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<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>Principal Component Analysis</strong>
+ </p>
+ <p>
+ A principal component analysis can be performed via the <a
+ href="calculations.html">calculations dialog</a> which is accessed
+ by selecting <strong>Calculate→Calculate Tree or
+ PCA...</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 can be computationally
+ expensive, and may fail for very large sets of sequences - usually
+ because the JVM has run out of memory. However, the PCA
+ implementation in Jalview 2.10.2 employs more memory efficient
+ matrix storage structures, allowing larger PCAs to be performed.
+ </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
-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>
+ <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>
+ <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 pairwise similarity
+ scores between each pair of sequences. The similarity score model is
+ selected on the <a href="calculations.html">calculations dialog</a>, and
+ may use 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>, or by sequence percentage identity,
+ or sequence feature similarity.
+ </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 grey if the sequence is part of the currently selected
+ group. The viewer also employs depth cueing, so points appear darker
+ the farther away they are, and become brighter as they are rotated
+ towards the front of the view.</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
+ <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>
+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>
+ </p>
+ <p>
+ <strong>Reproducing PCA calculations performed with older
+ Jalview releases</strong> Jalview 2.10.2 included a revised PCA
+ implementation which treated Gaps and non-standard residues in the
+ same way as 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. To reproduce
+ calculations performed with earlier Jalview releases it is necessary
+ to execute the following Groovy script:
+ <pre>
+ jalview.analysis.scoremodels.ScoreMatrix.scoreGapAsAny=true
+ jalview.analysis.scoremodels.ScoreModels.instance.BLOSUM62.@matrix[4][1]=3
+ </pre>
+ This script enables the legacy PCA mode where gaps were treated as
+ 'X', and to modify the BLOSUM62 matrix so it is asymmetric for
+ mutations between C to R (this was a typo in the original Jalview
+ BLOSUM62 matrix which was fixed in 2.10.2).
+ </p>
</body>
</html>
git://source.jalview.org / jalview.git / blobdiff