-<html>\r
-<head><title>Principal Component Analysis</title></head>\r
-<body>\r
-<p><strong>Principal Component Analysis</strong></p>\r
-<p>This calculation creates a spatial representation of the\r
-similarities within a selected group, or all of the sequences in\r
-an alignment. After the calculation finishes, a 3D viewer displays the\r
-set of sequences as points in 'similarity space', and similar\r
-sequences tend to lie near each other in the space.</p>\r
-<p>Note: The calculation is computationally expensive, and may fail for very large sets of sequences -\r
- usually because the JVM has run out of memory. The next release of\r
- Jalview release will execute this calculation through a web service.</p>\r
-<p>Principal components analysis is a technique for examining the\r
-structure of complex data sets. The components are a set of dimensions\r
-formed from the measured values in the data set, and the principle\r
-component is the one with the greatest magnitude, or length. The\r
-sets of measurements that differ the most should lie at either end of\r
-this principle axis, and the other axes correspond to less extreme\r
-patterns of variation in the data set.\r
-</p>\r
-\r
-<p>In this case, the components are generated by an eigenvector\r
-decomposition of the matrix formed from the sum of BLOSUM scores at\r
-each aligned position between each pair of sequences. The basic method\r
-is described in the paper by G. Casari, C. Sander and\r
-A. Valencia. Structural Biology volume 2, no. 2, February 1995 (<a\r
-href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=pubmed&dopt=Abstract&list_uids=7749921">pubmed</a>)\r
- and implemented at the SeqSpace server at the EBI.\r
-</p>\r
-\r
-<p><strong>The PCA Viewer</strong></p>\r
-<p>This is an interactive display of the sequences positioned within\r
- the similarity space. The colour of each sequence point is the same\r
- as the sequence group colours, white if no colour has been\r
- defined for the sequence, and green if the sequence is part of a\r
- the currently selected group.\r
-</p>\r
- <p>The 3d view can be rotated by dragging the mouse with the\r
- <strong>left mouse button</strong> pressed. The view can also be\r
- zoomed in and out with the up and down <strong>arrow\r
- keys</strong>. Labels will be shown for each sequence\r
- if the entry in the View menu is checked, and the plot background\r
- colour changed from the View→Background Colour.. dialog\r
- box. The File menu allows the view to be saved (File→Save\r
- submenu) as an EPS or PNG image or printed, and the original\r
- alignment data and matrix resulting from its PCA analysis to be\r
- retrieved.\r
-</p>\r
- </p>\r
-<p>A tool tip gives the sequence ID corresponding to a point in the\r
- space, and clicking a point toggles the selection of the\r
- corresponding sequence in the alignment window. Rectangular region\r
- based selection is also possible, by holding the 'S' key whilst\r
- left-clicking and dragging the mouse over the display.\r
-</p>\r
-<p>Initially, the display shows the first three components of the\r
- similarity space, but any eigenvector can be used by changing the selected\r
- dimension for the x, y, or z axis through each ones menu located\r
- below the 3d display.\r
-</p>\r
-<p>\r
-\r
-</body>\r
-</html>\r
+<html>
+<!--
+ * Jalview - A Sequence Alignment Editor and Viewer (Version 2.8.0b1)
+ * Copyright (C) 2014 The Jalview Authors
+ *
+ * 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
+ * PURPOSE. See the GNU General Public License for more details.
+ *
+ * You should have received a copy of the GNU General Public License along with Jalview. If not, see <http://www.gnu.org/licenses/>.
+ * The Jalview Authors are detailed in the 'AUTHORS' file.
+-->
+<head>
+<title>Principal Component Analysis</title>
+</head>
+<body>
+<p><strong>Principal Component Analysis</strong></p>
+<p>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.</p>
+<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><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
+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>
+ <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,
+white if no colour has been defined for the sequence, and green if the
+sequence is part of a the currently selected group.</p>
+<p>The 3d view can be rotated by dragging the mouse with the <strong>left
+mouse button</strong> pressed. The view can also be zoomed in and out with the up
+and down <strong>arrow keys</strong> (and the roll bar of the mouse if
+present). Labels will be shown for each sequence if the entry in the
+View menu is checked, and the plot background colour changed from the
+View→Background Colour.. dialog box. The File menu allows the view
+to be saved (<strong>File→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. 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
+based selection is also possible, by holding the 'S' key whilst
+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→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. 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>
git://source.jalview.org / jalview.git / blobdiff