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Predicting Protein Concentrations with ELISA Microarray Assays, Monotonic Splines and Monte Carlo Simulation

Author

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  • Daly Don Simone

    (Pacific Northwest National Laboratory)

  • Anderson Kevin K

    (Pacific Northwest National Laboratory)

  • White Amanda M

    (Pacific Northwest National Laboratory)

  • Gonzalez Rachel M

    (Pacific Northwest National Laboratory)

  • Varnum Susan M

    (Pacific Northwest National Laboratory)

  • Zangar Richard C

    (Pacific Northwest National Laboratory)

Abstract

Making sound proteomic inferences using ELISA microarray assay requires both an accurate prediction of protein concentration and a credible estimate of its error. We present a method using monotonic spline statistical models (MS), penalized constrained least squares fitting (PCLS) and Monte Carlo simulation (MC) to predict ELISA microarray protein concentrations and estimate their prediction errors. We contrast the MSMC (monotone spline Monte Carlo) method with a LNLS (logistic nonlinear least squares) method using simulated and real ELISA microarray data sets.MSMC rendered good fits in almost all tests, including those with left and/or right clipped standard curves. MS predictions were nominally more accurate; especially at the extremes of the prediction curve. MC provided credible asymmetric prediction intervals for both MS and LN fits that were superior to LNLS propagation-of-error intervals in achieving the target statistical confidence. MSMC was more reliable when automated prediction across simultaneous assays was applied routinely with minimal user guidance.

Suggested Citation

  • 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.
    • Handle: RePEc:bpj:sagmbi:v:7:y:2008:i:1:n:21
    DOI: 10.2202/1544-6115.1364
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    References listed on IDEAS

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    1. S. N. Wood, 2000. "Modelling and smoothing parameter estimation with multiple quadratic penalties," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(2), pages 413-428.
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    1. 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.

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