ArticleMolecular omics2020
Detecting differential protein abundance by combining peptide level P-values.
Article in Molecular omics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Label-Free Targeted Proteomics Data Analysis Workflow Selection - Benchmarking AI-based and Data-Driven Approaches.Molecular & cellular proteomics : MCP · 2026Article
- Abnormal B-Cell Exosome Proapoptotic and Antiapoptotic Cargo in Multiple Sclerosis: Potential Implication in Progressive Disease Biology.Neurology(R) neuroimmunology & neuroinflammation · 2026Article
- Widespread discordance between mRNA expression, protein abundance andbioRxiv : the preprint server for biology · 2025Article
- Challenges and Opportunities for Single-cell Computational Proteomics.Molecular & cellular proteomics : MCP · 2023Review
- Urinary Proteomic Profile of Arterial Stiffness Is Associated With Mortality and Cardiovascular Outcomes.Journal of the American Heart Association · 2022Article
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Authors and funding
3 authors.
Funding
Abstract
The majority of methods for detecting differentially abundant proteins between samples in label-free LC-MS bottom-up proteomics experiments rely on statistically testing inferred protein abundances derived from peptide ionization intensities or averaging peptide level statistics. Here, we statistically test peptide ionization intensities directly and combine the resulting dependent P-values using the Empirical Brown's Method (EBM), avoiding error introduced through the estimation of protein abundances or summarizing test statistics. We show that on a spike-in proteomics dataset, a peptide level approach using EBM outperforms differential abundance detection using a protein level approach and several analysis workflows, including MSstats. Additionally, we demonstrate the effectiveness of this approach by detecting enriched proteins from an activity-based protein profiling dataset.
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