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 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.
+ 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.
@@ -58,27 +65,9 @@ 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
+ href="scorematrices.html#simplenucleotide">simple single
+ nucleotide substitution matrix. The options available for
+ calculation are given in the Change
Parameters menu.
@@ -141,5 +130,26 @@ left-clicking and dragging the mouse over the display. -->
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). +