...
首页> 外文期刊>International journal of proteomics >Assigning Significance in Label-Free Quantitative Proteomics to Include Single-Peptide-Hit Proteins with Low Replicates
【24h】

Assigning Significance in Label-Free Quantitative Proteomics to Include Single-Peptide-Hit Proteins with Low Replicates

机译:在无标签定量蛋白质组学中赋予重要意义,以包括低重复性的单肽命中蛋白质

获取原文
           

摘要

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.
机译:当在无标记蛋白质组学实验中限制样品重复时,对于具有单肽命中或小倍数变化的蛋白质,选择具有统计学意义的差异调节蛋白质仍然很困难。本文旨在解决这个问题。这里采用的方法的重要组成部分是利用最小有效置换对数(MPSP)的规则来减少误报。 MPSP规则从有限的分析重复中生成排列样本配对,并且仅要求仅当在指定数目的排列样本配对中发现显着差异的蛋白质时,才能选择差异调节的蛋白质。最初使用具有信噪比统计量(PLGEM-STN)和恒定倍数变化阈值的幂律全局误差模型来选择差异调节的蛋白质。但是,在本研究中,发现这两种方法都不够严格,无法将错误发现率控制在5%以下。另一方面,MPSP规则与这两种方法之一的组合可显着减少假阳性,对选择差异调节蛋白(包括单肽命中或变化<2倍的蛋白)的敏感性几乎没有影响。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号