Introduction

High-throughput reverse-phase protein array (RPPA) technology allows for the parallel measurement of protein expression levels in approximately 1000 samples. However, the many steps required in the complex protocol (sample lysate preparation, slide printing, hybridization, washing and amplified detection) may create substantial variability in data quality. We are not aware of any other quality control algorithm that is tuned to the special characteristics of RPPAs.We have developed a novel classifier for quality control of RPPA experiments using a generalized linear model and logistic function. The outcome of the classifier, ranging from 0 to 1, is defined as the probability that a slide is of good quality. After training, we tested the classifier using two independent validation datasets. We conclude that the classifier can distinguish RPPA slides of good quality from those of poor quality sufficiently well such that normalization schemes, protein expression patterns and advanced biological analyses will not be drastically impacted by erroneous measurements or systematic variations.The classifier, implemented in the "SuperCurve" R package, can be freely downloaded at http://bioinformatics.mdanderson.org/main/OOMPA:Overview or http://r-forge.r-project.org/projects/supercurve/. The data used to develop and validate the classifier are available at http://bioinformatics.mdanderson.org/MOAR.

Publications

  1. Development of a robust classifier for quality control of reverse-phase protein arrays.
    Cite this
    Ju Z, Liu W, Roebuck PL, Siwak DR, Zhang N, Lu Y, Davies MA, Akbani R, Weinstein JN, Mills GB, Coombes KR, 2015-03-01 - Bioinformatics (Oxford, England)

Credits

  1. Zhenlin Ju
    Developer

    Department of Bioinformatics and Computational Biology, Department of Systems Biology and Department of Melanoma Medical Oncology, United States of America

  2. Wenbin Liu
    Developer

    Department of Bioinformatics and Computational Biology, Department of Systems Biology and Department of Melanoma Medical Oncology, United States of America

  3. Paul L Roebuck
    Developer

    Department of Bioinformatics and Computational Biology, Department of Systems Biology and Department of Melanoma Medical Oncology, United States of America

  4. Doris R Siwak
    Developer

    Department of Bioinformatics and Computational Biology, Department of Systems Biology and Department of Melanoma Medical Oncology, United States of America

  5. Nianxiang Zhang
    Developer

    Department of Bioinformatics and Computational Biology, Department of Systems Biology and Department of Melanoma Medical Oncology, United States of America

  6. Yiling Lu
    Developer

    Department of Bioinformatics and Computational Biology, Department of Systems Biology and Department of Melanoma Medical Oncology, United States of America

  7. Michael A Davies
    Developer

    Department of Bioinformatics and Computational Biology, Department of Systems Biology and Department of Melanoma Medical Oncology, United States of America

  8. Rehan Akbani
    Developer

    Department of Bioinformatics and Computational Biology, Department of Systems Biology and Department of Melanoma Medical Oncology, United States of America

  9. John N Weinstein
    Developer

    Department of Bioinformatics and Computational Biology, Department of Systems Biology and Department of Melanoma Medical Oncology, United States of America

  10. Gordon B Mills
    Developer

    Department of Bioinformatics and Computational Biology, Department of Systems Biology and Department of Melanoma Medical Oncology, United States of America

  11. Kevin R Coombes
    Investigator

    Department of Bioinformatics and Computational Biology, Department of Systems Biology and Department of Melanoma Medical Oncology, United States of America

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Summary
AccessionBT001219
Tool TypeApplication
Category
PlatformsLinux/Unix
TechnologiesR
User InterfaceTerminal Command Line
Download Count0
Country/RegionUnited States of America
Submitted ByKevin R Coombes