检测蛋白质丰度的显著变化。
Detecting Significant Changes in Protein Abundance.
作者信息
Kammers Kai, Cole Robert N, Tiengwe Calvin, Ruczinski Ingo
机构信息
Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
Mass Spectrometry and Proteomics Core Facility, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
出版信息
EuPA Open Proteom. 2015 Jun;7:11-19. doi: 10.1016/j.euprot.2015.02.002.
Abstract
We review and demonstrate how an empirical Bayes method, shrinking a protein's sample variance towards a pooled estimate, leads to far more powerful and stable inference to detect significant changes in protein abundance compared to ordinary t-tests. Using examples from isobaric mass labeled proteomic experiments we show how to analyze data from multiple experiments simultaneously, and discuss the effects of missing data on the inference. We also present easy to use open source software for normalization of mass spectrometry data and inference based on moderated test statistics.
摘要
我们回顾并展示了一种经验贝叶斯方法，即将蛋白质的样本方差向合并估计值收缩，与普通t检验相比，该方法在检测蛋白质丰度的显著变化时能带来更强大且稳定的推断。通过等压质量标记蛋白质组学实验的示例，我们展示了如何同时分析来自多个实验的数据，并讨论了缺失数据对推断的影响。我们还提供了易于使用的开源软件，用于质谱数据的标准化以及基于适度检验统计量的推断。
相似文献
1
Detecting Significant Changes in Protein Abundance.
EuPA Open Proteom. 2015 Jun;7:11-19. doi: 10.1016/j.euprot.2015.02.002.
2
Linear models and empirical bayes methods for assessing differential expression in microarray experiments.
Stat Appl Genet Mol Biol. 2004;3:Article3. doi: 10.2202/1544-6115.1027. Epub 2004 Feb 12.
3
Protein quantification in label-free LC-MS experiments.
J Proteome Res. 2009 Nov;8(11):5275-84. doi: 10.1021/pr900610q.
4
Detecting Modifications in Proteomics Experiments with Param-Medic.
J Proteome Res. 2019 Apr 5;18(4):1902-1906. doi: 10.1021/acs.jproteome.8b00954. Epub 2019 Mar 5.
5
A multi-model statistical approach for proteomic spectral count quantitation.
J Proteomics. 2016 Jul 20;144:23-32. doi: 10.1016/j.jprot.2016.05.032. Epub 2016 May 31.
6
On the beta-binomial model for analysis of spectral count data in label-free tandem mass spectrometry-based proteomics.
Bioinformatics. 2010 Feb 1;26(3):363-9. doi: 10.1093/bioinformatics/btp677. Epub 2009 Dec 9.
7
NormalyzerDE: Online Tool for Improved Normalization of Omics Expression Data and High-Sensitivity Differential Expression Analysis.
J Proteome Res. 2019 Feb 1;18(2):732-740. doi: 10.1021/acs.jproteome.8b00523. Epub 2018 Oct 15.
8
Statistical design and analysis of label-free LC-MS proteomic experiments: a case study of coronary artery disease.
Methods Mol Biol. 2011;728:293-319. doi: 10.1007/978-1-61779-068-3_20.
9
Empirical Bayes method for reducing false discovery rates of correlation matrices with block diagonal structure.
BMC Bioinformatics. 2017 Apr 12;18(1):213. doi: 10.1186/s12859-017-1623-y.
10
Protein inference: A protein quantification perspective.
Comput Biol Chem. 2016 Aug;63:21-29. doi: 10.1016/j.compbiolchem.2016.02.006. Epub 2016 Feb 13.
引用本文的文献
1
The late stages of yeast mitoribosome large subunit biogenesis.
Biochim Biophys Acta Mol Cell Res. 2025 Aug 25;1872(8):120051. doi: 10.1016/j.bbamcr.2025.120051.
2
Quantitative Proteomics Reveals Fh15 as an Antagonist of TLR4 Downregulating the Activation of NF-κB, Inducible Nitric Oxide, Phagosome Signaling Pathways, and Oxidative Stress of LPS-Stimulated Macrophages.
Int J Mol Sci. 2025 Jul 18;26(14):6914. doi: 10.3390/ijms26146914.
