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Multivariate analysis of the groundwater data set was performed by PCA, CA and APCS.
PCA of this data set was performed, and the first four components are shown in (b), with the respective Fourier loadings (c).
The pre-normalization of Vijver data set was performed on an array-by-array basis.
Distance, parsimony, and likelihood analysis of each data set was performed in PAUP*4.0b10 with random addition, tree-bisection-reconnection branch swapping, and heuristic search methods.
Cluster analysis using our data set was performed with CTWC using the expression matrix of 84 samples (80 glioblastoma, 4 non-tumoral brain samples) and 3,860 probe-sets passing a non-specific filter based on standard deviation (>0.75 sd) [7].
A preliminary analysis of the whole data set was performed using this multitrait model.
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The GOBS data set was contributed by D.G. and J.B. Preparation of the transcriptome data was performed by J.E.C., H.H.H.G. and M.A.C., and analyses of this data set were performed by R.A.P. and D.R.M.
Analyses of the combined data set were performed under three optimality criteria with different sets of assumptions, such as excluding hypervariable positions, downweighting the most likely transformations, and indirect weighting of rps4 codon positions by including amino acid translations.
Statistical analyses of the data set were performed using the MixTox tool and were based on the conceptual model of Independent Action (IA) and possible deviations to synergism or antagonism, dose-ratio or dose-level response pattern.
Statistical analysis of each data set is performed as follows.
The GB decompositions for our data set were performed by means of two different approaches.
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