Covariation of Peptide Abundances Accurately Reflects Protein Concentration Differences
Open accessAbstract
Most implementations of mass spectrometry-based proteomics involve enzymatic digestion of proteins, expanding the analysis to multiple proteolytic peptides for each protein. Currently, there is no consensus of how to summarize peptides' abundances to protein concentrations, and such efforts are complicated by the fact that error control normally is applied to the identification process, and do not directly control errors linking peptide abundance measures to protein concentration. Peptides resulting from suboptimal digestion or being partially modified are not representative of the protein concentration. Without a mechanism to remove such unrepresentative peptides, their abundance adversely impacts the estimation of their protein's concentration. Here, we present a relative quantification approach, Diffacto, that applies factor analysis to extract the covariation of peptides' abundances. The method enables a weighted geometrical average summarization and automatic elimination of incoherent peptides. We demonstrate, based on a set of controlled label-free experiments using standard mixtures of proteins, that the covariation structure extracted by the factor analysis accurately reflects protein concentrations. In the 1% peptide-spectrum match-level FDR data set, as many as 11% of the peptides have abundance differences incoherent with the other peptides attributed to the same protein. If not controlled, such contradicting peptide abundance have a severe impact on protein quantifications. When adding the quantities of each protein's three most abundant peptides, we note as many as 14% of the proteins being estimated as having a negative correlation with their actual concentration differences between samples. Diffacto reduced the amount of such obviously incorrectly quantified proteins to 1.6%. Furthermore, by analyzing clinical data sets from two breast cancer studies, our method revealed the persistent proteomic signatures linked to three subtypes of breast cancer. We conclude that Diffacto can facilitate the interpretation and enhance the utility of most types of proteomics data.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Several proposed methods exclude peptide measurements from the aggregate protein quantity when they are outliers relative to other peptides mapping to the same protein-coding gene.
“The approach of several proposed methods focuses on the exclusion of peptide measurements from inclusion in the aggregate protein quantity if they are outliers from other peptide measurements mapping to the same protein coding gene.(28–33)”
From Putting humpty dumpty back together again: what does protein quantification mean in bottom-up proteomics? (2022) · cites this paper as [30]
Outlier-exclusion methods improve protein concentration estimates but still report only one protein quantity and ignore peptides that do not agree with that value.
“While these all demonstrate improved protein concentration estimates, they still only report a single protein quantity and ignore peptides that do not agree with that single value.”
From Putting humpty dumpty back together again: what does protein quantification mean in bottom-up proteomics? (2022) · cites this paper as [30]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Scikit-learn: Machine Learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., et al. (2012) · arXiv · doi:10.48550/arxiv.1201.0490 · Cited by 71,016 · see citing works
Cox, J., & Mann, M. (2008) · Nature Biotechnology · doi:10.1038/nbt.1511 · Cited by 14,855 · see citing works
Statistical significance for genomewide studies
Storey, J. D., & Tibshirani, R. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1530509100 · Cited by 9,692 · see citing works
Lehmann, B. D., Bauer, J. A., Chen, X., Sanders, M. E., Chakravarthy, A. B., Shyr, Y., & Pietenpol, J. A. (2011) · Journal of Clinical Investigation · doi:10.1172/jci45014 · Cited by 5,592 · see citing works
