Bioinformatics Advance Access originally published online on June 28, 2007
Bioinformatics 2007 23(15):1986-1994; doi:10.1093/bioinformatics/btm283
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Non-parametric quantification of protein lysate arrays
1Department of Bioinformatics and Computational Biology, University of Texas M.D. Anderson Cancer Center, 2Department of Statistics, University of Illinois at Urbana-Champaign, 3Department of Gynecologic Medical Oncology and 4Department of Systems Biology, University of Texas M.D. Anderson Cancer Center, USA
*To whom correspondence should be addressed.
| Abstract |
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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 non-parametric approach which adapts to any monotone response curve. The non-parametric approach is shown to be promising via both simulation and real data studies; it reduces the bias due to model misspecification and protects against outliers in the data. The non-parametric 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/
Contact: jhu{at}mdanderson.org
Supplementary information: Supplementary data are available at Bioinformatics online.
Associate Editor: John Quackenbush
Received on February 7, 2007; revised on April 10, 2007; accepted on May 19, 2007
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