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Before analysis the data were log2 transformed and a set value of 10 was added to the expression value to avoid random effects at low expression levels.
Before analysis, the data were examined by calculating frequency distributions, means, SDs, and other measures.
Before analysis, the data obtained were log transformed where necessary to improve normality and homogeneity of variance.
Before analysis the data was decimated by a factor of 25 – this was necessary to ensure an adequate number of samples for the analysis of very low frequencies.
Before analysis, the data are usually modified using some window function to correct the gap between the imaginary infinite data and real finite data if the amount of data is not sufficiently large.
Before analysis, the data were randomly divided into two sets as follows: the training set consisted of 11 patients with stages I/II lung cancer, 63 patients with stages III/IV lung cancer and 20 healthy controls.
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Before analysis, the PUR data were edited to correct for likely outliers with unusually high application rates using previously developed methods (Gunier et al. 2001).
Before statistical analysis, the data were first subjected to PCA [ 76] and hierarchical clustering analysis to compare the gene expression patterns of the arrays in terms of our classification.
Before analysis of the data, the heat changes accompanying ATPγS binding to CaMKII alone were subtracted from the individual data of enthalpy change accompanying ATPγS binding to NR2B or NR2A saturated CaMKII.
Buffer scans were subtracted, and concentrations normalized before analysis of the data using the CDSSTR deconvolution algorithm in the software suite Dichroweb (53).
Before analysis the normality of the data was confirmed, and ANOVAs were carried out using procGLM in SAS (Version 9.01, SAS Institute Inc., Cary, NC).
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