Cox, J., Hein, M. Y., Luber, C. A., Paron, I., Nagaraj, N., & Mann, M. (2014) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m113.031591 · Cited by 5,180 · see citing works
UniProt: a hub for protein information
The UniProt Consortium (2015) · Nucleic Acids Research · doi:10.1093/nar/gku989 · Cited by 5,106 · see citing works
Supervised Risk Predictor of Breast Cancer Based on Intrinsic Subtypes
Parker, J. S., Mullins, M., Cheang, M. C. U., Leung, S. C. Y., Voduc, D. K., Vickery, T. L., Davies, S. R., Fauron, C. M. ‐., He, X., Hu, Z., et al. (2009) · Journal of Clinical Oncology · doi:10.1200/jco.2008.18.1370 · Cited by 4,737 · see citing works
Expression monitoring by hybridization to high-density oligonucleotide arrays
Lockhart, D. J., Dong, H., Byrne, M. C., Follettie, M. T., Gallo, M. V., Chee, M. S., Mittmann, M., Wang, C., Kobayashi, M., Norton, H., et al. (1996) · Nature Biotechnology · doi:10.1038/nbt1296-1675 · Cited by 2,841 · see citing works
Ishihama, Y., Oda, Y., Tabata, T., Sato, T., Nagasu, T., Rappsilber, J., & Mann, M. (2005) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m500061-mcp200 · Cited by 2,055 · see citing works
A Model-Based Background Adjustment for Oligonucleotide Expression Arrays
Wu, Z. J., Irizarry, R. A., Gentleman, R. C., Martínez-Murillo, F., & Spencer, F. A. (2004) · Journal of the American Statistical Association · doi:10.1198/016214504000000683 · Cited by 1,717 · see citing works
Proteogenomics connects somatic mutations to signalling in breast cancer
CPTAC, N., Mertins, P., Mani, D. R., Ruggles, K. V., Gillette, M. A., Clauser, K. R., Wang, P., Wang, X., Qiao, J. W., Cao, S., et al. (2016) · Nature · doi:10.1038/nature18003 · Cited by 1,676 · see citing works
Self-citationMass spectrometry–based proteomics turns quantitative
Ong, S.-E., & Mann, M. (2005) · Nature Chemical Biology · doi:10.1038/nchembio736 · Cited by 1,572 · see citing works
Absolute Quantification of Proteins by LCMSE
Silva, J. C., Gorenstein, M. V., Li, G.-Z., Vissers, J. P. C., & Geromanos, S. J. (2006) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m500230-mcp200 · Cited by 1,529 · see citing works
Use of within-array replicate spots for assessing differential expression in microarray experiments
Smyth, G. K., Michaud, J., & Scott, H. S. (2005) · Computer applications in the biosciences · doi:10.1093/bioinformatics/bti270 · Cited by 1,406 · see citing works
Choi, M., Chang, C.-Y., Clough, T., Broudy, D., Killeen, T., MacLean, B., & Olga Vitek, A. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu305 · Cited by 1,041 · see citing works
Quantitative mass spectrometry in proteomics: critical review update from 2007 to the present
Bantscheff, M., Lemeer, S., Savitski, M. M., & Kuster, B. (2012) · Analytical and Bioanalytical Chemistry · doi:10.1007/s00216-012-6203-4 · Cited by 801 · see citing works
Is Proteomics the New Genomics?
Cox, J., & Mann, M. (2007) · Cell · doi:10.1016/j.cell.2007.07.032 · Cited by 444 · see citing works
How basal are triple‐negative breast cancers?
Bertucci, F., Finetti, P., Cervera, N., Esterni, B., Hermitte, F., Viens, P., & Birnbaum, D. (2008) · International Journal of Cancer · doi:10.1002/ijc.23518 · Cited by 443 · see citing works
A Scalable Approach for Protein False Discovery Rate Estimation in Large Proteomic Data Sets
Savitski, M. M., Wilhelm, M., Hahne, H., Küster, B., & Bantscheff, M. (2015) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m114.046995 · Cited by 436 · see citing works
Mass spectrometry–based proteomics in cell biology
Walther, T. C., & Mann, M. (2010) · The Journal of Cell Biology · doi:10.1083/jcb.201004052 · Cited by 411 · see citing works
Label-free, normalized quantification of complex mass spectrometry data for proteomic analysis
