Clement, Lieven

Goeminne, Ludger

Martens, Lennart

Sticker, Adriaan

English

Ghent University Academic Bibliography

statOmics adriaan.sticker@ugent.beRobust summarization and inference in label-free quantificationAdriaan Sticker1,2,3,4, Ludger Goeminne1,2,3,4, Lennart Martens2,3,4 & Lieven Clement1,41 Department of Applied Mathematics, Computer Science & Statistics, Ghent University, Belgium 2 VIB-UGent Center for Medical Biotechnology, VIB, Ghent, Belgium3 UGent Department of Biomolecular Medicine, Ghent University, Ghent, Belgium 4 Bioinformatics Institute Ghent, Ghent University, Ghent, BelgiumBackgroundLabel-Free Quantitative (LFQ) mass spectrometry is standard for differential expres-sion (DE) analysis, but protein abundance estimation remains challenging:1. Peptide-specific effects: Different peptides from a protein have distinctphysio-chemical properties leading to high variability in MS1 intensities.2. Context-sensitive missingness: Peptides sometimes stay unidentified,especially low abundant ones co-eluting with high abundant ones get missing.Summarization methods aggregate MS1 peptide to protein intensities and DE anal-ysis is done on these protein summaries. The state-of-the-art peptide-based modelMSqRob [1] tests for DE directly from peptide intensities using a linear model:yitsp = β0i + βtreatmentit + βsampleis + βpeptideip + εiWith yitsp the normalised log2 intensity of peptide p in protein i from sample s withtreatment t . Parameter estimators variability is reduced through ridge penalisation,outliers are weighed down by M-estimation using Huber weights and the variance ofεi is estimated with an empirical Bayes estimator. This protects against overfittingand gives more stable and accurate estimators, especially with few observations.ProblemThe summarization strategies show huge differences in performance dependingon the absolute and relative abundances of proteins between conditions and noneclearly outperforms others. By reducing bias and improved uncertainty estimation,MSqRob has more precise and more accurate FC estimates then any other methodand always outperforms the others for DE analysis (Figure 1).MSqRob still has some drawbacks:1. Fitting models on complex experimental designs is computational costly.2. The residual degrees of freedoms are unclear due to nonrandom missingness.3. Peptide random effects in a MSqRob mixed model often confuses end-users.4. Protein summaries for visualisation or downstream processing are unavailable.Figure 1: Comparison of current state-of-the-art tools for DE analysis. Panel A shows theperformance and panel B the estimated log2 FC of DE and non-DE proteins in a vs b.Materials and MethodsBenchmark dataset All software tools for DE analysis are benchmarked on a publically availabledataset (PRIDE identifier: PXD003881) [2]. E. Coli lysates were spiked at 5 concentrations (a: 3%,b: 4.5%, c: 6%, d: 7.5% and e: 9% wt/wt) in a stable human background (4 replicates/treatment).E.coli proteins are labelled as DE (true positives) and human proteins stay constant (true negatives).Summarization-based LFQ methods We compare 4 state-of-the-art software packages: Proteus[3] uses high-flyers summarization. Perseus [4] and the Differential Enrichment analysis of Pro-teomics data (DEP) package[5] use maxquant’s maxLFQ summarization. DEP also benefits fromvsn-normalisation and mixed imputation. MSstats[6] uses median polish summarization.References[1] L. J. E. Goeminne, A. Argentini, L. Martens, and L. Clement, “Summarization vs peptide-based models in label-free quantitative proteomics:Performance, pitfalls, and data analysis guidelines,” Journal of Proteome Research, vol. 14, p. 2457–2465, May 2015.[2] X. Shen, S. Shen, J. Li, Q. Hu, L. Nie, C. Tu, X. Wang, D. J. Poulsen, B. C. Orsburn, and J. Wang, “Ionstar enables high-precision,low-missing-data proteomics quantification in large biological cohorts,” Proceedings of the National Academy of Sciences, vol. 115,pp. E4767–E4776, May 2018.[3] M. Gierlinski, F. Gastaldello, C. Cole, and G. J. Barton, “Proteus: an r package for downstream analysis of maxquant output,” Sep 2018.[4] J. Cox, M. Y. Hein, C. A. Luber, I. Paron, N. Nagaraj, and M. Mann, “Accurate proteome-wide label-free quantification by delayednormalization and maximal peptide ratio extraction, termed maxlfq,” Molecular & Cellular Proteomics, vol. 13, pp. 2513–2526, Jun 2014.[5] X. Zhang, A. H. Smits, G. B. van Tilburg, H. Ovaa, W. Huber, and M. Vermeulen, “Proteome-wide identification of ubiquitin interactions usingubia-ms,” Nature Protocols, vol. 13, p. 530–550, Feb 2018.[6] M. Choi, C.-Y. Chang, T. Clough, D. Broudy, T. Killeen, B. MacLean, and O. Vitek, “Msstats: an r package for statistical analysis ofquantitative mass spectrometry-based proteomic experiments,” Bioinformatics, vol. 30, p. 2524–2526, May 2014.SolutionWe propose a novel summarization strategy, MSqRobSum, which trains MSqRobin a two-stage procedure circumventing the drawbacks of MSqRob.1. Peptide intensities of a protein are aggregated using robust regression withM-estimation using Huber weights using a protein-wise linear model:yisp = βsampleis + βpeptideip + εiBy encoding the peptide effect as a sum contrast, βsampleis can be interpreted asthe mean intensity for protein i in sample s.2. A simplified MSqRob analysis is done on these protein summaries:yit = β0i + βtreatmentit + εiMSqRobSum has similar performance to MSqRob for medium to highly DE proteinsand only breaks down at increasingly lower fold changes in DE (Figure 2). However,all summarization methods drop in performance at lower fold changes. The FDR isalways correctly controlled except in comparisons c-a and d-a due to ion competition.Figure 2:Performance of MSqRob vs MSqRobSum.Modular workflowsMSqRobSum enables a more modular workflow, which exist of three steps: prepro-cessing, summarization with robust regression and DE analysis with robust ridgeregression. All steps can have an important impact on the performance of the entireDE data analysis. Figure 3 shows how we incrementally improve the performanceof a DE analysis by changing parts of the workflow from a default Perseus analysesto our MSqRob workflow.MSqRobSum provides robust protein summaries for visualisation and integration inother tools for DE. MSqRob now also work with protein summaries from other tools.ConclusionI MSqRobSum enables fast and robust DE analysisI and flexible modular workflows for specific applications.’msqrobsum’ R package:Integrated in R’s proteomics ecosystem (through MSnBase)In developmentFigure 3: Improving DE analysis in a modular data analysis workflow.

Robust summarisation and inference for Label-Free Quantification

https://biblio.ugent.be/publication/8601169/file/8601325.pdf

Robust summarisation and inference for Label-Free Quantification

Abstract

Similar works

Full text

Available Versions

Ghent University Academic Bibliography