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<head>
<title>Principal Component Analysis</title>
</head>
of memory. A future release of Jalview will be able to avoid this by
executing the calculation via a web service.</p>
-<p><strong>About PCA</strong>Principal components analysis is a technique for examining the
+<p><strong>About PCA</strong></p>
+<p>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
href="scorematrices.html#simplenucleotide">simple single
nucleotide substitution matrix</a>. The options available for
calculation are given in the
- <strong><em>Change Parameters</em></strong> menu.<br /> Jalview allows
- two types of PCA calculation. The default
- <em><strong>Jalview PCA Calculation</strong></em> mode (indicated when
- that option is ticked in the
- <strong><em>Change Parameters</em></strong> 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 (<a
+ <strong><em>Change Parameters</em></strong> menu.</p>
+ <p>
+ <em>PCA Calculation modes</em><br/>
+ The default Jalview calculation mode
+ (indicated when <em><strong>Jalview PCA Calculation</strong></em> is
+ ticked in the <strong><em>Change Parameters</em></strong> 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 (<a
href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=pubmed&dopt=Abstract&list_uids=7749921">pubmed</a>)
- 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
- <strong><em>Jalview PCA Calculation</em></strong> option in the
- <strong><em>Change Parameters</em></strong> menu.
+ 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 <strong><em>Jalview
+ PCA Calculation</em></strong> option in the <strong><em>Change
+ Parameters</em></strong> menu.
</p>
-<img src="pcaviewer.gif">
+ <img src="pcaviewer.gif">
<p><strong>The PCA Viewer</strong></p>
<p>This is an interactive display of the sequences positioned within
the similarity space, as points in a rotateable 3D scatterplot. The
git://source.jalview.org / jalview.git / blobdiff