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 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.
-
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.
+Caveats
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.
About PCA
+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 principal +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 +principal axis, and the other axes correspond to less extreme patterns +of variation in the data set.
+ +
+ Calculating PCAs for aligned sequences
Jalview can
+ perform PCA analysis on both proteins and nucleotide sequence
+ alignments. In both cases, components are generated by an eigenvector
+ decomposition of the matrix formed from the sum of substitution matrix
+ scores at each aligned position between each pair of sequences -
+ computed with one of the available score matrices, such as
+ BLOSUM62, PAM250, or the simple single
+ nucleotide substitution matrix. The options available for
+ calculation are given in the
+ Change Parameters menu.
+ PCA Calculation modes
+ The default Jalview calculation mode
+ (indicated when Jalview PCA Calculation is
+ ticked in the Change Parameters menu) is to
+ perform a PCA on a matrix where 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. For protein
+ substitution models like BLOSUM62, this gives an asymmetric matrix,
+ and a different PCA to a matrix produced with 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. This method
+ preconditions the matrix by multiplying it with its transpose, and can be employed in the PCA viewer by unchecking the Jalview
+ PCA Calculation option in the Change
+ Parameters menu.
+
+ 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.
+
Options for coordinates export are:
+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. 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. The Reset button will reset axis and rotation settings to their defaults.
++
+The output of points and transformed point coordinates was added to the Jalview desktop in v2.7. +The Reset button and Change Parameters menu were added in Jalview 2.8. +Support for PAM250 based PCA was added in Jalview 2.8.1. + +