ArticleNature methods2024
A data analysis framework for combining multiple batches increases the power of isobaric proteomics experiments.
Article in Nature methods, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Article
- Article
- Beyond volume and toward coherence: a research parasite's perspective.GigaScience · 2026Article
- The proteogenomic landscape of the human kidney and implications for cardio-kidney-metabolic health.Nature medicine · 2025 · on this mapArticle
- msqrob2TMT: Robust Linear Mixed Models for Inferring Differential Abundant Proteins in Labeled Experiments With Arbitrarily Complex Design.Molecular & cellular proteomics : MCP · 2025Article
- Article
- zMAP toolset: model-based analysis of large-scale proteomic data via a variance stabilizing z-transformation.Genome biology · 2024Article
- Evaluation of Protein Identification and Quantification by the diaPASEF Method on timsTOF SCP.Journal of the American Society for Mass Spectrometry · 2024Article
- Plasma and Kidney Proteome Profiling Combined with Laser Capture Microdissection Reveal Large Increases in Immunoglobulins with Age.Proteomes · 2024Article
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Authors and funding
8 authors.
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Abstract
We present a framework for the analysis of multiplexed mass spectrometry proteomics data that reduces estimation error when combining multiple isobaric batches. Variations in the number and quality of observations have long complicated the analysis of isobaric proteomics data. Here we show that the power to detect statistical associations is substantially improved by utilizing models that directly account for known sources of variation in the number and quality of observations that occur across batches.In a multibatch benchmarking experiment, our open-source software (msTrawler) increases the power to detect changes, especially in the range of less than twofold changes, while simultaneously increasing quantitative proteome coverage by utilizing more low-signal observations. Further analyses of previously published multiplexed datasets of 4 and 23 batches highlight both increased power and the ability to navigate complex missing data patterns without relying on unverifiable imputations or discarding reliable measurements.
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