JAL-1013 JAL-1167 JAL-1121 JAL-1125

diff --git a/help/html/calculations/pca.html b/help/html/calculations/pca.html
index 695c45a..09529a5 100755 (executable)
--- a/help/html/calculations/pca.html
+++ b/help/html/calculations/pca.html
@@ -26,11 +26,12 @@ 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.</p>
-<p>Note: The calculation is computationally expensive, and may fail
+<p><em>Caveats</em><br/>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.</p>
-<p>Principal components analysis is a technique for examining the
+
+<p><strong>About PCA</strong>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
@@ -38,18 +39,34 @@ 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.</p>
 
-<p>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 (<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.</p>
-
-<p><strong>The PCA Viewer</strong></p>
+       <p>
+               <em>Calculating PCAs for aligned sequences</em><br />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 <a href="scorematrices.html#blosum62">BLOSUM62</a> or the <a
+                       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
+                       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.
+       </p>
+<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
 colour of each sequence point is the same as the sequence group colours,
@@ -83,10 +100,10 @@ Nodes</strong> sub-menu.</p>
 <p>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 <em>Reset</em> button will reset axis and rotation settings to their defaults.</p>
+located below the 3d display. The <strong><em>Reset</em></strong> button will reset axis and rotation settings to their defaults.</p>
 <p>
 <p>
 <em>The output of points and transformed point coordinates was added to the Jalview desktop in v2.7.</em>
-<em>The Reset button, and Nucleotide or Protein calculation settings were added in Jalview 2.8.</em>
+<em>The Reset button and Change Parameters menu were added in Jalview 2.8.</em>
 </body>
 </html>

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