-<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 (<a\r
- href="http://industry.ebi.ac.uk/SeqSpace/">http://industry.ebi.ac.uk/SeqSpace</a>) 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>.</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
-\r
-</body>\r
-</html>\r
+<html>
+<!--
+ * Jalview - A Sequence Alignment Editor and Viewer (Version 2.9)
+ * Copyright (C) 2015 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>
+ </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
+ principal 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 principal 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 with one of the available score
+ matrices, such as <a href="scorematrices.html#blosum62">BLOSUM62</a>,
+ <a href="scorematrices.html#pam250">PAM250</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.
+ </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. 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">
+ <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> <em>Support
+ for PAM250 based PCA was added in Jalview 2.8.1.</em>
+</body>
+</html>
git://source.jalview.org / jalview.git / blobdiff