Nonparametric Bayesian evaluation of differential protein quantification.

Serang O, Cansizoglu AE, Käll L, Steen H, Steen JA

J. Proteome Res. 12 (10) 4556-4565 [2013-10-04; online 2013-09-11]

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.

Affiliated researcher

PubMed 24024742

DOI 10.1021/pr400678m

Crossref 10.1021/pr400678m

pmc: PMC3824391
mid: NIHMS523988

Publications 7.1.2