Griffin, N. M., Yu, J., Long, F., Oh, P., Shore, S., Li, Y., Koziol, J., & Schnitzer, J. E. (2009) · Nature Biotechnology · doi:10.1038/nbt.1592 · Cited by 400 · see citing works
Proteomic maps of breast cancer subtypes
Tyanova, S., Albrechtsen, R., Kronqvist, P., Cox, J., Mann, M., & Geiger, T. (2016) · Nature Communications · doi:10.1038/ncomms10259 · Cited by 327 · see citing works
TOPP—the OpenMS proteomics pipeline
Kohlbacher, O., Reinert, K., Gröpl, C., Lange, E., Pfeifer, N., Schulz-Trieglaff, O. B., & Sturm, M. (2007) · Bioinformatics · doi:10.1093/bioinformatics/btl299 · Cited by 281 · see citing works
Tang, K., Page, J. S., & Smith, R. (2004) · Journal of the American Society for Mass Spectrometry · doi:10.1016/j.jasms.2004.04.034 · Cited by 262 · see citing works
Novor: Real-Time Peptide de Novo Sequencing Software
Ma, B. (2015) · Journal of the American Society for Mass Spectrometry · doi:10.1007/s13361-015-1204-0 · Cited by 260 · see citing works
A new summarization method for affymetrix probe level data
Hochreiter, S., Clevert, D.-A., & Obermayer, K. (2006) · Bioinformatics · doi:10.1093/bioinformatics/btl033 · Cited by 249 · see citing works
Robust estimation of the false discovery rate
Pounds, S. B., & Cheng, C. (2006) · Bioinformatics · doi:10.1093/bioinformatics/btl328 · Cited by 235 · see citing works
Multiple testing corrections in quantitative proteomics: A useful but blunt tool
Pascovici, D., Handler, D. C. L., Wu, J. X., & Haynes, P. A. (2016) · PROTEOMICS · doi:10.1002/pmic.201600044 · Cited by 218 · see citing works
Goloborodko, A. A., Levitsky, L. I., Ivanov, M. V., & Gorshkov, M. V. (2013) · Journal of the American Society for Mass Spectrometry · doi:10.1007/s13361-012-0516-6 · Cited by 184 · see citing works
Ting, Y. S., Egertson, J. D., Payne, S. H., Kim, S., MacLean, B., Käll, L., Aebersold, R., Smith, R. D., Noble, W. S., & MacCoss, M. J. (2015) · Molecular & Cellular Proteomics · doi:10.1074/mcp.o114.047035 · Cited by 175 · see citing works
Self-citationStatistical Similarities between Transcriptomics and Quantitative Shotgun Proteomics Data
Pavelka, N., Fournier, M. L., Swanson, S. K., Pelizzola, M., Ricciardi‐Castagnoli, P., Florens, L. A., & Washburn, M. P. (2007) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m700240-mcp200 · Cited by 167 · see citing works
Breast cancer classification by proteomic technologies: Current state of knowledge
Lam, S. W., Jiménez, C. R., & Boven, E. (2013) · Cancer Treatment Reviews · doi:10.1016/j.ctrv.2013.06.006 · Cited by 162 · see citing works
A Proteomics Search Algorithm Specifically Designed for High-Resolution Tandem Mass Spectra
Wenger, C. D., & Coon, J. J. (2013) · Journal of Proteome Research · doi:10.1021/pr301024c · Cited by 158 · see citing works
Rapid and Deep Human Proteome Analysis by Single-dimension Shotgun Proteomics
Pirmoradian, M., Budamgunta, H., Chingin, K., Zhang, B., Astorga-Wells, J., & Zubarev, R. A. (2013) · Molecular & Cellular Proteomics · doi:10.1074/mcp.o113.028787 · Cited by 144 · see citing works
Self-citationClough, T., Thaminy, S., Ragg, S., Aebersold, R. H., & Olga Vitek, A. (2012) · BMC Bioinformatics · doi:10.1186/1471-2105-13-s16-s6 · Cited by 137 · see citing works
Moestue, S. A., Borgan, E., Huuse, E. M., Lindholm, E. M., Sitter, B., Børresen‐Dale, A., Engebraaten, O., Mælandsmo, G. M., & Gribbestad, I. S. (2010) · BMC Cancer · doi:10.1186/1471-2407-10-433 · Cited by 122 · see citing works
Talloen, W., Clevert, D.-A., Hochreiter, S., Amaratunga, D., Bijnens, L., Kass, S. U., & Göhlmann, H. W. H. (2007) · Bioinformatics · doi:10.1093/bioinformatics/btm478 · Cited by 117 · see citing works
Efficient error correction for next-generation sequencing of viral amplicons
Skums, P., Dimitrova, Z., Campo, D. S., et al. (2012) · BMC Bioinformatics · doi:10.1186/1471-2105-13-s10-s6 · Cited by 113 · see citing works
Goeminne, L. E., Gevaert, K., & Clement, L. (2016) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m115.055897 · Cited by 110 · see citing works
