University of Illinois at Chicago
Assigning Significance in Label-Free Quantitative Proteomics to.pdf (967.44 kB)
Download file

Assigning significance in label-free quantitative proteomics to include single-peptide-hit proteins with low replicates.

Download (967.44 kB)
journal contribution
posted on 2011-05-27, 00:00 authored by Qingbo Li
When sample replicates are limited in a label-free proteomics experiment, selecting differentially regulated proteins with an assignment of statistical significance remains difficult for proteins with a single-peptide hit or a small fold-change. This paper aims to address this issue. An important component of the approach employed here is to utilize the rule of Minimum number of Permuted Significant Pairings (MPSP) to reduce false positives. The MPSP rule generates permuted sample pairings from limited analytical replicates and simply requires that a differentially regulated protein can be selected only when it is found significant in designated number of permuted sample pairings. Both a power law global error model with a signal-to-noise ratio statistic (PLGEM-STN) and a constant fold-change threshold were initially used to select differentially regulated proteins. But both methods were found not stringent enough to control the false discovery rate to 5% in this study. On the other hand, the combination of the MPSP rule with either of these two methods significantly reduces false positives with little effect on the sensitivity to select differentially regulated proteins including those with a single-peptide hit or with a <2-fold change.


Part of this work was supported by the NIH Grant R03AI073469-01A1.


Publisher Statement

Copyright © 2010 Qingbo Li. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The original source for this publication is at Hindawi Publishing Corporation; DOI:10.1155/2010/731582


Hindawi Publishing Corporation


  • en_US



Issue date


Usage metrics


    No categories selected