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The correlations between vectors of differentiation and between directions of main variance were estimated by the angle between the two vectors ( = the arc cosine of the inner product of the two vectors elements).
The direction of main variance in Gardouch is correlated to the total variance across the whole geographic coverage (|R| = 0.848, P<0.001) and to the direction from mainland to Corsica (|R| = 0.723, P<0.001).
The correlation is even higher when considering the direction of main variance in the Corsican population of Fango (correlation with: the total variance |R| = 0.996, the shape change from mainland to Corsica |R| = 0.916, the shape change from Corsica to Piana |R| = 0.838; all P<0.001).
Our main variance measurement is therefore the mean measured variance of the gene across the four species, which is calculated as described in Methods.
For visual inspection, the data points are projected onto a two-dimensional map that accounts for the main variance in the data.
Therefore the vectors of 10 genes of each microarray dataset were projected into two dimensions which contain the main variance of the data.
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As PCA is a data reduction technique, one can visualize the first three main variances (principle components) within the gene expression data by three-dimensional display (Supplemental Data 2).
The main data variance (59%%) is described by principal component 1 (PC1), whereas PC2 stands for 19%% of the data variance.
Based on the potential reduction on the main loop variance, it is possible to quantify the economic impact produced by improving the tuning of given control loop.
The main phenotypic variance indeed appeared as channeling evolution between populations.
We think the middle is where the main effect variance is coming from.
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