Serial dilution curve: a new method for analysis of reverse phase protein array data.
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Reverse phase protein arrays (RPPAs) are a powerful high-throughput tool for measuring protein concentrations in a large number of samples. In RPPA technology, the original samples are often diluted successively multiple times, forming dilution series to extend the dynamic range of the measurements and to increase confidence in quantitation. An RPPA experiment is equivalent to running multiple ELISA assays concurrently except that there is usually no known protein concentration from which one can construct a standard response curve. Here, we describe a new method called 'serial dilution curve for RPPA data analysis'. Compared with the existing methods, the new method has the advantage of using fewer parameters and offering a simple way of visualizing the raw data. We showed how the method can be used to examine data quality and to obtain robust quantification of protein concentrations.A computer program in R for using serial dilution curve for RPPA data analysis is freely available at http://odin.mdacc.tmc.edu/~zhangli/RPPA.
Published Version (Please cite this version)10.1093/bioinformatics/btn663
Publication InfoWei, Qingyi; Zhang, Li; Mao, Li; Liu, Wenbin; Mills, Gordon B; & Coombes, Kevin (2009). Serial dilution curve: a new method for analysis of reverse phase protein array data. Bioinformatics (Oxford, England), 25(5). pp. 650-654. 10.1093/bioinformatics/btn663. Retrieved from https://hdl.handle.net/10161/17970.
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Professor in Population Health Sciences
Qingyi Wei, MD, PhD, Professor in the Department of Medicine, is Associate Director for Cancer Control and Population Sciences, Co-leader of CCPS and Co-leader of Epidemiology and Population Genomics (Focus Area 1). He is a professor of Medicine and an internationally recognized epidemiologist focused on the molecular and genetic epidemiology of head and neck cancers, lung cancer, and melanoma. His research focuses on biomarkers and genetic determinants for the DNA repair deficient phenotype and