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.
-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. -
- -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 (http://industry.ebi.ac.uk/SeqSpace) 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. -
- - - + + + ++ 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 pairwise similarity
+ scores between each pair of sequences. The similarity score model is
+ selected on the calculations dialog, and
+ may use one of the available score matrices, such as
+ BLOSUM62,
+ PAM250, or the simple single
+ nucleotide substitution matrix, or by sequence percentage identity,
+ or sequence feature similarity.
+
+ + 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 grey if the sequence is part of the currently selected + group. The viewer also employs depth cueing, so points appear darker + the farther away they are, and become brighter as they are rotated + towards the front of the view.
++ 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). + + +