Latosińska, A., Vougas, K. N., Makridakis, M., Klein, J., Mullen, W., Abbas, M., STRAVODIMOS, K. G., Katafigiotis, I. S., Merseburger, A. S., Zoidakis, J., et al. (2015) · PLoS ONE · doi:10.1371/journal.pone.0137048 · Cited by 99 · see citing works
Lyutvinskiy, Y., Yang, H., Rutishauser, D., et al. (2013) · Molecular & Cellular Proteomics · doi:10.1074/mcp.o112.023804 · Cited by 95 · see citing works
Naven, T. J. P., & Harvey, D. J. (1996) · Rapid Communications in Mass Spectrometry · doi:10.1002/(sici)1097-0231(199608)10:11 · Cited by 90 · see citing works
DeNovoGUI: An Open Source Graphical User Interface for de Novo Sequencing of Tandem Mass Spectra
Muth, T., Weilnböck, L., Rapp, E., Huber, C. G., Martens, L., Vaudel, M., & Barsnes, H. (2013) · Journal of Proteome Research · doi:10.1021/pr4008078 · Cited by 83 · see citing works
A review of statistical methods for protein identification using tandem mass spectrometry
Noble, W. S., & Serang, O. (2012) · Statistics and Its Interface · doi:10.4310/sii.2012.v5.n1.a2 · Cited by 67 · see citing works
DeMix-Q: Quantification-Centered Data Processing Workflow
Zhang, B., Käll, L., & Zubarev, R. A. (2016) · Molecular & Cellular Proteomics · doi:10.1074/mcp.o115.055475 · Cited by 65 · see citing works
Self-citationZhang, B., Pirmoradian, M., Chernobrovkin, A. L., & Zubarev, R. A. (2014) · Molecular & Cellular Proteomics · doi:10.1074/mcp.o114.038877 · Cited by 56 · see citing works
Self-citationNoetzel, E., Rose, M., Sevinc, E., et al. (2010) · Oncogene · doi:10.1038/onc.2010.229 · Cited by 54 · see citing works
Solution to Statistical Challenges in Proteomics Is More Statistics, Not Less
Serang, O., & Käll, L. (2015) · Journal of Proteome Research · doi:10.1021/acs.jproteome.5b00568 · Cited by 50 · see citing works
Self-citationHow to talk about protein‐level false discovery rates in shotgun proteomics
The, M., Tasnim, A., & Käll, L. (2016) · PROTEOMICS · doi:10.1002/pmic.201500431 · Cited by 47 · see citing works
Self-citationChoi, M., DOĞU, Z. F. E., Colangelo, C. M., Cottrell, J. S., Hoopmann, M. R., Kapp, E. A., Kim, S., Lam, H., Neubert, T. A., Palmblad, M., et al. (2016) · Journal of Proteome Research · doi:10.1021/acs.jproteome.6b00881 · Cited by 45 · see citing works
Sequential Monte Carlo multiple testing
Sandve, G. K., Ferkingstad, E., & Nygård, S. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr568 · Cited by 44 · see citing works
Using Peptide-Level Proteomics Data for Detecting Differentially Expressed Proteins
Suomi, T., Corthals, G. L., Nevalainen, O., & Elo, L. L. (2015) · Journal of Proteome Research · doi:10.1021/acs.jproteome.5b00363 · Cited by 42 · see citing works
Bayesian Proteoform Modeling Improves Protein Quantification of Global Proteomic Measurements
Webb‐Robertson, B., Matzke, M. M., Datta, S., Payne, S., Kang, J., Bramer, L., Nicora, C., Shukla, A. K., Metz, T., Rodland, K., et al. (2014) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m113.030932 · Cited by 39 · see citing works
Statistical Approach to Protein Quantification
Gerster, S., Kwon, T., Ludwig, C., Matondo, M., Vogel, C., Marcotte, E. M., Aebersold, R. H., & Bühlmann, P. (2013) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m112.025445 · Cited by 36 · see citing works
Forshed, J., Johansson, H. J., Pernemalm, M., Branca, R. M. M., Sandberg, A., & Lehtiö, J. (2011) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m111.010264 · Cited by 32 · see citing works
Coupling proteomics and transcriptomics in the quest of subtype‐specific proteins in breast cancer
Pavlou, M. P., Dimitromanolakis, A., & Diamandis, E. P. (2013) · PROTEOMICS · doi:10.1002/pmic.201200526 · Cited by 31 · see citing works
SpliceVista, a Tool for Splice Variant Identification and Visualization in Shotgun Proteomics Data
Zhu, Y., Hultin‐Rosenberg, L., Forshed, J., Branca, R. M., Orre, L. M., & Lehtiö, J. (2014) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m113.031203 · Cited by 29 · see citing works
