X-Git-Url: http://source.jalview.org/gitweb/?a=blobdiff_plain;f=help%2Fhtml%2Fcalculations%2Fpca.html;h=f7afef43dad88b3e9f16ad3bad009fc1bd6fe8b9;hb=e66bdc1c0086ab56c56979d9dc6635e81503eb84;hp=d9b4b45010475cf3b3192fc47c39dfa7a21a7518;hpb=c53aeee9a688f68d64106a968876e542f701fbc6;p=jalview.git

diff --git a/help/html/calculations/pca.html b/help/html/calculations/pca.html
index d9b4b45..f7afef4 100755
--- a/help/html/calculations/pca.html
+++ b/help/html/calculations/pca.html
@@ -1,14 +1,14 @@
 <html>
 <!--
- * Jalview - A Sequence Alignment Editor and Viewer (Version 2.5)
- * Copyright (C) 2010 J Procter, AM Waterhouse, G Barton, M Clamp, S Searle
+ * Jalview - A Sequence Alignment Editor and Viewer (Version 2.8)
+ * Copyright (C) 2012 J Procter, AM Waterhouse, LM Lui, J Engelhardt, G Barton, M Clamp, S Searle
  * 
  * This file is part of Jalview.
  * 
  * Jalview is free software: you can redistribute it and/or
  * modify it under the terms of the GNU General Public License 
  * as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
- * 
+ *  
  * Jalview is distributed in the hope that it will be useful, but 
  * WITHOUT ANY WARRANTY; without even the implied warranty 
  * of MERCHANTABILITY or FITNESS FOR A PARTICULAR 
@@ -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,
@@ -63,19 +80,30 @@ View menu is checked, and the plot background colour changed from the
 View&#8594;Background Colour.. dialog box. The File menu allows the view
 to be saved (<strong>File&#8594;Save</strong> submenu) as an EPS or PNG
 image or printed, and the original alignment data and matrix resulting
-from its PCA analysis to be retrieved.</p>
+from its PCA analysis to be retrieved. The coordinates for the whole PCA
+space, or just the current view may also be exported as CSV files for
+visualization in another program or further analysis.<p>
+<p>Options for coordinates export are:</p>
+<ul>
+<li>Output Values - complete dump of analysis (TxT* matrix computed from sum of scores for all pairs of aligned residues from from i->j and j->i, conditioned matrix to be diagonalised, tridiagonal form, major eigenvalues found)</li>
+<li>Output Points - The eigenvector matrix - rows correspond to sequences, columns correspond to each dimension in the PCA</li>
+<li>Transformed Points - The 3D coordinates for each sequence as shown in the PCA plot</li></ul>
+
 <p>A tool tip gives the sequence ID corresponding to a point in the
 space, and clicking a point toggles the selection of the corresponding
-sequence in the associated alignment window views. Rectangular region
+sequence in the associated alignment window views.<!-- Rectangular region
 based selection is also possible, by holding the 'S' key whilst
-left-clicking and dragging the mouse over the display. By default,
+left-clicking and dragging the mouse over the display. --> By default,
 points are only associated with the alignment view from which the PCA
 was calculated, but this may be changed via the <strong>View&#8594;Associate
 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.</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 Change Parameters menu were added in Jalview 2.8.</em>
 </body>
 </html>
