Predicting protein concentrations with ELISA microarray assays, monotonic splines and Monte Carlo simulation.
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.
Daly
Don Simone
DS
Pacific Northwest National Laboratory, USA. ds.daly@pnl.gov
Anderson
Kevin K
KK
White
Amanda M
AM
Gonzalez
Rachel M
RM
Varnum
Susan M
SM
Zangar
Richard C
RC
D000465:Algorithms; D000937:Antigen-Antibody Reactions; D003198:Computer Simulation; D004306:Dose-Response Relationship, Immunologic; D004797:Enzyme-Linked Immunosorbent Assay; D020869:Gene Expression Profiling; D006801:Humans; D016018:Least-Squares Analysis; D015233:Models, Statistical; D009010:Monte Carlo Method; D009994:Osmolar Concentration; D040081:Protein Array Analysis / Q000592:standards; D040901:Proteomics / Q000379:methods*; D012015:Reference Standards