Detecting differential protein abundance by combining peptide level P-values

Killinger, Bryan J.; Petyuk, Vladislav A.; Wright, Aaron T.

doi:10.1039/d0mo00045k

Detecting differential protein abundance by combining peptide level P-values

Journal Article · Fri Sep 11 04:00:00 EDT 2020 · Molecular Omics

DOI:https://doi.org/10.1039/d0mo00045k· OSTI ID:1752963

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Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Washington State Univ., Pullman, WA (United States)
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Washington State Univ., Pullman, WA (United States

The majority of methods for detecting differentially abundant proteins between samples in label-free LC-MS bottom-up proteomics experiments rely on statistically testing inferred protein abundances derived from peptide ionization intensities or averaging peptide level statistics. Here in this paper, we statistically test peptide ionization intensities directly and combine the resulting dependent P-values using the Empirical Brown's Method (EBM), avoiding error introduced through the estimation of protein abundances or summarizing test statistics. We show that on a spike-in proteomics dataset, a peptide level approach using EBM outperforms differential abundance detection using a protein level approach and several analysis workflows, including MSstats. Additionally, we demonstrate the effectiveness of this approach by detecting enriched proteins from an activity-based protein profiling dataset.

View Accepted Manuscript (DOE)

Research Organization:: Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)

Sponsoring Organization:: USDOE

Grant/Contract Number:: AC05-76RL01830; AC06-76RL01830

OSTI ID:: 1752963

Report Number(s):: PNNL-SA--144005

Journal Information:: Molecular Omics, Journal Name: Molecular Omics Journal Issue: 6 Vol. 16; ISSN 2515-4184

Publisher:: Royal Society of ChemistryCopyright Statement

Country of Publication:: United States

Language:: English

References (18)

Combining dependent P-values Kost, James T.; McDermott, Michael P. Statistics & Probability Letters, Vol. 60, Issue 2 https://doi.org/10.1016/S0167-7152(02)00310-3	journal	November 2002
Thousand and one ways to quantify and compare protein abundances in label-free bottom-up proteomics Blein-Nicolas, Mélisande; Zivy, Michel Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics, Vol. 1864, Issue 8 https://doi.org/10.1016/j.bbapap.2016.02.019	journal	August 2016
Multidimensional Tracking of GPCR Signaling via Peroxidase-Catalyzed Proximity Labeling Paek, Jaeho; Kalocsay, Marian; Staus, Dean P. Cell, Vol. 169, Issue 2 https://doi.org/10.1016/j.cell.2017.03.028	journal	April 2017
mapDIA: Preprocessing and statistical analysis of quantitative proteomics data from data independent acquisition mass spectrometry Teo, Guoshou; Kim, Sinae; Tsou, Chih-Chiang Journal of Proteomics, Vol. 129 https://doi.org/10.1016/j.jprot.2015.09.013	journal	November 2015
Using Peptide-Level Proteomics Data for Detecting Differentially Expressed Proteins Suomi, Tomi; Corthals, Garry L.; Nevalainen, Olli S. Journal of Proteome Research, Vol. 14, Issue 11 https://doi.org/10.1021/acs.jproteome.5b00363	journal	September 2015
Protein Analysis by Shotgun/Bottom-up Proteomics Zhang, Yaoyang; Fonslow, Bryan R.; Shan, Bing Chemical Reviews, Vol. 113, Issue 4 https://doi.org/10.1021/cr3003533	journal	February 2013
Activity-Based Probes for Isoenzyme- and Site-Specific Functional Characterization of Glutathione S -Transferases Stoddard, Ethan G.; Killinger, Bryan J.; Nair, Reji N. Journal of the American Chemical Society, Vol. 139, Issue 45 https://doi.org/10.1021/jacs.7b07378	journal	November 2017
The CPTAC Data Portal: A Resource for Cancer Proteomics Research Edwards, Nathan J.; Oberti, Mauricio; Thangudu, Ratna R. Journal of Proteome Research, Vol. 14, Issue 6 https://doi.org/10.1021/pr501254j	journal	May 2015
MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification Cox, Jürgen; Mann, Matthias Nature Biotechnology, Vol. 26, Issue 12 https://doi.org/10.1038/nbt.1511	journal	November 2008
Accurate Proteome-wide Label-free Quantification by Delayed Normalization and Maximal Peptide Ratio Extraction, Termed MaxLFQ Cox, Jürgen; Hein, Marco Y.; Luber, Christian A. Molecular & Cellular Proteomics, Vol. 13, Issue 9 https://doi.org/10.1074/mcp.M113.031591	journal	June 2014
Peptide-level Robust Ridge Regression Improves Estimation, Sensitivity, and Specificity in Data-dependent Quantitative Label-free Shotgun Proteomics Goeminne, Ludger J. E.; Gevaert, Kris; Clement, Lieven Molecular & Cellular Proteomics, Vol. 15, Issue 2 https://doi.org/10.1074/mcp.M115.055897	journal	November 2015
DAnTE: a statistical tool for quantitative analysis of -omics data Polpitiya, A. D.; Qian, W. -J.; Jaitly, N. Bioinformatics, Vol. 24, Issue 13 https://doi.org/10.1093/bioinformatics/btn217	journal	May 2008
MSstats: an R package for statistical analysis of quantitative mass spectrometry-based proteomic experiments Choi, Meena; Chang, Ching-Yun; Clough, Timothy Bioinformatics, Vol. 30, Issue 17 https://doi.org/10.1093/bioinformatics/btu305	journal	May 2014
Combining dependent P- values with an empirical adaptation of Brown’s method Poole, William; Gibbs, David L.; Shmulevich, Ilya Bioinformatics, Vol. 32, Issue 17 https://doi.org/10.1093/bioinformatics/btw438	journal	September 2016
UniProt: a hub for protein information Consortium, UniPot Nucleic Acids Research, Vol. 43, Issue D1, p. D204-D212 https://doi.org/10.1093/nar/gku989	journal	October 2014
Activity-Based Protein Profiling: From Enzyme Chemistry to Proteomic Chemistry Cravatt, Benjamin F.; Wright, Aaron T.; Kozarich, John W. Annual Review of Biochemistry, Vol. 77, Issue 1 https://doi.org/10.1146/annurev.biochem.75.101304.124125	journal	June 2008
Multiscale mutation clustering algorithm identifies pan-cancer mutational clusters associated with pathway-level changes in gene expression Poole, William; Leinonen, Kalle; Shmulevich, Ilya PLOS Computational Biology, Vol. 13, Issue 2 https://doi.org/10.1371/journal.pcbi.1005347	journal	February 2017
400: A Method for Combining Non-Independent, One-Sided Tests of Significance Brown, Morton B. Biometrics, Vol. 31, Issue 4 https://doi.org/10.2307/2529826	journal	December 1975

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