MiMB Example

This class contains all code snippets used in the chapter Probabilistic Approaches to Transcription Factor Binding Site Prediction of the book Computational Biology of Transcription Factors of the series Methods in Molecular Biology:
You can also download the complete code at MiMBExample.java.

import java.io.PrintWriter;

import de.jstacs.algorithms.optimization.Optimizer;
import de.jstacs.classifier.AbstractScoreBasedClassifier;
import de.jstacs.classifier.ConfusionMatrix;
import de.jstacs.classifier.MeasureParameters;
import de.jstacs.classifier.AbstractScoreBasedClassifier.DoubleTableResult;
import de.jstacs.classifier.MeasureParameters.Measure;
import de.jstacs.classifier.assessment.RepeatedHoldOutAssessParameterSet;
import de.jstacs.classifier.assessment.RepeatedHoldOutExperiment;
import de.jstacs.classifier.modelBased.ModelBasedClassifier;
import de.jstacs.classifier.scoringFunctionBased.OptimizableFunction.KindOfParameter;
import de.jstacs.classifier.scoringFunctionBased.gendismix.GenDisMixClassifierParameterSet;
import de.jstacs.classifier.scoringFunctionBased.logPrior.CompositeLogPrior;
import de.jstacs.classifier.scoringFunctionBased.msp.MSPClassifier;
import de.jstacs.data.DNASample;
import de.jstacs.data.Sample;
import de.jstacs.data.Sequence;
import de.jstacs.models.Model;
import de.jstacs.models.VariableLengthWrapperModel;
import de.jstacs.models.discrete.inhomogeneous.BayesianNetworkModel;
import de.jstacs.models.discrete.inhomogeneous.StructureLearner.LearningType;
import de.jstacs.models.discrete.inhomogeneous.StructureLearner.ModelType;
import de.jstacs.models.discrete.inhomogeneous.parameters.BayesianNetworkModelParameterSet;
import de.jstacs.results.ListResult;
import de.jstacs.results.ResultSet;
import de.jstacs.scoringFunctions.directedGraphicalModels.BayesianNetworkScoringFunction;
import de.jstacs.scoringFunctions.directedGraphicalModels.BayesianNetworkScoringFunctionParameterSet;
import de.jstacs.scoringFunctions.directedGraphicalModels.structureLearning.measures.InhomogeneousMarkov;
import de.jstacs.utils.REnvironment;


/**
 * This class implements a main that shows some features of Jstacs including models for generative and discriminative learning,
 * creation of classifiers, evaluation of classifiers, hold-out sampling, and binding site prediction.
 * 
 * @author Jan Grau, Jens Keilwagen
 */
public class MiMBExample {

	/**
	 * @param args only the first parameter will be used; it determines the home directory
	 */
	public static void main( String[] args ) throws Exception {
	
		String home = args[0]+System.getProperty( "file.separator" );
		
		
		/* read data */
		
		//read foreground and background data set form FastA-files
		Sample fgData = new DNASample(home+"foreground.fa");
		Sample bgData = new DNASample(home+"background.fa");
		
		/* generative part */
		
		//create set of parameters for foreground model
		BayesianNetworkModelParameterSet pars = new BayesianNetworkModelParameterSet(
				fgData.getAlphabetContainer(),//used alphabets
				fgData.getElementLength(),//element length == sequence length of each sequence in the sample
				4,//ESS == equivalent sample size (has to be non-negative)
				"fg model",//user description of the model
				ModelType.IMM,//type of statistical model, here an inhomogeneous Markov model (IMM)
				(byte)0,// model order, here 0, so we get an IMM(0) == PWM = position weight matrix
				LearningType.ML_OR_MAP//how to learn the parameters, depends on ESS; for ESS=0 it is ML otherwise MAP
		);
		//create foreground model from these parameters
		Model fgModel = new BayesianNetworkModel(pars);
		
		//analogously, create the background model
		BayesianNetworkModelParameterSet pars2 = new BayesianNetworkModelParameterSet(
				fgData.getAlphabetContainer(),
				fgData.getElementLength(),
				1024,
				"bg model",
				ModelType.IMM,
				(byte)0,
				LearningType.ML_OR_MAP
		);		
		Model bgModel = new BayesianNetworkModel(pars2);
		bgModel = new VariableLengthWrapperModel(bgModel);
		
		//create generative classifier from the models defined before
		ModelBasedClassifier cl = new ModelBasedClassifier(fgModel, bgModel);	
		
		cl.train( fgData, bgData );
		
