A Tutorial for Variance-Sensitive Clustering and the Quantitative Analysis of Protein Complexes

Veit Schwämmle*, Christina E. Hagensen

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingBook chapterCommunication


Data clustering facilitates the identification of biologically relevant molecular features in quantitative proteomics experiments with thousands of measurements over multiple conditions. It finds groups of proteins or peptides with similar quantitative behavior across multiple experimental conditions. This co-regulatory behavior suggests that the proteins of such a group share their functional behavior and thus often can be mapped to the same biological processes and molecular subnetworks. While usual clustering approaches dismiss the variance of the measured proteins, VSClust combines statistical testing with pattern recognition into a common algorithm. Here, we show how to use the VSClust web service on a large proteomics data set and present further tools to assess the quantitative behavior of protein complexes.

Original languageEnglish
Title of host publicationQuantitative Methods in Proteomics
EditorsKatrin Marcus, Martin Eisenacher Eisenacher, Barbara Sitek
PublisherHumana Press
Publication date2021
ISBN (Print)978-1-0716-1023-7
ISBN (Electronic)978-1-0716-1024-4
Publication statusPublished - 2021
SeriesMethods in Molecular Biology

Bibliographical note

Publisher Copyright:
© 2021, Springer Science+Business Media, LLC, part of Springer Nature.


  • Bioinformatics
  • Biological pathways
  • Cluster analysis
  • Differential analysis
  • Multivariate analysis
  • Pattern recognition
  • Protein complexes
  • Proteomics


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