ArticleMolecular & cellular proteomics : MCP2020
Robust Summarization and Inference in Proteome-wide Label-free Quantification.
Article in Molecular & cellular proteomics : MCP, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers.
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Who cites it
51 citing papers in PubMed.
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- Ovalbumin oxidative modification fingerprints depend on gas plasma-driven reactive species profiles.Redox report : communications in free radical research · 2026Article
- Multi-omics integration of proteomics and metabolomics in pediatric health and disease.Communications medicine · 2026Review
- Label-Free Targeted Proteomics Data Analysis Workflow Selection - Benchmarking AI-based and Data-Driven Approaches.Molecular & cellular proteomics : MCP · 2026Article
- DiaReport: reproducible workflow for differential expression analysis and interactive reporting in DIA-based proteomics.Bioinformatics (Oxford, England) · 2026Article
- omicsGMF: a multi-tool for dimensionality reduction, batch correction and imputation in bulk- and single-cell proteomics.Nature communications · 2026Article
- GRASPing experience-dependent protein expression signatures enriched for hippocampal engram cell synapses.Science advances · 2026Article
- Modeling CLN3 Batten disease in astrocytes reveals alterations in mitochondria homeostasis, fatty acid metabolism and oxidative stress response.Journal of biomedical science · 2026Article
- JUMPlion improves quantitative DIA proteomics through ion-level recovery of missing values.bioRxiv : the preprint server for biology · 2026Article
- Dynamic subcellular proteomics identifies regulators of adipocyte insulin action.Nature communications · 2026Article
- The Ste20 kinase TAOK3 restrains Rac-driven cytoskeletal-mitochondrial coupling to preserve naive CD8Frontiers in immunology · 2026Article
- Increased plasma fibronectin mirrors intimal phenotypic switching of vascular smooth muscle cells in moyamoya arteriopathy.Scientific reports · 2025Article
- A shrinkage-based statistical method for testing group mean differences in quantitative bottom-up proteomics.BMC bioinformatics · 2025Article
- Aflatoxin B1 induces subtle but coordinated histone modifications in Epstein-Barr virus infected and non-infected Burkitt lymphoma cells.Environment international · 2025Article
- Detection and Quantitation of Small Proteins Using Mass Spectrometry.Molecular & cellular proteomics : MCP · 2025Article
- msqrob2TMT: Robust Linear Mixed Models for Inferring Differential Abundant Proteins in Labeled Experiments With Arbitrarily Complex Design.Molecular & cellular proteomics : MCP · 2025Article
- Systematic evaluation of normalization approaches in tandem mass tag and label-free protein quantification data using PRONE.Briefings in bioinformatics · 2025Article
- Pupylation-Based Proximity Labeling Unravels a Comprehensive Protein and Phosphoprotein Interactome of the Arabidopsis TOR Complex.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Relative quantification of proteins and post-translational modifications in proteomic experiments with shared peptides: a weight-based approach.Bioinformatics (Oxford, England) · 2025Article
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4 authors.
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Abstract
Label-Free Quantitative mass spectrometry based workflows for differential expression (DE) analysis of proteins impose important challenges on the data analysis because of peptide-specific effects and context dependent missingness of peptide intensities. Peptide-based workflows, like MSqRob, test for DE directly from peptide intensities and outperform summarization methods which first aggregate MS1 peptide intensities to protein intensities before DE analysis. However, these methods are computationally expensive, often hard to understand for the non-specialized end-user, and do not provide protein summaries, which are important for visualization or downstream processing. In this work, we therefore evaluate state-of-the-art summarization strategies using a benchmark spike-in dataset and discuss why and when these fail compared with the state-of-the-art peptide based model, MSqRob. Based on this evaluation, we propose a novel summarization strategy, MSqRobSum, which estimates MSqRob's model parameters in a two-stage procedure circumventing the drawbacks of peptide-based workflows. MSqRobSum maintains MSqRob's superior performance, while providing useful protein expression summaries for plotting and downstream analysis. Summarizing peptide to protein intensities considerably reduces the computational complexity, the memory footprint and the model complexity, and makes it easier to disseminate DE inferred on protein summaries. Moreover, MSqRobSum provides a highly modular analysis framework, which provides researchers with full flexibility to develop data analysis workflows tailored toward their specific applications.
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