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Bioinformatics Advance Access published online on June 28, 2007
Bioinformatics, doi:10.1093/bioinformatics/btm283
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© The Author (2007). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Nonparametric Quantification of Protein Lysate Arrays

Jianhua Hu 1,*, Xuming He 2, Keith A. Baggerly 1, Kevin R. Coombes 1, Bryan T.J. Hennessy 3 and Gordon B. Mills 4

1Department of Bioinformatics and Computational Biology, 3Department of Gynecologic Medical Oncology, 4Department of Systems Biology, University of Texas M.D. Anderson Cancer Center.
2Department of Statistics, University of Illinois at Urbana-Champaign.

*To whom correspondence should be addressed. Prof. Jianhua Hu, E-mail: jhu{at}mdanderson.org


  Abstract

Motivation: Proteins play a crucial role in biological activity, so much can be learned from measuring protein expression and post-translational modification quantitatively. The Reverse-phase protein lysate arrays allow us to quantify the relative expression levels of a protein in many different cellular samples simultaneously. Existing approaches to quantify protein arrays use parametric response curves fit to dilution series data. The results can be biased when the parametric function does not fit the data.

Results: We propose a nonparametric approach which adapts to any monotone response curve. The nonparametric approach is shown to be promising via both simulation and real data studies; it reduces the bias due to model mis-specification and protects against outliers in the data. The nonparametric approach enables more reliable quantification of protein lysate arrays.

Availability: Code to implement the proposed method in the statistical package R is available at: http://odin.mdacc.tmc.edu/jhu/lysatearray-analysis/.

Associate Editor: Prof. John Quackenbush

Received on February 7, 2007; revised on April 10, 2007; accepted on May 19, 2007

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