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Statistical Applications in Genetics and Molecular Biology

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Volume 9, Issue 1 (Jan 2010)

An Internal Calibration Method for Protein-Array Studies

Don Simone Daly
  • Pacific Northwest National Laboratory
/ Kevin K Anderson
  • Pacific Northwest National Laboratory
/ Shannon L Seurynck-Servoss
  • University of Arkansas
/ Rachel M Gonzalez
  • University of Washington
/ Amanda M White
  • Pacific Northwest National Laboratory
/ Richard C Zangar
  • Pacific Northwest National Laboratory
Published Online: 2010-01-27 | DOI: https://doi.org/10.2202/1544-6115.1506

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.

Keywords: protein array; internal calibration

About the article

Published Online: 2010-01-27

Citation Information: Statistical Applications in Genetics and Molecular Biology, ISSN (Online) 1544-6115, DOI: https://doi.org/10.2202/1544-6115.1506. Export Citation

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