Predicting protein concentrations with ELISA microarray assays, monotonic splines and Monte Carlo simulation.

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.

authors

Daly DS,Anderson KK,White AM,Gonzalez RM,Varnum SM,Zangar RC

doi

10.2202/1544-6115.1364

subject

Has Abstract

pub_date

2008-01-01 00:00:00

pages

Article21

issue

1

eissn

2194-6302

issn

1544-6115

journal_volume

7

pub_type

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