		/* discriminative part */
		
		//create set of parameters for foreground scoring function
		BayesianNetworkScoringFunctionParameterSet parsD = new BayesianNetworkScoringFunctionParameterSet(
				fgData.getAlphabetContainer(),//used alphabets
				fgData.getElementLength(),//element length == sequence length of each sequence in the sample
				4,//ESS == equivalent sample size (has to be non-negative)
				true,//use MAP-parameters as start values of the optimization
				new InhomogeneousMarkov(0) //the statistical model, here an IMM(0) == PWM
		);
		//create foreground scoring function from these parameters
		BayesianNetworkScoringFunction fgFun = new BayesianNetworkScoringFunction(parsD);
		
		//analogously, create the background scoring function
		BayesianNetworkScoringFunctionParameterSet parsDbg = new BayesianNetworkScoringFunctionParameterSet(
				fgData.getAlphabetContainer(),
				fgData.getElementLength(),
				1024,
				true,
				new InhomogeneousMarkov(0));
		BayesianNetworkScoringFunction bgFun = new BayesianNetworkScoringFunction(parsDbg);
		
		//create set of parameter for the discriminative classifier
		GenDisMixClassifierParameterSet clPars = new GenDisMixClassifierParameterSet(
				fgData.getAlphabetContainer(),//used alphabets
				fgData.getElementLength(),//element length == sequence length of each sequence in the samples
				Optimizer.QUASI_NEWTON_BFGS,//determines the algorithm for numerical optimization
				1E-6,//epsilon to stop the numerical optimization
				1E-6,//epsilon to stop the line search within the numerical optimization
				1,//start step width in the numerical optimization 
				false,//a switch that decides whether to use only the free parameters or all parameters
				KindOfParameter.PLUGIN,//a switch to decide which start parameters to choose
				true,//a switch that states the objective function will be normalized
				1//number of threads used during optimization
		);
		//create discriminative classifier from the parameters and the scoring function defined before
		MSPClassifier cll = new MSPClassifier( 
				clPars,//the parameters of the classifier
				new CompositeLogPrior(),//the used prior, to obtain MCL use null
				fgFun,//the scoring function for the foreground class
				bgFun//the scoring function for the background class
		);
		
		//train the discriminative classifier
		cll.train( fgData, bgData );
		
		/* performance measures */
		
		//partition data
		Sample[] fgSplit = bisect( fgData, fgData.getElementLength() );
		Sample[] bgSplit = bisect( bgData, fgData.getElementLength() );

		Sample fgTest = fgSplit[1];
		Sample bgTest = bgSplit[1];
		
		//train the generative classifier
		cl.train( fgSplit[0], bgSplit[0] );
		
		//fill a confusion matrix
		ConfusionMatrix confMatrix = cl.test( fgTest, bgTest );
		
		//read the entries of the table
		double tp = confMatrix.getCountsFor(0, 0);
		double fn = confMatrix.getCountsFor(1, 0);
		double tn = confMatrix.getCountsFor(1, 1);
		double fp = confMatrix.getCountsFor(0, 1);
		
		double p = tp+fn;
		double barp = tp+fp;
		double n = tn+fp;
		double barn = tn+fn;
		
		System.out.println("TP = "+tp+"\t\tFP = "+fp+"\t\tbarp = "+barp+"\n" +
				"FN = "+fn+"\t\tTN = "+tn+"\t\tbarn = "+barn+"\n" +
				"p = "+p+"\t\tn = "+n+"\t\tN' = "+(n+p));
		
		//compute the measures
		double sn = tp/p;
		double ppv = tp/barp;
		double fpr = fp/n;
		double sp = tn/n;
		double cr = (tp+tn)/(n+p);
		
		System.out.println("cr = "+cr+"\nSn = "+sn+"\nppv = "+ppv+"\nSp = "+sp+"\nfpr = "+fpr+"\n");
		
		//define the measures that shall be evaluated
		MeasureParameters mp = new MeasureParameters(
				true,//evaluate all performance measures
				0.999,//use specificity of 0.999 to measure the sensitivity
				0.95,//use sensitivity of 0.95 to measure the false positive rate 
				0.95//use sensitivity of 0.95 to measure the positive predictive value
		);
		
		//evaluates the classifier
		ResultSet rs = cl.evaluateAll(
				mp,//defines the measures that will be evaluated
				true,//allows to throw an exception if a measure can not be computed
				fgTest,//the test data for the foreground class
				bgTest//the test data for the background class
		);
		System.out.println(rs); 

		//plot ROC and PR curve
		DoubleTableResult roc = (DoubleTableResult)rs.getResultAt( rs.findColumn( Measure.ReceiverOperatingCharacteristicCurve.getNameString() ) );
		DoubleTableResult pr = (DoubleTableResult)rs.getResultAt( rs.findColumn( Measure.PrecisionRecallCurve.getNameString() ) );
		
		REnvironment re = null;
		//you need to have a Rserve running
		try {
			re = new REnvironment(
					"localhost",//server name
					"",//user name
					""//password
				);
			
