An assessment of false discovery rates and statistical significance in label-free quantitative proteomics with combined filters.
An assessment of false discovery rates and statistical significance in label-free quantitative proteomics with combined filters.
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DOI：
10.1186/1471-2105-10-43
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发表时间：
2009-02-02
影响因子：
3
通讯作者：
Roxas, Bryan A. P.
中科院分区：
文献类型：
--
作者：
Li, Qingbo;Roxas, Bryan A. P.
许多研究已经提供了算法或方法来评估定量蛋白质组学中的统计学显著性，当蛋白质样品的多个重复和LC/MS分析可用时。但是，在没有蛋白质样品重复的情况下使用数据集进行生物学解释仍然缺乏信心。尽管倍数变化是当没有样品重复时可以使用的常规阈值，但它不提供统计学显著性的评估，例如错误发现率（FDR），其是鉴定差异表达蛋白质的可靠性的重要指标。在这项工作中，我们调查是否差异表达的蛋白质可以从一对未标记的蛋白质样品中检测到具有统计学意义，没有重复，每个样品只有重复的LC/MS注射。FDR用于测量差异表达蛋白的统计学显著性。我们已经实验了几个参数来控制FDR，包括倍数变化，统计检验和最小数量的置换显著配对。虽然这些参数中没有一个单独给出了一个令人满意的控制FDR，我们发现，这些参数的组合提供了一个非常有效的手段来控制FDR，而不损害灵敏度。结果表明，可以在没有蛋白质样品重复的情况下进行显著性分析。每份样品仅需重复LC/MS进样。我们说明，差异表达的蛋白质可以检测到FDR在0和15%之间，阳性率为4- 16%。通过ROC分析评价该方法的灵敏度和特异性，并使用[15 N]标记的内标蛋白样品和额外的未标记蛋白样品重复进行进一步验证。我们证明了在无标记定量蛋白质组学中，可以在没有蛋白质样品重复的情况下推断出统计学显著性。在这项研究中描述的方法将是有用的，在许多探索性的实验中，样品量或仪器的时间是有限的。当然，这种方法也适用于蛋白质组学实验，其中多个样品重复可用。它很简单，并且与其他更复杂的算法互补，这些算法不是为处理少量样本重复而设计的。
Many studies have provided algorithms or methods to assess a statistical significance in quantitative proteomics when multiple replicates for a protein sample and a LC/MS analysis are available. But, confidence is still lacking in using datasets for a biological interpretation without protein sample replicates. Although a fold-change is a conventional threshold that can be used when there are no sample replicates, it does not provide an assessment of statistical significance such as a false discovery rate (FDR) which is an important indicator of the reliability to identify differentially expressed proteins. In this work, we investigate whether differentially expressed proteins can be detected with a statistical significance from a pair of unlabeled protein samples without replicates and with only duplicate LC/MS injections per sample. A FDR is used to gauge the statistical significance of the differentially expressed proteins. We have experimented to operate on several parameters to control a FDR, including a fold-change, a statistical test, and a minimum number of permuted significant pairings. Although none of these parameters alone gives a satisfactory control of a FDR, we find that a combination of these parameters provides a very effective means to control a FDR without compromising the sensitivity. The results suggest that it is possible to perform a significance analysis without protein sample replicates. Only duplicate LC/MS injections per sample are needed. We illustrate that differentially expressed proteins can be detected with a FDR between 0 and 15% at a positive rate of 4–16%. The method is evaluated for its sensitivity and specificity by a ROC analysis, and is further validated with a [15N]-labeled internal-standard protein sample and additional unlabeled protein sample replicates. We demonstrate that a statistical significance can be inferred without protein sample replicates in label-free quantitative proteomics. The approach described in this study would be useful in many exploratory experiments where a sample amount or instrument time is limited. Naturally, this method is also suitable for proteomics experiments where multiple sample replicates are available. It is simple, and is complementary to other more sophisticated algorithms that are not designed for dealing with a small number of sample replicates.
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影响因子：
12.3
作者：
Levine, David M.;Haynor, David R.;Johnson, Jason M.
通讯作者：
Johnson, Jason M.
影响因子：
4.4
作者：
Andreev, Victor P.;Li, Lingyun;Karger, Barry L.
通讯作者：
Karger, Barry L.
DOI：
10.1073/pnas.091062498
发表时间：
2001-04-24
影响因子：
11.1
作者：
Tusher, VG;Tibshirani, R;Chu, G
通讯作者：
Chu, G
影响因子：
4.4
作者：
Du, Xiuxia;Yang, Feng;Smith, Richard D.
通讯作者：
Smith, Richard D.
影响因子：
4.8
作者：
Hochleitner, EO;Kastner, B;Lottspeich, F
通讯作者：
Lottspeich, F