Note: The calculation is computationally expensive, and may fail +
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 components analysis is a technique for examining the + +
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 @@ -38,18 +40,34 @@ 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 matrix is not -symmetric - 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. This is a refinement of 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 PCA Viewer
+
+ 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, @@ -83,10 +101,10 @@ 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.
+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 Nucleotide or Protein calculation settings were added in Jalview 2.8. +The Reset button and Change Parameters menu were added in Jalview 2.8.