Multiple testing corrections in quantitative proteomics: A useful but blunt tool
| Autor: | Dana Pascovici, David C. L. Handler, Jemma X. Wu, Paul A. Haynes |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Proteomics
0301 basic medicine False discovery rate Computer science Quantitative proteomics Computational Biology Proteins Context (language use) Biochemistry Medium scale Reliability engineering 03 medical and health sciences 030104 developmental biology 0302 clinical medicine Tandem Mass Spectrometry Multiple comparisons problem Molecular Biology True positive rate Algorithms 030217 neurology & neurosurgery Simulation |
| Zdroj: | PROTEOMICS. 16:2448-2453 |
| ISSN: | 1615-9853 |
| DOI: | 10.1002/pmic.201600044 |
| Popis: | Multiple testing corrections are a useful tool for restricting the FDR, but can be blunt in the context of low power, as we demonstrate by a series of simple simulations. Unfortunately, in proteomics experiments low power can be common, driven by proteomics-specific issues like small effects due to ratio compression, and few replicates due to reagent high cost, instrument time availability and other issues; in such situations, most multiple testing corrections methods, if used with conventional thresholds, will fail to detect any true positives even when many exist. In this low power, medium scale situation, other methods such as effect size considerations or peptide-level calculations may be a more effective option, even if they do not offer the same theoretical guarantee of a low FDR. Thus, we aim to highlight in this article that proteomics presents some specific challenges to the standard multiple testing corrections methods, which should be employed as a useful tool but not be regarded as a required rubber stamp. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Plný text