ArticleBioinformatics (Oxford, England)2015
Estimating the proportion of true null hypotheses when the statistics are discrete.
Article in Bioinformatics (Oxford, England), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- A field-wide assessment of differential expression profiling by high-throughput sequencing reveals widespread bias.PLoS biology · 2023Article
- MultipleTesting.com: A tool for life science researchers for multiple hypothesis testing correction.PloS one · 2021Article
- Article
- Evaluation of copy-number variants as modifiers of breast and ovarian cancer risk for BRCA1 pathogenic variant carriers.European journal of human genetics : EJHG · 2017Article
- Variance component testing for identifying differentially expressed genes in RNA-seq data.PeerJ · 2017Article
- reChIP-seq reveals widespread bivalency of H3K4me3 and H3K27me3 in CD4(+) memory T cells.Nature communications · 2016Article
- The effects of natural selection across molecular pathways in Drosophila melanogaster.BMC evolutionary biology · 2015Article
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Authors and funding
3 authors.
Funding
Abstract
motivationIn high-dimensional testing problems π0, the proportion of null hypotheses that are true is an important parameter. For discrete test statistics, the P values come from a discrete distribution with finite support and the null distribution may depend on an ancillary statistic such as a table margin that varies among the test statistics. Methods for estimating π0 developed for continuous test statistics, which depend on a uniform or identical null distribution of P values, may not perform well when applied to discrete testing problems.
resultsThis article introduces a number of π0 estimators, the regression and 'T' methods that perform well with discrete test statistics and also assesses how well methods developed for or adapted from continuous tests perform with discrete tests. We demonstrate the usefulness of these estimators in the analysis of high-throughput biological RNA-seq and single-nucleotide polymorphism data. AVAILABILITY AND IMPLEMENTATION: implemented in R.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.