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Spatial normalization of reverse phase protein array data.

Kaushik P, Molinelli EJ, Miller ML, Wang W, Korkut A, Liu W, Ju Z, Lu Y, Mills G, Sander C - PLoS ONE (2014)

Bottom Line: This surface is used to calculate correction factors that can normalize the relative protein concentrations of the samples on each slide.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.It is compatible with MicroVigene and SuperCurve, packages commonly used in RPPA data analysis.

View Article: PubMed Central - PubMed

Affiliation: Computational Biology Center, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America.

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.

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Coefficient of variation between intensities of intraslide technical replicates in dataset B decreases significantly with normalization.One out of 5 dilutions of positive controls is used for spatial normalization. The correlation of the remaining positive controls, which are technical replicates within each dilution, is observed after normalization. Correlations increase with normalization for each of the observed dilutions.
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pone-0097213-g006: Coefficient of variation between intensities of intraslide technical replicates in dataset B decreases significantly with normalization.One out of 5 dilutions of positive controls is used for spatial normalization. The correlation of the remaining positive controls, which are technical replicates within each dilution, is observed after normalization. Correlations increase with normalization for each of the observed dilutions.

Mentions: The slides evaluated for interslide reproducibility each have 480 positive controls, spotted as 96 sets of 5 dilutions each. The 96 points within a dilution are hence all technical replicates of one another. While the normalization method uses one of these sets, the median set, as anchor points for evaluating spatial variation and correction factors, we can use the remaining dilutions of the positive controls to measure %CV between each set before and after normalization. Doing this showed significant improvements in agreement between each such set of technical replicates, across most antibodies used. (Fig. 6) In the melanoma data-set, agreement between technical replicates showed an average improvement of 4%, with %CV falling from 12% to 8%, after normalization across slides probed with different antibodies. Further, 16 out of the 168 antibodies showed improvements of 10% or above in the coefficient of variation between technical replicates.


Spatial normalization of reverse phase protein array data.

Kaushik P, Molinelli EJ, Miller ML, Wang W, Korkut A, Liu W, Ju Z, Lu Y, Mills G, Sander C - PLoS ONE (2014)

Coefficient of variation between intensities of intraslide technical replicates in dataset B decreases significantly with normalization.One out of 5 dilutions of positive controls is used for spatial normalization. The correlation of the remaining positive controls, which are technical replicates within each dilution, is observed after normalization. Correlations increase with normalization for each of the observed dilutions.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4264691&req=5

pone-0097213-g006: Coefficient of variation between intensities of intraslide technical replicates in dataset B decreases significantly with normalization.One out of 5 dilutions of positive controls is used for spatial normalization. The correlation of the remaining positive controls, which are technical replicates within each dilution, is observed after normalization. Correlations increase with normalization for each of the observed dilutions.
Mentions: The slides evaluated for interslide reproducibility each have 480 positive controls, spotted as 96 sets of 5 dilutions each. The 96 points within a dilution are hence all technical replicates of one another. While the normalization method uses one of these sets, the median set, as anchor points for evaluating spatial variation and correction factors, we can use the remaining dilutions of the positive controls to measure %CV between each set before and after normalization. Doing this showed significant improvements in agreement between each such set of technical replicates, across most antibodies used. (Fig. 6) In the melanoma data-set, agreement between technical replicates showed an average improvement of 4%, with %CV falling from 12% to 8%, after normalization across slides probed with different antibodies. Further, 16 out of the 168 antibodies showed improvements of 10% or above in the coefficient of variation between technical replicates.

Bottom Line: This surface is used to calculate correction factors that can normalize the relative protein concentrations of the samples on each slide.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.It is compatible with MicroVigene and SuperCurve, packages commonly used in RPPA data analysis.

View Article: PubMed Central - PubMed

Affiliation: Computational Biology Center, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America.

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.

Show MeSH
Related in: MedlinePlus