/*
- * Jalview - A Sequence Alignment Editor and Viewer (Version 2.5)
- * Copyright (C) 2010 J Procter, AM Waterhouse, G Barton, M Clamp, S Searle
+ * Jalview - A Sequence Alignment Editor and Viewer (Version 2.8.2b1)
+ * Copyright (C) 2014 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.
- *
+ * 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/>.
+ * 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.
*/
package jalview.analysis;
import java.io.*;
import jalview.datamodel.*;
+import jalview.datamodel.BinarySequence.InvalidSequenceTypeException;
import jalview.math.*;
+import jalview.schemes.ResidueProperties;
+import jalview.schemes.ScoreMatrix;
/**
* Performs Principal Component Analysis on given sequences
StringBuffer details = new StringBuffer();
/**
- * Creates a new PCA object.
+ * Creates a new PCA object. By default, uses blosum62 matrix to generate
+ * sequence similarity matrices
*
* @param s
- * Set of sequences to perform PCA on
+ * Set of amino acid sequences to perform PCA on
*/
public PCA(String[] s)
{
+ this(s, false);
+ }
+
+ /**
+ * Creates a new PCA object. By default, uses blosum62 matrix to generate
+ * sequence similarity matrices
+ *
+ * @param s
+ * Set of sequences to perform PCA on
+ * @param nucleotides
+ * if true, uses standard DNA/RNA matrix for sequence similarity
+ * calculation.
+ */
+ public PCA(String[] s, boolean nucleotides)
+ {
+ this(s, nucleotides, null);
+ }
+
+ public PCA(String[] s, boolean nucleotides, String s_m)
+ {
BinarySequence[] bs = new BinarySequence[s.length];
int ii = 0;
while ((ii < s.length) && (s[ii] != null))
{
- bs[ii] = new BinarySequence(s[ii]);
+ bs[ii] = new BinarySequence(s[ii], nucleotides);
bs[ii].encode();
ii++;
}
BinarySequence[] bs2 = new BinarySequence[s.length];
ii = 0;
-
+ ScoreMatrix smtrx = null;
+ String sm = s_m;
+ if (sm != null)
+ {
+ smtrx = ResidueProperties.getScoreMatrix(sm);
+ }
+ if (smtrx == null)
+ {
+ // either we were given a non-existent score matrix or a scoremodel that
+ // isn't based on a pairwise symbol score matrix
+ smtrx = ResidueProperties.getScoreMatrix(sm = (nucleotides ? "DNA"
+ : "BLOSUM62"));
+ }
+ details.append("PCA calculation using " + sm
+ + " sequence similarity matrix\n========\n\n");
while ((ii < s.length) && (s[ii] != null))
{
- bs2[ii] = new BinarySequence(s[ii]);
- bs2[ii].blosumEncode();
+ bs2[ii] = new BinarySequence(s[ii], nucleotides);
+ if (smtrx != null)
+ {
+ try
+ {
+ bs2[ii].matrixEncode(smtrx);
+ } catch (InvalidSequenceTypeException x)
+ {
+ details.append("Unexpected mismatch of sequence type and score matrix. Calculation will not be valid!\n\n");
+ }
+ }
ii++;
}
*/
public void run()
{
- Matrix mt = m.transpose();
-
- details.append(" --- OrigT * Orig ---- \n");
- // eigenvector = mt.preMultiply(m); // standard seqspace comparison matrix
- eigenvector = mt.preMultiply(m2); // jalview variation on seqsmace method
-
PrintStream ps = new PrintStream(System.out)
{
public void print(String x)
}
};
- eigenvector.print(ps);
+ try
+ {
+ details.append("PCA Calculation Mode is "
+ + (jvCalcMode ? "Jalview variant" : "Original SeqSpace")
+ + "\n");
+ Matrix mt = m.transpose();
- symm = eigenvector.copy();
+ details.append(" --- OrigT * Orig ---- \n");
+ if (!jvCalcMode)
+ {
+ eigenvector = mt.preMultiply(m); // standard seqspace comparison matrix
+ }
+ else
+ {
+ eigenvector = mt.preMultiply(m2); // jalview variation on seqsmace
+ // method
+ }
- eigenvector.tred();
+ eigenvector.print(ps);
- details.append(" ---Tridiag transform matrix ---\n");
- details.append(" --- D vector ---\n");
- eigenvector.printD(ps);
- ps.println();
- details.append("--- E vector ---\n");
- eigenvector.printE(ps);
- ps.println();
+ symm = eigenvector.copy();
+
+ eigenvector.tred();
+
+ details.append(" ---Tridiag transform matrix ---\n");
+ details.append(" --- D vector ---\n");
+ eigenvector.printD(ps);
+ ps.println();
+ details.append("--- E vector ---\n");
+ eigenvector.printE(ps);
+ ps.println();
- // Now produce the diagonalization matrix
- eigenvector.tqli();
+ // Now produce the diagonalization matrix
+ eigenvector.tqli();
+ } catch (Exception q)
+ {
+ q.printStackTrace();
+ details.append("\n*** Unexpected exception when performing PCA ***\n"
+ + q.getLocalizedMessage());
+ details.append("*** Matrices below may not be fully diagonalised. ***\n");
+ }
details.append(" --- New diagonalization matrix ---\n");
+ eigenvector.print(ps);
details.append(" --- Eigenvalues ---\n");
eigenvector.printD(ps);
ps.println();
- // taps.println();
- // taps.println("Transformed sequences = ");
- // Matrix trans = m.preMultiply(eigenvector);
- // trans.print(System.out);
+ /*
+ * for (int seq=0;seq<symm.rows;seq++) { ps.print("\"Seq"+seq+"\""); for
+ * (int ev=0;ev<symm.rows; ev++) {
+ *
+ * ps.print(","+component(seq, ev)); } ps.println(); }
+ */
+ }
+
+ boolean jvCalcMode = true;
+
+ public void setJvCalcMode(boolean calcMode)
+ {
+ this.jvCalcMode = calcMode;
}
}
git://source.jalview.org / jalview.git / blobdiff