SpotLight Proteomics: uncovering the hidden blood proteome improves diagnostic power of proteomics
Lundström, S. L., Zhang, B., Rutishauser, D., Aarsland, D., & Zubarev, R. A. (2017) · Scientific Reports · doi:10.1038/srep41929 · Cited by 26 · see citing works
Self-citationKloten, V., Schlensog, M., Eschenbruch, J., et al. (2016) · PLoS ONE · doi:10.1371/journal.pone.0159073 · Cited by 16 · see citing works
Vincent, C. E., Potts, G. K., Ulbrich, A., Westphall, M. S., Atwood, J., Coon, J. J., & Weatherly, D. B. (2013) · Analytical Chemistry · doi:10.1021/ac303352n · Cited by 15 · see citing works
Ning, Z., Zhang, X., Mayne, J., & Figeys, D. (2016) · Analytical Chemistry · doi:10.1021/acs.analchem.5b04148 · Cited by 14 · see citing works
Protein Inference Using Peptide Quantification Patterns
Lukasse, P., & America, A. H. P. (2014) · Journal of Proteome Research · doi:10.1021/pr401072g · Cited by 13 · see citing works
Bayesian Proteoform Modeling Improves Protein Quantification of Global Proteomic Measurements
Webb-Robertson, B.-J. M., Matzke, M. M., Datta, S., et al. (2014) · Molecular & Cellular Proteomics · doi:10.1074/mcp.o113.030932 · Cited by 1 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Systematic detection of functional proteoform groups from bottom-up proteomic datasets
Bludau, I., Frank, M., Dörig, C., Cai, Y., Heusel, M., Rosenberger, G., Picotti, P., Collins, B. C., Röst, H., & Aebersold, R. (2021) · Nature Communications · doi:10.1038/s41467-021-24030-x · Cited by 88 · see citing works
Plubell, D. L., Käll, L., Webb-Robertson, B.-J., Bramer, L. M., Ives, A., Kelleher, N. L., Smith, L. M., Montine, T. J., Wu, C. C., & MacCoss, M. J. (2022) · Journal of Proteome Research · doi:10.1021/acs.jproteome.1c00894 · Cited by 85 · see citing works
Self-citationPeptide Correlation Analysis (PeCorA) Reveals Differential Proteoform Regulation
Dermit, M., Peters-Clarke, T. M., Shishkova, E., & Meyer, J. G. (2021) · Journal of Proteome Research · doi:10.1021/acs.jproteome.0c00602 · Cited by 40 · see citing works
Integrated Identification and Quantification Error Probabilities for Shotgun Proteomics
The, M., & Käll, L. (2019) · Molecular & Cellular Proteomics · doi:10.1074/mcp.ra118.001018 · Cited by 37 · see citing works
Self-citationTsai, T.-H., Choi, M., Banfai, B., Liu, Y., MacLean, B. X., Dunkley, T., & Vitek, O. (2020) · Molecular & Cellular Proteomics · doi:10.1074/mcp.ra119.001792 · Cited by 31 · see citing works
- Authors
- Zhang, B., Pirmoradian, M., Zubarev, R., & Käll, L.
- Affiliations
- Karolinska Institutet (SE)
- Karolinska University Hospital (SE)
- Science for Life Laboratory (SE)
- Corresponding author
- Zubarev, Roman
- Käll, Lukas
- Venue
- Molecular & Cellular Proteomics
- Publisher
- Elsevier BV
- Published
- 2017-05
- Volume
- 16
- Issue
- 5
- Pages
- 936-948
- ISSN
- 1535-9476 (ISSN-L)
- PMC
- PMC5417831
- OpenAlex
- W2596516271
- Cited by
- 68
- References
- 63
- Citations by year
- 2026: 3 · 2025: 7 · 2024: 3 · 2023: 11 · 2022: 9 · 2021: 8 · 2020: 8 · 2019: 8 · 2018: 9 · 2017: 2
- FWCI
- 3.64
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Molecular & Cellular Proteomics (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- Knut och Alice Wallenbergs Stiftelse (2010.0022)
- Topics
- Physical Sciences › Chemistry › Spectroscopy › Advanced Proteomics Techniques and Applications
- Physical Sciences › Chemistry › Spectroscopy › Mass Spectrometry Techniques and Applications
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Advanced Biosensing Techniques and Applications
- Keywords
- factor analysis, label-free quantification, peptide-spectrum matches, false discovery rate, breast cancer subtypes
- MeSH terms
- Breast Neoplasms, Female, Humans, Peptides, Proteins, Saccharomyces cerevisiae, Software, Sequence Analysis, Protein, Proteomics, Signal-To-Noise Ratio
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
