Principal Component Analysis
-This calculation creates a spatial representation of the -similarities within a selected group, or all of the sequences in an -alignment. After the calculation finishes, a 3D viewer displays the set -of sequences as points in 'similarity space', and similar sequences tend -to lie near each other in the space.
-Note: The calculation is computationally expensive, and may fail -for very large sets of sequences - usually because the JVM has run out -of memory. A future release of Jalview will be able to avoid this by -executing the calculation via a web service.
-Principal components analysis is a technique for examining the -structure of complex data sets. The components are a set of dimensions -formed from the measured values in the data set, and the principle -component is the one with the greatest magnitude, or length. The sets of -measurements that differ the most should lie at either end of this -principle axis, and the other axes correspond to less extreme patterns -of variation in the data set.
- -In this case, the components are generated by an eigenvector -decomposition of the matrix formed from the sum of BLOSUM scores at each -aligned position between each pair of sequences. The basic method is -described in the paper by G. Casari, C. Sander and A. Valencia. -Structural Biology volume 2, no. 2, February 1995 (pubmed) -and implemented at the SeqSpace server at the EBI.
- -The PCA Viewer
-This is an interactive display of the sequences positioned within -the similarity space, as points in a rotateable 3D scatterplot. The -colour of each sequence point is the same as the sequence group colours, -white if no colour has been defined for the sequence, and green if the -sequence is part of a the currently selected group.
-The 3d view can be rotated by dragging the mouse with the left -mouse button pressed. The view can also be zoomed in and out with the up -and down arrow keys (and the roll bar of the mouse if -present). Labels will be shown for each sequence if the entry in the -View menu is checked, and the plot background colour changed from the -View→Background Colour.. dialog box. The File menu allows the view -to be saved (File→Save submenu) as an EPS or PNG -image or printed, and the original alignment data and matrix resulting -from its PCA analysis to be retrieved.
-A tool tip gives the sequence ID corresponding to a point in the -space, and clicking a point toggles the selection of the corresponding -sequence in the associated alignment window views. Rectangular region -based selection is also possible, by holding the 'S' key whilst -left-clicking and dragging the mouse over the display. By default, -points are only associated with the alignment view from which the PCA -was calculated, but this may be changed via the View→Associate -Nodes sub-menu.
-Initially, the display shows the first three components of the -similarity space, but any eigenvector can be used by changing the -selected dimension for the x, y, or z axis through each ones menu -located below the 3d display.
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