fsl_mvlm.cc

/*  fsl_glm - 

    Christian F. Beckmann, FMRIB Image Analysis Group

    Copyright (C) 2006-2008 University of Oxford  */

/*  Part of FSL - FMRIB's Software Library
    http://www.fmrib.ox.ac.uk/fsl
    fsl@fmrib.ox.ac.uk
    
    Developed at FMRIB (Oxford Centre for Functional Magnetic Resonance
    Imaging of the Brain), Department of Clinical Neurology, Oxford
    University, Oxford, UK
    
    
    LICENCE
    
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    Oxford (the "Software")
    
    The Software remains the property of the University of Oxford ("the
    University").
    
    The Software is distributed "AS IS" under this Licence solely for
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//Header & includes
#include "libvis/miscplot.h"
#include "miscmaths/miscmaths.h"
#include "miscmaths/miscprob.h"
#include "utils/options.h"
#include <vector>
#include "newimage/newimageall.h"
#include "melhlprfns.h"

#define message(msg) { \
  if(verbose.value()) \
    { \
      cout << msg; \
    } \
}

#define dbgmsg(msg) { \
  if(debug.value()) {\
	cerr << msg << endl; } \
}

#define outMsize(msg,Mat) { \
  if(debug.value())						\
    cerr << "     " << msg << "  " <<Mat.Nrows() << " x " << Mat.Ncols() << endl;	\
}

#define outM(msg,Mat) { \
  if(verbose.value())						\
    cout << "     " << msg << "  " <<Mat.Nrows() << " x " << Mat.Ncols() << endl;	\
}


using namespace MISCPLOT;
using namespace MISCMATHS;
using namespace Utilities;
using namespace std;


//Command-line output
// The two strings below specify the title and example usage that is
// printed out as the help or usage message

  string title=string("fsl_mvlm (Version 1.0)")+
		string("\nCopyright(c) 2008, University of Oxford (Christian F. Beckmann)\n")+
		string(" \n Multivariate Linear Model regression on\n")+
		string(" time courses and/or 3D/4D imges using  SVD (PCA), PLS, normalised PLS, \n")+
		string(" CVA, SVD-CVA or  MLM\n\n");
  string examples="fsl_mvlm -i <input> -o <output> [options]";


//Command line Options 
    Option<string> fnin(string("-i,--in"), string(""),
		string("        input file name (text matrix or 3D/4D image file)"),
		true, requires_argument);
    Option<string> fnout(string("-o,--out"), string(""),
		string("basename for output files "),
		true, requires_argument);
	Option<string> approach(string("-a,--alg"), string("PCA"),
		string("algorithm for decomposition: PCA (or SVD; default), PLS, orthoPLS, CVA, SVD-CVA, MLM, NMF"),
		false, requires_argument);	
    Option<string> fndesign(string("-d,--design"), string(""),
		string("file name of the GLM design matrix (time courses or spatial maps)"),
		false, requires_argument);
    Option<string> fnmask(string("-m,--mask"), string(""),
		string("mask image file name if input is image"),
		false, requires_argument);
	Option<bool> normdes(string("--des_norm"),FALSE,
		string("switch on normalisation of the design matrix columns to unit std. deviation"),
		false, no_argument);
	Option<bool> perfvn(string("--vn"),FALSE,
		string("        perform MELODIC variance-normalisation on data"),
		false, no_argument);
	Option<bool> perf_demean(string("--demean"),FALSE,
		string("switch on de-meaning of design and data"),
		false, no_argument);
	Option<int> nmfdim(string("--nmf_dim"), 0,
		string(" Number of underlying factors for NMF"),
		false,requires_argument);
	Option<int> nmfitt(string("--nmfitt"), 100,
		string("number of NMF itterations (default 100)"),
		false,requires_argument);
	Option<int> help(string("-h,--help"), 0,
		string("display this help text"),
		false,no_argument);
	Option<bool> verbose(string("-v,--verbose"),FALSE,
		string("switch on verbose output"),
		false, no_argument);
	Option<bool> debug(string("--debug"),FALSE,
		string("switch on debug output"),
		false, no_argument, false);
	// Output options	
	Option<string> outres(string("--out_res"),string(""),
		string("output file name for residuals"),
		false, requires_argument, false);
	Option<string> outdata(string("--out_data"),string(""),
		string("output file name for pre-processed data"),
		false, requires_argument);
	Option<string> outvnscales(string("--out_vnscales"),string(""),
		string("output file name for scaling factors for variance normalisation"),
		false, requires_argument);

//Globals 
	Melodic::basicGLM glm;
	int voxels = 0;
	Matrix data, tmpdata;
	Matrix design;
	Matrix meanR;
	Matrix svd_X_U, svd_X_V, svd_Y_U, svd_Y_V;
	DiagonalMatrix svd_X_D, svd_Y_D;	
	RowVector vnscales;
	volume<float> mask;  

