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