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Spatial Normalization of Reverse Phase Protein Array Data

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...

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Autores principales: Kaushik, Poorvi, Molinelli, Evan J., Miller, Martin L., Wang, Weiqing, Korkut, Anil, Liu, Wenbin, Ju, Zhenlin, Lu, Yiling, Mills, Gordon, Sander, Chris
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4264691/
https://www.ncbi.nlm.nih.gov/pubmed/25501559
http://dx.doi.org/10.1371/journal.pone.0097213
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author Kaushik, Poorvi
Molinelli, Evan J.
Miller, Martin L.
Wang, Weiqing
Korkut, Anil
Liu, Wenbin
Ju, Zhenlin
Lu, Yiling
Mills, Gordon
Sander, Chris
author_facet Kaushik, Poorvi
Molinelli, Evan J.
Miller, Martin L.
Wang, Weiqing
Korkut, Anil
Liu, Wenbin
Ju, Zhenlin
Lu, Yiling
Mills, Gordon
Sander, Chris
author_sort Kaushik, Poorvi
collection PubMed
description 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.
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spelling pubmed-42646912014-12-19 Spatial Normalization of Reverse Phase Protein Array Data Kaushik, Poorvi Molinelli, Evan J. Miller, Martin L. Wang, Weiqing Korkut, Anil Liu, Wenbin Ju, Zhenlin Lu, Yiling Mills, Gordon Sander, Chris PLoS One Research Article 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. Public Library of Science 2014-12-12 /pmc/articles/PMC4264691/ /pubmed/25501559 http://dx.doi.org/10.1371/journal.pone.0097213 Text en © 2014 Kaushik et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Kaushik, Poorvi
Molinelli, Evan J.
Miller, Martin L.
Wang, Weiqing
Korkut, Anil
Liu, Wenbin
Ju, Zhenlin
Lu, Yiling
Mills, Gordon
Sander, Chris
Spatial Normalization of Reverse Phase Protein Array Data
title Spatial Normalization of Reverse Phase Protein Array Data
title_full Spatial Normalization of Reverse Phase Protein Array Data
title_fullStr Spatial Normalization of Reverse Phase Protein Array Data
title_full_unstemmed Spatial Normalization of Reverse Phase Protein Array Data
title_short Spatial Normalization of Reverse Phase Protein Array Data
title_sort spatial normalization of reverse phase protein array data
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4264691/
https://www.ncbi.nlm.nih.gov/pubmed/25501559
http://dx.doi.org/10.1371/journal.pone.0097213
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