title: Spatial normalization of reverse phase protein array data.
abstract: Reverse phase protein arrays (RPPA) are an efficient, high-throughput, cost-effective method for the quantification of specific proteins in complex biological samples. The quality of RPPA data may be affected by various sources of error. One of these, spatial variation, is caused by uneven exposure of different parts of an RPPA slide to the reagents used in protein detection. We present a method for the determination and correction of systematic spatial variation in RPPA slides using positive control spots printed on each slide. The method uses a simple bi-linear interpolation technique to obtain a surface representing the spatial variation occurring across the dimensions of a slide. This surface is used to calculate correction factors that can normalize the relative protein concentrations of the samples on each slide. The adoption of the method results in increased agreement between technical and biological replicates of various tumor and cell-line derived samples. Further, in data from a study of the melanoma cell-line SKMEL-133, several slides that had previously been rejected because they had a coefficient of variation (CV) greater than 15%, are rescued by reduction of CV below this threshold in each case. The method is implemented in the R statistical programing language. It is compatible with MicroVigene and SuperCurve, packages commonly used in RPPA data analysis. The method is made available, along with suggestions for implementation, at http://bitbucket.org/rppa_preprocess/rppa_preprocess/src.
authors.affiliation: Computational Biology Center, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America.
authors.forename: Poorvi
authors.initials: P
authors.lastname: Kaushik
authors.affiliation: Computational Biology Center, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America.
authors.forename: Evan J
authors.initials: EJ
authors.lastname: Molinelli
authors.affiliation: Computational Biology Center, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America.
authors.forename: Martin L
authors.initials: ML
authors.lastname: Miller
authors.affiliation: Computational Biology Center, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America.
authors.forename: Weiqing
authors.initials: W
authors.lastname: Wang
authors.affiliation: Computational Biology Center, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America.
authors.forename: Anil
authors.initials: A
authors.lastname: Korkut
authors.affiliation: Division of Quantitative Sciences, The University of Texas M. D. Anderson Cancer Center, Houston, Texas, United States of America.
authors.forename: Wenbin
authors.initials: W
authors.lastname: Liu
authors.affiliation: Division of Quantitative Sciences, The University of Texas M. D. Anderson Cancer Center, Houston, Texas, United States of America.
authors.forename: Zhenlin
authors.initials: Z
authors.lastname: Ju
authors.affiliation: Department of Systems Biology, The University of Texas M. D. Anderson Cancer Center, Houston, Texas, United States of America.
authors.forename: Yiling
authors.initials: Y
authors.lastname: Lu
authors.affiliation: Department of Systems Biology, The University of Texas M. D. Anderson Cancer Center, Houston, Texas, United States of America.
authors.forename: Gordon
authors.initials: G
authors.lastname: Mills
authors.affiliation: Computational Biology Center, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America.
authors.forename: Chris
authors.initials: C
authors.lastname: Sander
mesh_terms: D045744:Cell Line, Tumor; D003627:Data Interpretation, Statistical; D057166:High-Throughput Screening Assays / Q000379:methods*; D006801:Humans; D007202:Indicators and Reagents / Q000378:metabolism*; D040081:Protein Array Analysis / Q000379:methods* / Q000592:standards*; D011786:Quality Control
chemical_list: D007202:Indicators and Reagents