D_CDF test of negative log transformed p-values with application to genetic pathway analysis

Hongying Dai, Richard Charnigo

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


In genetic pathway analysis and other high dimensional data analysis, thousands and millions of tests could be performed simultaneously. p-values from multiple tests are often presented in a negative log-transformed format. We construct a contaminated exponential mixture model for-ln(P) and propose a D CDF test to determine whether some-ln(P) are from tests with underlying effects. By comparing the cumulative distribution functions (CDF) of-ln(P) under mixture models, the proposed method can detect the cumulative effect from a number of variants with small effect sizes. Weight functions and truncations can be incorporated to the D CDF test to improve power and better control the correlation among data. By using the modified maximum likelihood estimators (MMLE), the D CDF tests have very tractable limiting distributions under H0. A copula based procedure is proposed to address the correlation issue among p-values. We also develop power and sample size calculation for the D CDF test. The extensive empirical assessments on the correlated data demonstrate that the (weighted and/or c-level truncated) D CDF tests have well controlled Type I error rates and high power for small effect sizes. We applied our method to gene expression data in mice and identified significant pathways related the mouse body weight.

Original languageEnglish
Pages (from-to)187-200
Number of pages14
JournalStatistics and its Interface
Issue number2
StatePublished - 2014


  • D_CDF test
  • Mixture model
  • Modified maximum likelihood estimator (MMLE)
  • Negative log transformed p-values
  • Weight function
  • c-level truncated test

ASJC Scopus subject areas

  • Statistics and Probability
  • Applied Mathematics


Dive into the research topics of 'D_CDF test of negative log transformed p-values with application to genetic pathway analysis'. Together they form a unique fingerprint.

Cite this