3
Proteomic aging signatures across mouse organs and life stages.
EMBO J. 2025 Jul 15. doi: 10.1038/s44318-025-00509-x.
4
Mitochondrial proteomic adaptations to daily torpor in the Djungarian hamster (Phodopus sungorus).
J Comp Physiol B. 2025 Jul 15. doi: 10.1007/s00360-025-01625-0.
5
Behavioral assessment and gene expression changes in a mouse model with dysfunctional STAT1 signaling.
Cell Commun Signal. 2025 Jul 1;23(1):305. doi: 10.1186/s12964-025-02313-w.
6
Cardiovascular protein profiling in patients with first-episode psychosis.
Schizophrenia (Heidelb). 2025 Jun 14;11(1):88. doi: 10.1038/s41537-025-00633-x.
7
Spatiotemporal deciphering of dynamic the FUS interactome during liquid-liquid phase separation in living cells.
Nat Commun. 2025 May 9;16(1):4328. doi: 10.1038/s41467-025-59457-z.
8
Methodological Aspects of μLC-MS/MS for Wide-Scale Proteomic Analysis of Anthracycline-Induced Cardiomyopathy.
ACS Omega. 2025 Mar 18;10(12):11980-11993. doi: 10.1021/acsomega.4c09377. eCollection 2025 Apr 1.
9
Chronic low-dose rate irradiation induces transient hormesis effect on cyanobacterium .
iScience. 2025 Jan 25;28(3):111891. doi: 10.1016/j.isci.2025.111891. eCollection 2025 Mar 21.
10
Time-resolved proteomic profiling of Cupriavidus metallidurans CH34 in the copper-induced viable-but-nonculturable state.
Metallomics. 2025 Mar 7;17(3). doi: 10.1093/mtomcs/mfaf007.
本文引用的文献
1
MSstats: an R package for statistical analysis of quantitative mass spectrometry-based proteomic experiments.
Bioinformatics. 2014 Sep 1;30(17):2524-6. doi: 10.1093/bioinformatics/btu305. Epub 2014 May 2.
2
Adaption of the global test idea to proteomics data with missing values.
Bioinformatics. 2014 May 15;30(10):1424-30. doi: 10.1093/bioinformatics/btu062. Epub 2014 Jan 30.
3
Assessment and improvement of statistical tools for comparative proteomics analysis of sparse data sets with few experimental replicates.
J Proteome Res. 2013 Sep 6;12(9):3874-83. doi: 10.1021/pr400045u. Epub 2013 Aug 5.
4
The Application of SILAC Mouse in Human Body Fluid Proteomics Analysis Reveals Protein Patterns Associated with IgA Nephropathy.
Evid Based Complement Alternat Med. 2013;2013:275390. doi: 10.1155/2013/275390. Epub 2013 May 15.
5
Using R and Bioconductor for proteomics data analysis.
Biochim Biophys Acta. 2014 Jan;1844(1 Pt A):42-51. doi: 10.1016/j.bbapap.2013.04.032. Epub 2013 May 18.
6
Statistical inference from multiple iTRAQ experiments without using common reference standards.
J Proteome Res. 2013 Feb 1;12(2):594-604. doi: 10.1021/pr300624g. Epub 2013 Jan 16.
7
Normalization and missing value imputation for label-free LC-MS analysis.
BMC Bioinformatics. 2012;13 Suppl 16(Suppl 16):S5. doi: 10.1186/1471-2105-13-S16-S5. Epub 2012 Nov 5.
8
A cross-platform toolkit for mass spectrometry and proteomics.
Nat Biotechnol. 2012 Oct;30(10):918-20. doi: 10.1038/nbt.2377.
9
OCAP: an open comprehensive analysis pipeline for iTRAQ.
Bioinformatics. 2012 May 15;28(10):1404-5. doi: 10.1093/bioinformatics/bts150. Epub 2012 Mar 29.
10
Computational mass spectrometry-based proteomics.
PLoS Comput Biol. 2011 Dec;7(12):e1002277. doi: 10.1371/journal.pcbi.1002277. Epub 2011 Dec 1.
Zotero 插件