JAL-1503 documentation and release notes

[jalview.git] / help / html / calculations / pca.html

diff --git a/help/html/calculations/pca.html b/help/html/calculations/pca.html
index 49019a7..ba9dc30 100755 (executable)
--- a/help/html/calculations/pca.html
+++ b/help/html/calculations/pca.html
@@ -32,7 +32,8 @@ 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.</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
@@ -52,26 +53,26 @@ of variation in the data set.</p>
                        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