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 matrix is not symmetric - elements in the upper diagonal give the sum of scores for mutating in one direction, and the lower diagonal is the sum of scores for mutating in the other. This is a refinement of the method 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. The coordinates for the whole PCA space, or just the current view may also be exported as CSV files for visualization in another program or further analysis.

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.