How does multiple testing correction work?

By William Stafford Noble. 2009

748 citations recorded by the source corpus.

Drawing valid conclusions from an experiment often requires associating statistical confidence measures with the observed data. But these measures can be stated in terms of p-values, false discovery rates or q-values. What are the differences? And how should you decide which one to use?

Subfields: Genetics, Molecular Biology, Statistics and Probability

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Citations in the corpus

A survey of best practices for RNA-seq data analysis (2016, methodology): “For methods that return a false discovery rate (FDR), the proportion of genes that are highly expressed out of the total set of genes being tested will also influence the power of detection after multiple testing correction [193].”
agriGO v2.0: a GO analysis toolkit for the agricultural community, 2017 update (2017, methodology): “In SEA, we used Fisher’s exact test and all of the multiple hypothesis tests (48) provided in agriGO v2.”
A computational pipeline for comparative ChIP-seq analyses (2011, background): “, a scenario in which even the best of many random candidates would show good enrichment”
Review of recent developments in GC–MS approaches to metabolomics-based research (2018, neutral): “2005), National Institute of Standards and Technology (NIST) Mass Spectral Database (Daniel et al.”
From big data analysis to personalized medicine for all: challenges and opportunities (2015, background): “4) [70].”
Universal database search tool for proteomics (2014, neutral): “…of individual PSMs (independently of the database), they need to be transformed into E -value( S , A þ , t , N ) to take into account the fact that the database search represents ‘multiple testing’ where multiple variants (arising from different database peptides) are scored against a spectrum 37 .”
Premature ovarian failure. (2010, background): “05 level of significance is divided by the number of gene variants being tested (Noble 2009).”