Spectral Clustering Improves Label-Free Quantification of Low-Abundant Proteins.
Johannes GrissFlorian StanekOtto HudeczGerhard DürnbergerYasset Perez-RiverolJuan Antonio VizcaínoKarl MechtlerPublished in: Journal of proteome research (2019)
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.
Keyphrases
- label free
- mass spectrometry
- electronic health record
- big data
- optical coherence tomography
- healthcare
- machine learning
- deep learning
- high resolution
- magnetic resonance imaging
- emergency department
- rna seq
- mental health
- data analysis
- magnetic resonance
- liquid chromatography
- lymph node
- quality improvement
- artificial intelligence
- high performance liquid chromatography
- adverse drug
- capillary electrophoresis