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Article overview
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P-value evaluation, variability index and biomarker categorization for adaptively weighted Fisher's meta-analysis method in omics applications | | Zhiguang Huo
; Shaowu Tang
; Yongseok Park
; George Tseng
; | | Date: |
16 Aug 2017 | | Abstract: | Meta-analysis methods have been widely used to combine results from multiple
clinical or genomic studies to increase statistical power and ensure robust and
accurate conclusion. Adaptively weighted Fisher’s method (AW-Fisher) is an
effective approach to combine p-values from $K$ independent studies and to
provide better biological interpretation by characterizing which studies
contribute to meta-analysis. Currently, AW-Fisher suffers from lack of fast,
accurate p-value computation and variability estimate of AW weights. When the
number of studies $K$ is large, the $3^K - 1$ possible differential expression
pattern categories can become intractable. In this paper, we apply an
importance sampling technique with spline interpolation to increase accuracy
and speed of p-value calculation. Using resampling techniques, we propose a
variability index for the AW weight estimator and a co-membership matrix to
characterize pattern similarities between genes. The co-membership matrix is
further used to categorize differentially expressed genes based on their
meta-patterns for further biological investigation. The superior performance of
the proposed methods is shown in simulations. These methods are also applied to
two real applications to demonstrate intriguing biological findings. | | Source: | arXiv, 1708.5084 | | Services: | Forum | Review | PDF | Favorites | |
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