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.
+ 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 PCAPrincipal 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.
++ 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 either with BLOSUM62 or the simple single nucleotide
- substitution matrix. The options available for calculation are given
- in the Change Parameters menu.
- Jalview allows two types of PCA calculation. The default Jalview
- PCA Calculation mode (indicated when that option is ticked in the Change
- Parameters menu) of the viewer performs 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 one 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. The original method
- preconditions the matrix by multiplying it with its transpose, and
- this mode is enabled 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:
-
+ 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. +left-clicking and dragging the mouse over the display. --> + 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.