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An Internal Calibration Method for Protein-Array Studies

Author

Listed:
  • Daly Don Simone

    (Pacific Northwest National Laboratory)

  • Anderson Kevin K

    (Pacific Northwest National Laboratory)

  • Seurynck-Servoss Shannon L

    (University of Arkansas)

  • Gonzalez Rachel M

    (University of Washington)

  • White Amanda M

    (Pacific Northwest National Laboratory)

  • Zangar Richard C

    (Pacific Northwest National Laboratory)

Abstract

Nuisance factors in a protein-array study add obfuscating variation to spot intensity measurements, diminishing the accuracy and precision of protein concentration predictions. The effects of nuisance factors may be reduced by design of experiments, and by estimating and then subtracting nuisance effects. Estimated nuisance effects also inform about the quality of the study and suggest refinements for future studies.We demonstrate a method to reduce nuisance effects by incorporating a non-interfering internal calibration in the study design and its complemental analysis of variance. We illustrate this method by applying a chip-level internal calibration in a biomarker discovery study.The variability of sample intensity estimates was reduced 16% to 92% with a median of 58%; confidence interval widths were reduced 8% to 70% with a median of 35%. Calibration diagnostics revealed processing nuisance trends potentially related to spot print order and chip location on a slide.The accuracy and precision of a protein-array study may be increased by incorporating a non-interfering internal calibration. Internal calibration modeling diagnostics improve confidence in study results and suggest process steps that may need refinement. Though developed for our protein-array studies, this internal calibration method is applicable to other targeted array-based studies.

Suggested Citation

  • Daly Don Simone & Anderson Kevin K & Seurynck-Servoss Shannon L & Gonzalez Rachel M & White Amanda M & Zangar Richard C, 2010. "An Internal Calibration Method for Protein-Array Studies," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 9(1), pages 1-23, January.
    • Handle: RePEc:bpj:sagmbi:v:9:y:2010:i:1:n:14
    DOI: 10.2202/1544-6115.1506
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    References listed on IDEAS

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    1. Thilakarathne Pushpike J & Verbeke Geert & Engelen Kristof & Marchal Kathleen, 2009. "A Nonlinear Mixed-Effects Model for Estimating Calibration Intervals for Unknown Concentrations in Two-Color Microarray Data with Spike-Ins," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 8(1), pages 1-25, January.
    2. Daly Don Simone & Anderson Kevin K & White Amanda M & Gonzalez Rachel M & Varnum Susan M & Zangar Richard C, 2008. "Predicting Protein Concentrations with ELISA Microarray Assays, Monotonic Splines and Monte Carlo Simulation," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 7(1), pages 1-21, July.
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