ArticleBiology direct2015
Quantitative proteomics signature profiling based on network contextualization.
Article in Biology direct, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Designing a Comparative Proteomics Experiment: Retention-Time Alignment and Imputation Algorithms Affect Statistical Comparisons between Samples.Journal of proteome research · 2025Article
- ProInfer: An interpretable protein inference tool leveraging on biological networks.PLoS computational biology · 2023Article
- Protein complex-based analysis is resistant to the obfuscating consequences of batch effects --- a case study in clinical proteomics.BMC genomics · 2017Article
- Systematic Analyses and Prediction of Human Drug Side Effect Associated Proteins from the Perspective of Protein Evolution.Genome biology and evolution · 2017Article
- GFS: fuzzy preprocessing for effective gene expression analysis.BMC bioinformatics · 2016Article
- Fuzzy-FishNET: a highly reproducible protein complex-based approach for feature selection in comparative proteomics.BMC medical genomics · 2016Article
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Authors and funding
4 authors.
Funding
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Abstract
backgroundWe present a network-based method, namely quantitative proteomic signature profiling (qPSP) that improves the biological content of proteomic data by converting protein expressions into hit-rates in protein complexes.
resultsWe demonstrate, using two clinical proteomics datasets, that qPSP produces robust discrimination between phenotype classes (e.g. normal vs. disease) and uncovers phenotype-relevant protein complexes. Regardless of acquisition paradigm, comparisons of qPSP against conventional methods (e.g. t-test or hypergeometric test) demonstrate that it produces more stable and consistent predictions, even at small sample size. We show that qPSP is theoretically robust to noise, and that this robustness to noise is also observable in practice. Comparative analysis of hit-rates and protein expressions in significant complexes reveals that hit-rates are a useful means of summarizing differential behavior in a complex-specific manner.
conclusionsGiven qPSP's ability to discriminate phenotype classes even at small sample sizes, high robustness to noise, and better summary statistics, it can be deployed towards analysis of highly heterogeneous clinical proteomics data.
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