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.
Highlights
In the last decade, the study of cancer biology has been accelerated by many technological advances, enabling analyses of the genome at both high resolution and throughput
We examine one factor that contributes to noise in the Reverse phase protein arrays (RPPA) data – spatial heterogeneity – and describe a method for correcting it, thereby enhancing the quality of the data
RPPA is one of two main techniques used in large-scale proteomics studies today – array based techniques and mass spectrometry
Summary
The study of cancer biology has been accelerated by many technological advances, enabling analyses of the genome at both high resolution and throughput. This has led to the identification of mutations and biomarkers specific to various cancer types and patient sub-groups. One of the reasons for this is that while the causes of cancer are genetic, they result in cellular malfunction at the level of proteins. This increases the complexity of the proteome via the existence of multiple forms of – e.g. phosphorylated, nitrosylated and methylated – molecules that vary in function. There is a need for reliable and affordable methods for protein measurement, at a scale capable of complementing today’s genomics studies, so that together, they may reveal the mechanisms driving cancer
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