/*
* Jalview - A Sequence Alignment Editor and Viewer ($$Version-Rel$$)
* Copyright (C) $$Year-Rel$$ 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.
*
* 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 .
* The Jalview Authors are detailed in the 'AUTHORS' file.
*/
package jalview.analysis;
import jalview.api.analysis.ScoreModelI;
import jalview.api.analysis.SimilarityParamsI;
import jalview.bin.Console;
import jalview.datamodel.AlignmentView;
import jalview.datamodel.Point;
import jalview.datamodel.SequenceI;
import jalview.gui.PairwiseAlignPanel;
import jalview.gui.PaSiMapPanel;
import jalview.math.MatrixI;
import jalview.viewmodel.AlignmentViewport;
import java.io.PrintStream;
import java.util.Hashtable;
import java.util.Enumeration;
/**
* Performs Principal Component Analysis on given sequences
* @AUTHOR MorellThomas
*/
public class PaSiMap implements Runnable
{
/*
* inputs
*/
final private AlignmentViewport seqs;
final private ScoreModelI scoreModel;
final private SimilarityParamsI similarityParams;
final private byte dim = 3;
/*
* outputs
*/
private MatrixI pairwiseScores;
private MatrixI tridiagonal;
private MatrixI eigenMatrix;
/**
* Constructor given the sequences to compute for, the similarity model to
* use, and a set of parameters for sequence comparison
*
* @param sequences
* @param sm
* @param options
*/
//public PaSiMap(AlignmentView sequences, ScoreModelI sm,
//&! viewport or panel?
public PaSiMap(AlignmentViewport sequences, ScoreModelI sm,
SimilarityParamsI options)
{
this.seqs = sequences;
this.scoreModel = sm;
this.similarityParams = options;
}
/**
* Returns Eigenvalue
*
* @param i
* Index of diagonal within matrix
*
* @return Returns value of diagonal from matrix
*/
public double getEigenvalue(int i)
{
return eigenMatrix.getD()[i];
}
/**
* DOCUMENT ME!
*
* @param l
* DOCUMENT ME!
* @param n
* DOCUMENT ME!
* @param mm
* DOCUMENT ME!
* @param factor
* DOCUMENT ME!
*
* @return DOCUMENT ME!
*/
public Point[] getComponents(int l, int n, int mm, float factor)
{
Point[] out = new Point[getHeight()];
for (int i = 0; i < getHeight(); i++)
{
float x = (float) component(i, l) * factor;
float y = (float) component(i, n) * factor;
float z = (float) component(i, mm) * factor;
out[i] = new Point(x, y, z);
}
return out;
}
/**
* DOCUMENT ME!
*
* @param n
* DOCUMENT ME!
*
* @return DOCUMENT ME!
*/
public double[] component(int n)
{
// n = index of eigenvector
double[] out = new double[getHeight()];
for (int i = 0; i < out.length; i++)
{
out[i] = component(i, n);
}
return out;
}
/**
* DOCUMENT ME!
*
* @param row
* DOCUMENT ME!
* @param n
* DOCUMENT ME!
*
* @return DOCUMENT ME!
