*/
package jalview.analysis;
-import jalview.datamodel.BinarySequence;
-import jalview.datamodel.BinarySequence.InvalidSequenceTypeException;
-import jalview.math.Matrix;
import jalview.math.MatrixI;
-import jalview.math.SparseMatrix;
import jalview.schemes.ResidueProperties;
import jalview.schemes.ScoreMatrix;
{
boolean jvCalcMode = true;
- MatrixI m;
-
MatrixI symm;
- MatrixI m2;
-
double[] eigenvalue;
MatrixI eigenvector;
StringBuilder details = new StringBuilder(1024);
+ private String[] seqs;
+
+ private ScoreMatrix scoreMatrix;
+
/**
* Creates a new PCA object. By default, uses blosum62 matrix to generate
* sequence similarity matrices
public PCA(String[] s, boolean nucleotides, String s_m)
{
+ this.seqs = s;
- BinarySequence[] bs = new BinarySequence[s.length];
- int ii = 0;
-
- while ((ii < s.length) && (s[ii] != null))
- {
- bs[ii] = new BinarySequence(s[ii], nucleotides);
- bs[ii].encode();
- ii++;
- }
-
- BinarySequence[] bs2 = new BinarySequence[s.length];
- ScoreMatrix smtrx = null;
+ scoreMatrix = null;
String sm = s_m;
if (sm != null)
{
- smtrx = ResidueProperties.getScoreMatrix(sm);
+ scoreMatrix = ResidueProperties.getScoreMatrix(sm);
}
- if (smtrx == null)
+ if (scoreMatrix == 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"));
+ scoreMatrix = ResidueProperties
+ .getScoreMatrix(sm = (nucleotides ? "DNA" : "BLOSUM62"));
}
details.append("PCA calculation using " + sm
+ " sequence similarity matrix\n========\n\n");
- ii = 0;
- while ((ii < s.length) && (s[ii] != null))
- {
- 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++;
- }
-
- int count = 0;
- while ((count < bs.length) && (bs[count] != null))
- {
- count++;
- }
-
- double[][] seqmat = new double[count][];
- double[][] seqmat2 = new double[count][];
-
- int i = 0;
- while (i < count)
- {
- seqmat[i] = bs[i].getDBinary();
- seqmat2[i] = bs2[i].getDBinary();
- i++;
- }
-
- /*
- * using a SparseMatrix to hold the encoded sequences matrix
- * greatly speeds up matrix multiplication as these are mostly zero
- */
- m = new SparseMatrix(seqmat);
- m2 = new Matrix(seqmat2);
-
- }
-
- /**
- * Returns the matrix used in PCA calculation
- *
- * @return
- */
-
- public MatrixI getM()
- {
- return m;
}
/**
*/
public float[][] getComponents(int l, int n, int mm, float factor)
{
- float[][] out = new float[m.height()][3];
+ float[][] out = new float[getHeight()][3];
- for (int i = 0; i < m.height(); i++)
+ for (int i = 0; i < getHeight(); i++)
{
out[i][0] = (float) component(i, l) * factor;
out[i][1] = (float) component(i, n) * factor;
public double[] component(int n)
{
// n = index of eigenvector
- double[] out = new double[m.height()];
+ double[] out = new double[getHeight()];
- for (int i = 0; i < m.height(); i++)
+ for (int i = 0; i < out.length; i++)
{
out[i] = component(i, n);
}
details.append("PCA Calculation Mode is "
+ (jvCalcMode ? "Jalview variant" : "Original SeqSpace")
+ "\n");
- MatrixI mt = m.transpose();
- details.append(" --- OrigT * Orig ---- \n");
-
- eigenvector = mt.preMultiply(jvCalcMode ? m2 : m);
+ eigenvector = scoreMatrix.computePairwiseScores(seqs);
+ details.append(" --- OrigT * Orig ---- \n");
eigenvector.print(ps, "%8.2f");
symm = eigenvector.copy();
{
this.jvCalcMode = calcMode;
}
+
+ /**
+ * Answers the N dimensions of the NxN PCA matrix. This is the number of
+ * sequences involved in the pairwise score calculation.
+ *
+ * @return
+ */
+ public int getHeight()
+ {
+ // TODO can any of seqs[] be null?
+ return seqs.length;
+ }
}