Principal Component Analysis
-This is a method of clustering sequences based on the method developed by G.
- Casari, C. Sander and A. Valencia. Structural Biology volume 2, no. 2, February
- 1995 . Extra information can also be found at the SeqSpace server at the EBI.
-
- The version implemented here only looks at the clustering of whole sequences
- and not individual positions in the alignment to help identify functional residues.
- For large alignments plans are afoot to implement a web service to do this 'residue
- space' PCA remotely.
When the Principal component analysis option is selected all the sequences - ( or just the selected ones) are used in the calculation and for large numbers - of sequences this could take quite a time. When the calculation is finished - a new window is displayed showing the projections of the sequences along the - 2nd, 3rd and 4th vectors giving a 3dimensional view of how the sequences cluster. +
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. The next release of + Jalview release will execute this calculation through 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.
-This 3d view can be rotated by holding the left mouse button down in the PCA - window and moving it. The user can also zoom in and out by using the up and - down arrow keys.
-Individual points can be selected using the mouse and selected sequences show - up green in the PCA window and the usual grey background/white text in the alignment - and tree windows.
-Different eigenvectors can be used to do the projection by changing the selected
- dimensions in the 3 menus underneath the 3d window.
+
+
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. 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.
+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. +
+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. +
+