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

Daly Don Simone, Anderson Kevin K, White Amanda M, Gonzalez Rachel M, Varnum Susan M and Zangar Richard C
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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

Statistical Applications in Genetics and Molecular Biology, 2008, vol. 7, issue 1, 21

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.

Keywords: ELISA microarray; monotonie spline; prediction interval; Monte Carlo simulation (search for similar items in EconPapers)
Date: 2008
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DOI: 10.2202/1544-6115.1364

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