Clement, Lieven

Gevaert, Kris

Goeminne, Ludger

Vandepoele, Klaas

English

Ghent University Academic Bibliography

Peptide-level robust ridge regression modeling improves both sensitivity and specificity in quantitative proteomicsLudger Goeminne Promotors:Lieven ClementKris GevaertKlaas VandepoeleA story about relative protein quantificationin label-free shotgun proteomicsOutline• Problem: reliable differential quantitation• Solution: peptide-based models• Improving solution via:1. Shrinkage estimation2. Borrowing information across proteins3. Weighing down outliers• Leads to:1. Better fold change estimates2. Better sensitivity and specificity• Conclusions: all of the above• AcknowledgementsFor each protein:Problem: how to do differential quantification?22.6464 Treatment 1 Peptide A Rep 117.85773 Treatment 1 Peptide B Rep 115.4947 Treatment 1 Peptide C Rep 214.02125 Treatment 1 Peptide D Rep 218.0965 Treatment 2 Peptide A Rep 314.59100 Treatment 2 Peptide B Rep 314.2959 Treatment 2 Peptide C Rep 3𝑡𝑟𝑒𝑎𝑡𝑚𝑒𝑛𝑡 + 𝑝𝑒𝑝𝑡𝑖𝑑𝑒 + 𝑟𝑒𝑝𝑒𝑎𝑡log2 𝑀𝑆 𝑖𝑛𝑡𝑒𝑛𝑠𝑖𝑡𝑦Solution: 2 main ways:Problem: how to do differential quantification?NormalisationNormalisation+summarization(e.g. MaxLFQ)T-tests(e.g. Perseus)PeptideintensitiesPeptide-basedmodels1. Summarizeto proteinlevel2. Peptide-based modelsPeptide-based models are superiorDaly, et al. (2008), Journal of Proteome Research, 7, (3), 1209-1217.Clough et al. (2009, Journal of Proteome Research, 8, (11), 5275-5284.Karpievitch et al. (2009), Bioinformatics, 25, (16), 2028-2034.Goeminne et al. (2015), Journal of Proteome Research , 14, (6), 2457-2465.Spike-in: 48 human proteins in yeast proteomeA model for each protein:Peptide-based models22.6464 Intercept Treatment 1 Peptide A Rep 1 Error 117.85773 Intercept Treatment 1 Peptide B Rep 1 Error 215.4947 Intercept Treatment 1 Peptide C Rep 2 Error 314.02125 Intercept Treatment 1 Peptide D Rep 2 Error 418.0965 Intercept Treatment 2 Peptide A Rep 3 Error 514.59100 Intercept Treatment 2 Peptide B Rep 3 Error 614.2959 Intercept Treatment 2 Peptide C Rep 3 Error 7log2 𝑀𝑆 𝑖𝑛𝑡𝑒𝑛𝑠𝑖𝑡𝑦 ~ 𝑖𝑛𝑡𝑒𝑟𝑐𝑒𝑝𝑡 + 𝑡𝑟𝑒𝑎𝑡𝑚𝑒𝑛𝑡 + 𝑝𝑒𝑝𝑡𝑖𝑑𝑒 + 𝑟𝑒𝑝𝑒𝑎𝑡 + 𝑒𝑟𝑟𝑜𝑟A model for each protein:Peptide-based models22.6464 16 1.5 4.5 0.5 0.146417.85773 16 1.5 -0.2 0.5 0.0577315.4947 16 1.5 -1 -0.7 -0.305314.02125 16 1.5 -2 -0.7 -0.7787518.0965 16 -1.5 4.5 -0.3 -0.603514.59100 16 -1.5 -0.2 -0.3 0.5910014.2959 16 -1.5 -1 -0.3 0.0959log2 𝑀𝑆 𝑖𝑛𝑡𝑒𝑛𝑠𝑖𝑡𝑦 ~ 𝑖𝑛𝑡𝑒𝑟𝑐𝑒𝑝𝑡 + 𝑡𝑟𝑒𝑎𝑡𝑚𝑒𝑛𝑡 + 𝑝𝑒𝑝𝑡𝑖𝑑𝑒 + 𝑟𝑒𝑝𝑒𝑎𝑡 + 𝑒𝑟𝑟𝑜𝑟A model for each protein:Peptide-based models22.6464 16 1.5 4.5 0.5 0.146417.85773 16 1.5 -0.2 0.5 0.0577315.4947 16 1.5 -1 -0.7 -0.305314.02125 16 1.5 -2 -0.7 -0.7787518.0965 16 -1.5 4.5 -0.3 -0.603514.59100 16 -1.5 -0.2 -0.3 0.5910014.2959 16 -1.5 -1 -0.3 0.0959log2 𝑀𝑆 𝑖𝑛𝑡𝑒𝑛𝑠𝑖𝑡𝑦 ~ 𝑖𝑛𝑡𝑒𝑟𝑐𝑒𝑝𝑡 + 𝑡𝑟𝑒𝑎𝑡𝑚𝑒𝑛𝑡 + 𝑝𝑒𝑝𝑡𝑖𝑑𝑒 + 𝑟𝑒𝑝𝑒𝑎𝑡 + 𝑒𝑟𝑟𝑜𝑟A model for each protein:Peptide-based models22.6464 1.517.85773 1.515.4947 1.514.02125 1.518.0965 -1.514.59100 -1.514.2959 -1.5log2 𝑀𝑆 𝑖𝑛𝑡𝑒𝑛𝑠𝑖𝑡𝑦 ~ 𝑖𝑛𝑡𝑒𝑟𝑐𝑒𝑝𝑡 + 𝑡𝑟𝑒𝑎𝑡𝑚𝑒𝑛𝑡 + 𝑝𝑒𝑝𝑡𝑖𝑑𝑒 + 𝑟𝑒𝑝𝑒𝑎𝑡 + 𝑒𝑟𝑟𝑜𝑟1.5-1.5- 31. Unstable DA