Sentence examples for main variances from inspiring English sources

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 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 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 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.

In contrast, directions of greatest variance were comparable among most groups, and the direction of the change due to sexual dimorphism was parallel to the direction of main variance.

The PCA approach decomposes the data into latent variables describing the main variance directions in the data, and each score vector is a weighted sum of the original variables.

Corroborating the dominant effect of relative germ cell composition on the grouping outcome, the main variance of gene expression within the 34 samples is again defined by the most frequent germ cell type (spread along the first principle component PCA1).

We used CA to project the vectors of array samples into a lower-dimensional subspace (typically two dimensions) that accounts for the main variance in the data, in a way that distances among points reflect their original distances in the high-dimensional space as closely as possible (Fellenberg et al 2001).