- Principal Component Analysis -
-- A principal component analysis can be performed via the calculations dialog which is accessed - by selecting Calculate→Calculate Tree or - PCA.... -
-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 can be computationally
- expensive, and may fail for very large sets of sequences - usually
- because the JVM has run out of memory. However, the PCA
- implementation in Jalview 2.10.2 employs more memory efficient
- matrix storage structures, allowing larger PCAs to be performed.
-
- 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.
-
- - 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. -
-- Reproducing PCA calculations performed with older - Jalview releases Jalview 2.10.2 included a revised PCA - implementation which treated Gaps and non-standard residues in the - same way as 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. To reproduce - calculations performed with earlier Jalview releases it is necessary - to execute the following Groovy script: -
- jalview.analysis.scoremodels.ScoreMatrix.scoreGapAsAny=true - jalview.analysis.scoremodels.ScoreModels.instance.BLOSUM62.@matrix[4][1]=3 -- This script enables the legacy PCA mode where gaps were treated as - 'X', and to modify the BLOSUM62 matrix so it is asymmetric for - mutations between C to R (this was a typo in the original Jalview - BLOSUM62 matrix which was fixed in 2.10.2). - - -