Spectral Clustering Improves Label-Free Quantification of Low-Abundant Proteins

J Proteome Res. 2019 Apr 5;18(4):1477-1485. doi: 10.1021/acs.jproteome.8b00377. Epub 2019 Mar 22.


Label-free quantification has become a common-practice in many mass spectrometry-based proteomics experiments. In recent years, we and others have shown that spectral clustering can considerably improve the analysis of (primarily large-scale) proteomics data sets. Here we show that spectral clustering can be used to infer additional peptide-spectrum matches and improve the quality of label-free quantitative proteomics data in data sets also containing only tens of MS runs. We analyzed four well-known public benchmark data sets that represent different experimental settings using spectral counting and peak intensity based label-free quantification. In both approaches, the additionally inferred peptide-spectrum matches through our spectra-cluster algorithm improved the detectability of low abundant proteins while increasing the accuracy of the derived quantitative data, without increasing the data sets' noise. Additionally, we developed a Proteome Discoverer node for our spectra-cluster algorithm which allows anyone to rebuild our proposed pipeline using the free version of Proteome Discoverer.

Keywords: IMP free nodes; Proteome Discoverer; Proteome Discoverer node; benchmarking study; bioinformatics; label-free quantification; mass spectrometry; proteomics; spectral clustering; spectral counting.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Cluster Analysis*
  • Databases, Protein
  • Humans
  • Mass Spectrometry / methods*
  • Proteome / analysis*
  • Proteomics / methods*


  • Proteome