Quantitative proteomics signature profiling based on network contextualization

Biology Direct

Wilson Wen Bin Goh³  Tiannan Guo²  Ruedi Aebersold¹  Limsoon Wong³ 

[1] Faculty of Science, University of Zurich, Zurich, Switzerland

[2] Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland

[3] School of Computing, National University of Singapore, Singapore, Singapore

关键词: Systems Biology;    SWATH;    Bioinformatics;    Quantitative Proteomics Signature Profiling (qPSP);    Networks;    Proteomics;   

Others  :  1235128
DOI  :  10.1186/s13062-015-0098-x

 received in 2015-10-15, accepted in 2015-11-30,  发布年份 2015

【 摘 要 】

Background

We 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.

Results

We 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.

Conclusions

Given 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.

Reviewers

This article was reviewed by Frank Eisenhaber and Sebastian Maurer-Stroh.

Open peer review

Reviewed by Frank Eisenhaber and Sebastian Maurer-Stroh.

【 授权许可】

   
2015 Goh et al.

【 图 表 】

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