Nonparametric bayesian evaluation of differential protein quantification
2013 (English)In: Journal of Proteome Research, ISSN 1535-3893, E-ISSN 1535-3907, Vol. 12, no 10, 4556-4565 p.Article in journal (Refereed) Published
Arbitrary cutoffs are ubiquitous in quantitative computational proteomics: maximum acceptable MS/MS PSM or peptide q value, minimum ion intensity to calculate a fold change, the minimum number of peptides that must be available to trust the estimated protein fold change (or the minimum number of PSMs that must be available to trust the estimated peptide fold change), and the "significant" fold change cutoff. Here we introduce a novel experimental setup and nonparametric Bayesian algorithm for determining the statistical quality of a proposed differential set of proteins or peptides. By comparing putatively nonchanging case-control evidence to an empirical null distribution derived from a control-control experiment, we successfully avoid some of these common parameters. We then apply our method to evaluating different fold-change rules and find that for our data a 1.2-fold change is the most permissive of the plausible fold-change rules.
Place, publisher, year, edition, pages
2013. Vol. 12, no 10, 4556-4565 p.
control-control, fold-change, LC-MS/MS, npCI, null distribution, PSM, TMT labeling
IdentifiersURN: urn:nbn:se:kth:diva-133166DOI: 10.1021/pr400678mISI: 000326320300025ScopusID: 2-s2.0-84885230259OAI: oai:DiVA.org:kth-133166DiVA: diva2:660228
FunderScience for Life Laboratory - a national resource center for high-throughput molecular bioscienceSwedish Research Council
QC 201310292013-10-292013-10-282013-11-25Bibliographically approved