*/
double component(int row, int n)
{
double out = 0.0;
for (int i = 0; i < pairwiseScores.width(); i++)
{
out += (pairwiseScores.getValue(row, i) * eigenMatrix.getValue(i, n));
}
return out / eigenMatrix.getD()[n];
}
/**
* Answers a formatted text report of the PaSiMap calculation results (matrices
* and eigenvalues) suitable for display
*
* @return
*/
public String getDetails()
{
StringBuilder sb = new StringBuilder(1024);
sb.append("PaSiMap calculation using ").append(scoreModel.getName())
.append(" sequence similarity matrix\n========\n\n");
PrintStream ps = wrapOutputBuffer(sb);
/*
* pairwise similarity scores
*/
sb.append(" --- OrigT * Orig ---- \n");
pairwiseScores.print(ps, "%8.2f");
/*
* tridiagonal matrix, with D and E vectors
*/
sb.append(" ---Tridiag transform matrix ---\n");
sb.append(" --- D vector ---\n");
tridiagonal.printD(ps, "%15.4e");
ps.println();
sb.append("--- E vector ---\n");
tridiagonal.printE(ps, "%15.4e");
ps.println();
/*
* eigenvalues matrix, with D vector
*/
sb.append(" --- New diagonalization matrix ---\n");
eigenMatrix.print(ps, "%8.2f");
sb.append(" --- Eigenvalues ---\n");
eigenMatrix.printD(ps, "%15.4e");
ps.println();
return sb.toString();
}
/**
* Performs the PaSiMap calculation
*/
@Override
public void run()
{
try
{
/** here goes pasimap
* FASTA_to_secureFASTA + sed s/ /_/g ~~ aliases = 1st word of secure fasta header
* FASTA_to_stats ~~ unique_headers_count, unique_bodies_count + check: unique_bodies_min, dim_max
* if unaligned : EMBOSS needlall ~~ alignments.fas
* elseif aligned : MSA_to_pairwiseFASTA
* pairwiseFASTA_to_pairwiseCSV
* pairwise_to_connectivity ~~ connectivity.csv + warn if sequence has #connections >= dim
* pairwiseCSV_to_pairwiseQuantifier (use scoreModel) + label results
* cc_analysis quantifier.ssv ~~ vec.ssv + label results
* plot
*
* ######################
*
* input AlignemtView seqs ~~ check if aligned
* if not ~~ perform needlall (or try whatever is in there)
* calculate connectivity + check if fine
* quantify alignment with ScoreModelI scoreModel
* cc_analysis with quantification
* plot whatever comes out of cc_analysis
*/
/*
bool aligned = seqs.checkoridk;
if (!aligned)
{
seqs = needlall(seqs);
}
connectivity
pairwiseScores = scoreModel.findSimilarities(seqs, similarityParams);
cc_analysis
*/
// run needleman regardless if aligned or not
// gui.PairwiseAlignPanel <++>
PairwiseAlignPanel alignment = new PairwiseAlignPanel(seqs);
float[][] scores = alignment.getScores();
System.out.println(scores[8][18]); // do scores have to be divided by totscore??
// what is connectivity and why?
Hashtable connectivity = seqs.calculateConnectivity(scores, dim);
/** pca code */
/*
* sequence pairwise similarity scores
*/
//&! analysis/scoremodels/ScoreMatrix computeSimilarity
//pairwiseScores = scoreModel.findSimilarities(seqs, similarityParams);
/*
* tridiagonal matrix
*/
//tridiagonal = pairwiseScores.copy();
//tridiagonal.tred();
/*
* the diagonalization matrix
*/
//eigenMatrix = tridiagonal.copy();
//eigenMatrix.tqli();
} catch (Exception q)
{
Console.error("Error computing PaSiMap: " + q.getMessage());
q.printStackTrace();
}
}
/**
* Returns a PrintStream that wraps (appends its output to) the given
* StringBuilder
*
* @param sb
* @return
*/
protected PrintStream wrapOutputBuffer(StringBuilder sb)
{
PrintStream ps = new PrintStream(System.out)
{
@Override
public void print(String x)
{
sb.append(x);
}
@Override
public void println()
{
sb.append("\n");
}
};
return ps;
}
/**
* Answers the N dimensions of the NxN PaSiMap 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 pairwiseScores.height();// seqs.getSequences().length;
}
/**
* Answers the sequence pairwise similarity scores which were the first step
* of the PaSiMap calculation
*
* @return
*/
public MatrixI getPairwiseScores()
{
return pairwiseScores;
}
public void setPairwiseScores(MatrixI m)
{
pairwiseScores = m;
}
public MatrixI getEigenmatrix()
{
return eigenMatrix;
}
public void setEigenmatrix(MatrixI m)
{
eigenMatrix = m;
}
public MatrixI getTridiagonal()
{
return tridiagonal;
}
public void setTridiagonal(MatrixI tridiagonal)
{
this.tridiagonal = tridiagonal;
}
}