			String snfpr = "points( " + fpr + ", " + sn + ", col=" + 1 	+ ", pch=" +4 + ", cex=2, lwd=3 );\n";
			String ppvsn = "points( " + sn + ", " + ppv + ", col=" + 1 	+ ", pch=" +4 + ", cex=2, lwd=3 );\n";
			re.voidEval( "p<-palette();p[8]<-\"gray66\";palette(p);" );
			
			re.plotToPDF( DoubleTableResult.getPlotCommands( re, null, new int[]{8}, roc ).toString()+"\n"+snfpr,4,4.5, home+"roc.pdf",true);
			re.plotToPDF( DoubleTableResult.getPlotCommands( re, null, new int[]{8}, pr ).toString()+"\n"+ppvsn, 4,4.5, home+"pr.pdf",true);
		} catch( Exception e ) {
			System.out.println( "could not plot the curves" );
		} finally {
			if( re != null ) {
				re.close();
			}
		}
		separator();
		
		/* hold-out sampling */
		
		//define the measures that shall be evaluated
		mp = new MeasureParameters(
				false,//only evaluate numerical performance measures
				0.999,//use specificity of 0.999 to measure the sensitivity
				0.95,//use sensitivity of 0.95 to measure the false positive rate 
				0.95//use sensitivity of 0.95 to measure the positive predictive value
		);
		
		//create the parameters for the hold-out sampling
		RepeatedHoldOutAssessParameterSet parsA = new RepeatedHoldOutAssessParameterSet(
			Sample.PartitionMethod.PARTITION_BY_NUMBER_OF_SYMBOLS,//defines the way of splitting the data
			fgData.getElementLength(), //defines the length of the elements in the test data set
			true,//a switch that decides whether to throw an exception if a performance measure can not be evaluated
			1000,//the number of samplings
			new double[]{0.1,0.1}//the partition of the data of each class the will be used for testing
		);
		
		//creates an hold-out experiment for two classifiers (the generative and the discriminative classifier)
		RepeatedHoldOutExperiment exp = new RepeatedHoldOutExperiment(cl,cll); 
		//does the experiment a stores the results in a ListResult
		ListResult lr = exp.assess(
				mp,//the measures that will be computed
				parsA,//the parameters for the experiment
				fgData,//the foreground data
				bgData//the background data
		);
		
		System.out.println(lr);
		separator();
		
		/* prediction */
		
		//re-train discriminative classifier
		cll.train( fgData, bgData );
		
		//load data for prediction
		Sample promoters = new DNASample(home+"human_promoters.fa");
		
		//find best possible binding site
		int si=0,id=0;
		double llr, max=Double.NEGATIVE_INFINITY;
		
		PrintWriter out = new PrintWriter( home+"/allscores.txt" );
		
		int i = 0;
		//check all sequence
		for( Sequence seq : promoters ){
			//check each possible start position
			for(int l=0;l<seq.getLength()-cll.getLength()+1;l++){
				Sequence sub = seq.getSubSequence( l, cll.getLength() );
				//compute likelihood ratio
				llr = cll.getScore( sub, 0 ) - cll.getScore( sub, 1 );
				out.print( llr + "\t" );
				if(llr > max){
					//set new best likelihood ratio, sequence, and site
					max = llr;
					si = i;
					id = l;
				}
			}
			out.println();
			i++;
		}
		out.close();
		
		//write a file for the best prediction
		Sequence bestSequence = promoters.getElementAt( si );
		out = new PrintWriter(home+"/scores.txt");
		//write the sequence
		out.println(bestSequence.toString("\t", id-30,id+30));
		
		//write the log likelihood ratio that can be used to plot a profile
		for(int l=id-30;l<id+30;l++){
			Sequence site = bestSequence.getSubSequence( l, cll.getLength() );
			out.print((cll.getScore( site, 0 ) - cll.getScore( site, 1 ))+"\t");
		}
		out.println();
		out.close();
	}
	
	//method for obtaining reproduceably the same split of some given data
	private static Sample[] bisect(Sample data, int l) throws Exception {
		int mid = data.getNumberOfElements()/2;
		return new Sample[]{
				getSubSample(data, 0, mid, "train",l),
				getSubSample(data, mid, data.getNumberOfElements(), "test",l)
		};
	}
	
	//creates a sample from a specific part of the data
	private static Sample getSubSample( Sample data, int start, int end, String annotation, int l ) throws Exception {
		//copy the sequences into an array
		Sequence[] seqs = new Sequence[end-start];
		for(int i=0;i<seqs.length;i++){
			seqs[i] = data.getElementAt( i+start );
		}
		return new Sample( new Sample( annotation, seqs ), l );
	}
	
	//prints a separator
	private static void separator() {
		for( int i = 0; i < 50; i++) {
			System.out.print("=");
		}
		System.out.println();
	}
}