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The resulting warp field allowed nonlinear normalization of data from every single stage to MNI standard reference space.
This is in contrast to what is observed for microarray data where a nonlinear bias (disagreement) is typically observed as a function of mean abundance making nonlinear normalization necessary [ 30, 31].
Normalizations were performed using fastlo [ 33], a model-based nonlinear normalization similar to cyclic loess [ 32].
An Intensity-Dependent Normalization (known as Lowess normalization) was applied to correct for artifacts caused by nonlinear rates of dye incorporation as well as inconsistencies of the relative fluorescence intensity between some red and green dyes.
Feature extraction software provided by Agilent (version 10.7.3.1) was used to quantify the intensity of fluorescent images and to apply a linear normalization to correct for artifacts caused by nonlinear rates of dye incorporation and inconsistent relative fluorescence intensities between some green and red dyes.
This is also consistent with the findings of Bullard et al. (2010) [ 14] who find that no nonlinear normalizations are needed.
For statistical analysis, best-fit estimates of the IC50s and their 95% confidence intervals (CIs) were obtained by nonlinear regression fitting of the sigmoidal dose-response curve for each drug across all experiments after normalization using control well fluorescent signals.
The adjusted signal intensities were normalized using a two-step normalization: weighted print-tip group loess normalization of log-ratios (within-array normalization) followed by quantile normalization of average log-intensities of two channels (between-array normalization).
This was accompanied by a normalization of glucose uptake.
These variations were corrected by the normalization of natremia (P<10-5).
The increased oxidative stress was reversible by normalization of the iron load by phlebotomy.
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