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<p>DEqMS is an R package-based statistical tool for differential protein expression analysis in quantitative mass spectrometry-based proteomics. It implements a robust Bayesian method for accurate variance estimation that accounts for the number of mass spectrometry features used for protein quantification (number of peptide precursors or peptide spectrum matches). Originally validated for data-dependent acquisition proteomics, DEqMS now extends to data-independent acquisition workflows, as demonstrated using both spike-in and real-world datasets. Given a peptide- or protein-level quantification table with mass spectrometry feature count as inputs, DEqMS outputs a protein- or gene-level results table containing fold changes and multiple statistics (<i>t</i>-values, <i>P</i> value, among others) adjusted according to mass spectrometry feature count. Here we detail the use of the DEqMS R package. This updated workflow broadens DEqMS’s applicability, enabling researchers with basic R programming knowledge to identify proteins with significantly altered abundance between sample groups across diverse quantitative proteomics datasets. DEqMS is available to install at <a href="https://bioconductor.org/packages/DEqMS/">https://bioconductor.org/packages/DEqMS/</a>.</p>

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Differential protein expression analysis of quantitative mass spectrometry data using DEqMS

  • 期刊论文

  • 发表日期：

    2026年4月22日

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  • Yafeng Zhu,
  • Olena Berkovska,
  • Lingshuo Wang,
  • Mei Yang,
  • Henrik J. Johansson,
  • Georgios Mermelekas,
  • Mahshid Zarrineh,
  • Dong Yin,
  • Lukas M. Orre,
  • Janne Lehtiö

摘要

DEqMS is an R package-based statistical tool for differential protein expression analysis in quantitative mass spectrometry-based proteomics. It implements a robust Bayesian method for accurate variance estimation that accounts for the number of mass spectrometry features used for protein quantification (number of peptide precursors or peptide spectrum matches). Originally validated for data-dependent acquisition proteomics, DEqMS now extends to data-independent acquisition workflows, as demonstrated using both spike-in and real-world datasets. Given a peptide- or protein-level quantification table with mass spectrometry feature count as inputs, DEqMS outputs a protein- or gene-level results table containing fold changes and multiple statistics (t-values, P value, among others) adjusted according to mass spectrometry feature count. Here we detail the use of the DEqMS R package. This updated workflow broadens DEqMS’s applicability, enabling researchers with basic R programming knowledge to identify proteins with significantly altered abundance between sample groups across diverse quantitative proteomics datasets. DEqMS is available to install at https://bioconductor.org/packages/DEqMS/.