  • The method effectively identifies microRNA regulators associated with protein expression in ovarian cancer data.

Conclusions:

  • The developed simultaneous testing procedure is effective for high-dimensional multivariate regression.
  • It offers a powerful tool for discovering complex associations in genomic and genetic studies.
  • Application to ovarian cancer data highlights its utility in identifying biological regulators.