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LFQ-Based Peptide and Protein Intensity Differential Expression Analysis.

Mingze BaiJingwen DengChengxin DaiJulianus PfeufferTimo SachsenbergYasset Perez Riverol
Published in: Journal of proteome research (2023)
Testing for significant differences in quantities at the protein level is a common goal of many LFQ-based mass spectrometry proteomics experiments. Starting from a table of protein and/or peptide quantities from a given proteomics quantification software, many tools and R packages exist to perform the final tasks of imputation, summarization, normalization, and statistical testing. To evaluate the effects of packages and settings in their substeps on the final list of significant proteins, we studied several packages on three public data sets with known expected protein fold changes. We found that the results between packages and even across different parameters of the same package can vary significantly. In addition to usability aspects and feature/compatibility lists of different packages, this paper highlights sensitivity and specificity trade-offs that come with specific packages and settings.
Keyphrases
  • mass spectrometry
  • protein protein
  • amino acid
  • binding protein
  • machine learning
  • small molecule
  • label free
  • deep learning
  • mental health
  • electronic health record
  • high intensity
  • health information
  • gas chromatography