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Meta-analysis based on weighted ordered P-values for genomic data with heterogeneity.

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  • 1Department of Statistics, Pennsylvania State University, University Park, Pennsylvania 16802, USA. ghoshd@psu.edu.

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New weighted ordered p-value (WOP) methods enhance meta-analysis for genomic data. These methods effectively detect consistent signals in a majority of studies, outperforming existing approaches.

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Area of Science:

  • Genomics
  • Biostatistics
  • Bioinformatics

Background:

  • Meta-analysis is increasingly vital in genomic data analysis due to data growth.
  • Classical methods like Fisher's and Stouffer's often aim to find signals in at least one study.
  • Genomic meta-analysis increasingly requires methods to detect consistent signals across multiple studies.

Purpose of the Study:

  • To introduce a novel class of meta-analysis methods based on weighted ordered p-values (WOP).
  • To develop methods specifically designed for detecting significance in a majority of studies, crucial for genomic applications.
  • To enhance the detection of consistent signals in meta-analysis, particularly in the presence of heterogeneity.

Main Methods:

  • Proposed methods utilize weighted ordered p-values (WOP), adapting classical procedures like Fisher's and Stouffer's.
  • Weights are assigned based on the order of p-values, with a focus on binomial distribution weighting.
  • The approach down-weights outlying p-values and gives higher weight to the median p-value for robust signal detection.

Main Results:

  • The proposed WOP methods demonstrated superior performance in detecting signals present in a majority of studies.
  • Simulations confirmed the strengths of WOP methods compared to existing meta-analysis procedures.
  • Applications to gene expression data illustrated the practical utility of the WOP methodology.

Conclusions:

  • WOP methods are more effective than existing approaches for identifying signals in the majority of studies.
  • WOP methods exhibit greater robustness in practical applications compared to the rth ordered p-value (rOP) method.
  • The flexibility of WOP methods offers significant potential for detecting consistent signals in heterogeneous meta-analyses.