The same was repeated with the Lowess normalized data using a significance value of 0.01.
Hierarchical and k-means clustering were undertaken with normalized data, using a gene list where those with a low standard deviation were removed.
Differentially expressed genes were identified by analyzing these normalized data using a general linear model in SAS SAS Institutee, Cary, NC) on a gene by gene basis.
EC50 determinations and curve fitting was performed with Prism software (Graphpad, La Jolla, CA, USA) on normalized data using a constant Hill-factor.
Genes that were differentially expressed among genotypes were identified by performing a one-way ANOVA on the normalized data using a parametric test with no assumption of equal variance.
We applied the tool "Smooth waves from normalized aCGH data" using a calibration dataset of mental retardation samples [ 47] which had been previously normalized using the same settings as described for the aCGH data above.
As a first step we analyzed the normalized expression data using a generalized linear model (GLM) approach (see Methods) to classify genes as differentially expressed between 0 hours and any other time-point.
We log-transformed and normalized expression data using a mixed-model ANOVA in SAS (proc MIXED, Cary, NC): where Y is expression, dye and block are a fixed effects, and array, array*dye and array*block are random effects.
For the test data, the LM was generated using a normalized average of the weights obtained from the development sessions.
For comparative purposes, we estimated STRFs from the same data using normalized reverse correlation (NRC), a variant of the classical linear regression that has been used to estimate STRFs from natural stimuli in the auditory and visual systems [3], [34], [35].
