ArticleBMC bioinformatics2014
A fast algorithm for determining bounds and accurate approximate p-values of the rank product statistic for replicate experiments.
Article in BMC bioinformatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
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Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- An information-theoretic approach to the modeling and analysis of whole-genome bisulfite sequencing data.BMC bioinformatics · 2018Article
- Identification of influential observations in high-dimensional cancer survival data through the rank product test.BioData mining · 2018Article
- OptPipe - a pipeline for optimizing metabolic engineering targets.BMC systems biology · 2017Article
- RankProd 2.0: a refactored bioconductor package for detecting differentially expressed features in molecular profiling datasets.Bioinformatics (Oxford, England) · 2017Article
- ConTra v3: a tool to identify transcription factor binding sites across species, update 2017.Nucleic acids research · 2017Article
- Metabolic network segmentation: A probabilistic graphical modeling approach to identify the sites and sequential order of metabolic regulation from non-targeted metabolomics data.PLoS computational biology · 2017Article
- Exact p-values for pairwise comparison of Friedman rank sums, with application to comparing classifiers.BMC bioinformatics · 2017Article
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3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundThe rank product method is a powerful statistical technique for identifying differentially expressed molecules in replicated experiments. A critical issue in molecule selection is accurate calculation of the p-value of the rank product statistic to adequately address multiple testing. Both exact calculation and permutation and gamma approximations have been proposed to determine molecule-level significance. These current approaches have serious drawbacks as they are either computationally burdensome or provide inaccurate estimates in the tail of the p-value distribution.
resultsWe derive strict lower and upper bounds to the exact p-value along with an accurate approximation that can be used to assess the significance of the rank product statistic in a computationally fast manner. The bounds and the proposed approximation are shown to provide far better accuracy over existing approximate methods in determining tail probabilities, with the slightly conservative upper bound protecting against false positives. We illustrate the proposed method in the context of a recently published analysis on transcriptomic profiling performed in blood.
conclusionsWe provide a method to determine upper bounds and accurate approximate p-values of the rank product statistic. The proposed algorithm provides an order of magnitude increase in throughput as compared with current approaches and offers the opportunity to explore new application domains with even larger multiple testing issue. The R code is published in one of the Additional files and is available at http://www.ru.nl/publish/pages/726696/rankprodbounds.zip .
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