## Last updated: Mon Feb 03 13:35:00 2014
Summary of Data
PCA Scatter plot by defined populations
The populations defined by location do not show any observable clustering. Therefore, the samples will be clustered by k-means based on first 3 pricipal components.
Below, we see PCA scatter plots coloured by K-means clustering on first 3 PCs
Above, both figures show PC1 vs PC2 but coloured by k-means groups 2 and 3. In the second plot, some points seem to overlap, but they are seperated along the z-axis. Now we try to plot PC with elevation using the k-means groups.
PC vs elevation scatter plots coloured by k-means clustering on first 3 PCs
Above, we see no clustering based on elevation. In fact, all groups are at all elevations.
Now we try find.clusters from Adegenet to group individuals.
We get similar results to k-means, but still no correlation to elevation.
Spatial Locations
Here, PCA PC1 is plotted to spatial coordinates.
From above, PCA principal components show no spatial structure.
Now we carry out an sPCA analysis. Seen below are sPCA eigenvalues and screeplot.
Below is shown sPCA Global Score 1 actual scores and lagged scores.
Below is shown sPCA Global Score 2 actual scores and lagged scores.
Ultimately, these samples do not show any population structure using STRUCTURE or PCA or sPCA. The samples are not correlated to elevation by location grouping or by k-means clustering.
End of Document.