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+<head><title>Principal Component Analysis</title></head>\r
<body>\r
-<p>pca </p>\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
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