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-<head>
-<title>Principal Component Analysis</title>
-</head>
-<body>
- <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
- 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>
- <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>
- </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