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This article is part of the supplement: UT-ORNL-KBRIN Bioinformatics Summit 2010

Open Access Poster presentation

Statistical analysis of multiple significance test methods for differential proteomics

Bing Wang1, Fahim Mohammad2, Jun Zhang1, Xinmin Yin1, Eric Rouchka2 and Xiang Zhang1*

  • * Corresponding author: Xiang Zhang

Author Affiliations

1 Department of Chemistry, University of Louisville, Louisville, KY 40209, USA

2 Department of Computer Engineering and Computer Science, University of Louisville, Louisville, KY 40209, USA

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BMC Bioinformatics 2010, 11(Suppl 4):P30  doi:10.1186/1471-2105-11-S4-P30

The electronic version of this article is the complete one and can be found online at: http://www.biomedcentral.com/1471-2105/11/S4/P30


Published:23 July 2010

© 2010 Zhang et al; licensee BioMed Central Ltd.

Background

In current proteomics research, a big challenge is to differentiate the correlative proteins for a given biological function from all existing proteins, and if it does, how strong is the relationship between the proteins and function. Statistical significance testing can be used to address this question. However, every traditional statistical test method may suffer from the inability to identify important differentially expressed proteins if the biological samples do not completely meet the assumptions of each test method [1]. To detect the regulated proteins for differential proteomics, we analyze multiple significance test methods and discover some significance proteins.

Results

We use the four statistical methods, i.e., Kolmogorov-Smirnov test (KS-test), Baumgartner-Weib-Schindler test (BWS-test), T-test, Brunner-Munzel test (BM-test) to measure the difference of the expression level of individual protein under two experimental conditions, respectively. The results had been successfully used for the discovery of protein biomarkers in breast cancer.

Acknowledgements

This work was supported by a grant from National Institute of Health (NIH) under grant number RO1GM087735 and a Competitive Enhancement Grant funded by University of Louisville.

References

  1. Li J, Tang X, Zhao W, Huang J: A new framework for identifying differentially expressed genes.

    Pattern Recognition 2007, 40:3249-3262. Publisher Full Text OpenURL