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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: Reverse phase protein arrays (RPPA) are an efficient, high-throughput, cost-effective method for the quantification of specific proteins in complex biological samples.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.

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 (%CV) of biological replicates across all antibodies before and after normalization clearly improve with normalization.The degree of improvement varies from antibody to antibody (higher for EGFR-pY992 and cJUN-pS73 than YB1-pS102) and is significant for many antibodies relevant to signaling in the melanoma cell lines studied.
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pone-0097213-g003: Coefficient of variation (%CV) of biological replicates across all antibodies before and after normalization clearly improve with normalization.The degree of improvement varies from antibody to antibody (higher for EGFR-pY992 and cJUN-pS73 than YB1-pS102) and is significant for many antibodies relevant to signaling in the melanoma cell lines studied.

Mentions: Spatial normalization improves agreement between intraslide biological replicates in dataset B and ‘rescues’ previously discarded slides enabling further analysis of these proteins. Melanoma cell line samples were acquired for a large study aimed at understanding the basis of RAF inhibitor resistance in certain melanoma cell lines. Cell lysate was obtained from a melanoma cell line SKMEL-133 and subjected to various drug treatment conditions in triplicate, resulting in approximately 300 samples that were then quantified using RPPA. Agreement between the biological replicates was calculated before and after normalization. Around 10% of the slides (25/238) show increases of over 5% in agreement between biological replicates after normalization whereas only 1.2% (3/238) slides show a worsening of CV by over 5% with normalization. Despite increased agreement overall, biological replicates show different degrees of improvement with spatial normalization (Fig. 3).


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 (%CV) of biological replicates across all antibodies before and after normalization clearly improve with normalization.The degree of improvement varies from antibody to antibody (higher for EGFR-pY992 and cJUN-pS73 than YB1-pS102) and is significant for many antibodies relevant to signaling in the melanoma cell lines studied.
© Copyright Policy
Related In: Results  -  Collection

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

pone-0097213-g003: Coefficient of variation (%CV) of biological replicates across all antibodies before and after normalization clearly improve with normalization.The degree of improvement varies from antibody to antibody (higher for EGFR-pY992 and cJUN-pS73 than YB1-pS102) and is significant for many antibodies relevant to signaling in the melanoma cell lines studied.
Mentions: Spatial normalization improves agreement between intraslide biological replicates in dataset B and ‘rescues’ previously discarded slides enabling further analysis of these proteins. Melanoma cell line samples were acquired for a large study aimed at understanding the basis of RAF inhibitor resistance in certain melanoma cell lines. Cell lysate was obtained from a melanoma cell line SKMEL-133 and subjected to various drug treatment conditions in triplicate, resulting in approximately 300 samples that were then quantified using RPPA. Agreement between the biological replicates was calculated before and after normalization. Around 10% of the slides (25/238) show increases of over 5% in agreement between biological replicates after normalization whereas only 1.2% (3/238) slides show a worsening of CV by over 5% with normalization. Despite increased agreement overall, biological replicates show different degrees of improvement with spatial normalization (Fig. 3).

Bottom Line: Reverse phase protein arrays (RPPA) are an efficient, high-throughput, cost-effective method for the quantification of specific proteins in complex biological samples.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.

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