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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><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 principle
+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
-principle axis, and the other axes correspond to less extreme patterns
+principal 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 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,
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>
+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→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>
+<em>Support for PAM250 based PCA was added in Jalview 2.8.1.</em>
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git://source.jalview.org / jalview.git / blobdiff