Non-parametric quantification of protein lysate arrays
Bioinformatics, 2007•academic.oup.com
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 …
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 …
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 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@mdanderson.org
Supplementary information: Supplementary data are available at Bioinformatics online.
Oxford University Press