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+ * Jalview - A Sequence Alignment Editor and Viewer (Version 2.8.1)
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<head>
<title>Principal Component Analysis</title>
</head>
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
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 basic method is
-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,
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.</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>Associate
+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.</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>
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git://source.jalview.org / jalview.git / blobdiff