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.
+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. -
+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. -
+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. 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 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 alignment window. Rectangular region - based selection is also possible, by holding the 'S' key whilst - left-clicking and dragging the mouse over the display. -
+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. -
+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.-