estimates2. Unstablevarianceestimates3. OutliersStill some issues…A yeast null proteinStructure of my presentation• Problem: reliable differential quantitation• Solution: peptide-based models• Improving solution via:1. Shrinkage estimation2. Borrowing information across proteins3. Weighing down outliers• Leads to:1. Better fold change estimates2. Better sensitivity and specificity• Conclusions: all of the above• AcknowledgementsProblem1. Unstable DA estimates2. Unstable varianceestimates3. OutliersSolution1. Shrinkage estimation2. Borrow informationacross proteins3. Weigh down outlyingpeptidesHow can we improve upons existing peptide-based models?This will lead to:1. Better fold change estimates2. Better ranking1. Shrinkage estimationE.g. ridge regression: minimize the following lossfunction: 𝑦 − 𝑋  𝛽2+ 𝜆𝑡𝑟𝑒𝑎𝑡   𝛽𝑡𝑟𝑒𝑎𝑡2 + 𝜆𝑝𝑒𝑝   𝛽𝑝𝑒𝑝2 + 𝜆𝑖𝑛𝑠𝑡𝑟   𝛽𝑖𝑛𝑠𝑡𝑟2• Penalty on the effect sizes: shrinkage toward 0• Biased but (much) more stable estimator• Sparse data: shrinkage ↗• 𝜆s: via cross-validation or link with mixed models2. Borrow information across proteins:Empirical Bayes variance estimation𝝈𝒊𝟐𝝈𝟎𝟐 𝝈𝒊𝟐  𝝈𝒊𝟐  𝝈𝒊𝟐𝝈𝒊𝟐 𝝈𝒊𝟐Data decides!• Stabilizes variance estimates• Get rid of proteins with low fold changes and low variance caused by data sparsityMore details (limma paper):Smyth (2004), Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol, 3, Article3.3. Weigh down outlying peptidesE.g. M estimation with Huber weightsMinimize the following loss function: 𝒘 𝑦 − 𝑋  𝛽2+ 𝜆𝑡𝑟𝑒𝑎𝑡   𝛽𝑡𝑟𝑒𝑎𝑡2 + 𝜆𝑝𝑒𝑝   𝛽𝑝𝑒𝑝2 + 𝜆𝑖𝑛𝑠𝑡𝑟   𝛽𝑖𝑛𝑠𝑡𝑟2• Weigh down outlying observations1. Better fold change estimates-> More accurate andmore precise2. Better specificity andsensitivity-> Improved rankingResults!1. Better fold change estimatesFor null proteins1. Better fold change estimatesFor differentially abundant proteins2. Better sensitivity and specificityConclusions1. Use peptide-based models2. Our peptide-based model uses: 1. Shrinkage estimation2. Empirical Bayes variance estimation3. Downweighing of outliers3. Advantages:1. More stable fold change estimates2. Better sensititivity and specificityPapersGoeminne et al. (2015), Summarization vs. Peptide-Based Models in Label-free Quantitative Proteomics: Performance, Pitfalls and Data Analysis Guidelines. Journal of Proteome Research.Goeminne et al. (2015), Peptide-level robust ridge regression modeling with Empirical Bayes variance estimation and M estimation improves both sensitivity and specificity in quantitative label-free shotgun proteomics, submitted.Acknowledgements: peopleLieven Clement+ lab membersKris Gevaert+ lab membersLennart MartensAndrea ArgentiniKlaas VandepoeleAcknowledgements: organizationsThank you for your attention!Questions?

Peptide-level robust ridge regression modeling improves both sensitivity and specificity in quantitative proteomics

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Peptide-level robust ridge regression modeling improves both sensitivity and specificity in quantitative proteomics

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