Non-parametric quantification of protein lysate arrays
Author(s) -
Jianhua Hu,
Xuming He,
Keith Baggerly,
Kevin R. Coombes,
Bryan T. Hennessy,
Gordon B. Mills
Publication year - 2007
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btm283
Subject(s) - parametric statistics , outlier , computer science , parametric model , nonparametric statistics , lysis , data mining , algorithm , artificial intelligence , biology , statistics , mathematics , microbiology and biotechnology
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.
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