////////////////////////////////////////////////////////////////////////////

// Local functions
void save4D(Matrix what, string fname){
		if(what.Ncols()==data.Ncols()||what.Nrows()==data.Nrows()){
			volume4D<float> tempVol;
			if(what.Nrows()>what.Ncols())
				tempVol.setmatrix(what.t(),mask);
			else
				tempVol.setmatrix(what,mask);
			save_volume4D(tempVol,fname);
		}
}

bool isimage(Matrix what){
	if((voxels > 0)&&(what.Ncols()==voxels || what.Nrows()==voxels))
		return TRUE;
	else
		return FALSE;
}

void saveit(Matrix what, string fname){
	if(isimage(what))
		save4D(what,fname);
	else if(fsl_imageexists(fndesign.value()))
		write_ascii_matrix(what.t(),fname);
	else
		write_ascii_matrix(what,fname);
}

int setup(){
	
	dbgmsg(" In <setup>");
	message(" Reading data " << fnin.value() << " ... ");
	if(fsl_imageexists(fnin.value())){//read data
		//input is 3D/4D vol
		volume4D<float> tmpdata;
		read_volume4D(tmpdata,fnin.value());
		
		// create mask
		if(fnmask.value()>""){
			read_volume(mask,fnmask.value());
			if(!samesize(tmpdata[0],mask)){
				cerr << "ERROR: Mask image does not match input image" << endl;
				return 1;
			};
		}else{
			mask = tmpdata[0]*0.0+1.0;	
		}
		
		data = tmpdata.matrix(mask);
		voxels = data.Ncols();
		if(perf_demean.value())
			data = remmean(data,1);
		if(perfvn.value())
			vnscales = Melodic::varnorm(data);
	}
	else
		data = read_ascii_matrix(fnin.value());	

	message("done" << endl;);
	if(fndesign.value().length()>0){
		message(" Reading design " << fndesign.value() << " ... ");
		if(fsl_imageexists(fndesign.value())){//read design
			volume4D<float> tmpdata;
			read_volume4D(tmpdata,fndesign.value());
			if(!samesize(tmpdata[0],mask)){
				cerr << "ERROR: GLM design does not match input image in size" << endl;
				return 1;
			}
			design = tmpdata.matrix(mask).t();
			data = data.t();
		}else{
			design = read_ascii_matrix(fndesign.value());
		}
		message("done" << endl;);
	}else
		design = ones(data.Nrows(),1);

	meanR=mean(data,1);
	if(perf_demean.value()){
		data = remmean(data,1);
		design = remmean(design,1);
	}
	
	if(normdes.value())
		design =  SP(design,ones(design.Nrows(),1)*pow(stdev(design,1),-1));
	
	SVD( design, svd_X_D, svd_X_U, svd_X_V );
	if(approach.value()!=string("NMF")){
		if(data.Nrows()>=data.Ncols())
			SVD ( data, svd_Y_D, svd_Y_U, svd_Y_V );
		else{
			SVD ( data.t(), svd_Y_D, svd_Y_V, svd_Y_U );
		}
	}		

	if(fnout.value().length() == 0){
		string basename = fnin.value();
		basename = make_basename(basename);
		fnout.set_T(basename+string("_mvlm_"));	
	}
	
	outM("Data matrix : ", data);
	outM("Design matrix : ", design);

	dbgmsg(" initial SVD : ");
	outMsize("svd_Y_U",svd_Y_U);
	outMsize("svd_Y_V",svd_Y_V);
	outMsize("svd_Y_D",svd_Y_D);

	outMsize("svd_X_U",svd_X_U);
	outMsize("svd_X_V",svd_X_V);
	outMsize("svd_X_D",svd_X_D);

	dbgmsg(" Leaving <setup>");
	return 0;	
}

void write_res(){
	dbgmsg(" In <write_res>");
	
	message(" Writing results ... ")
	if(isimage(svd_Y_V)){
		saveit(svd_Y_V,fnout.value()+string("maps"));
		saveit(svd_Y_U,fnout.value()+string("tcs"));
	}
	else{
		saveit(svd_Y_V.t(),fnout.value()+string("tcs"));
		saveit(svd_Y_U,fnout.value()+string("maps"));
	}
	saveit(svd_Y_D.AsColumn(),fnout.value()+string("scales"));
	if(outres.value()>"")
		
	if(outdata.value()>"")
		saveit(data,outdata.value());
	if(outvnscales.value()>"")
		saveit(vnscales,outvnscales.value());
	
	message("done" << endl;);
	dbgmsg(" Leaving <write_res>");
		
}

int do_work(int argc, char* argv[]) {
	dbgmsg(" In <do_work>");
	
    if(setup())
      exit(1);

	//modify data
	
	//X = svd_X_U * svd_X_D * svd_X_V.t();
	//Y = svd_Y_U * svd_Y_D * svd_Y_V.t();
	//X'X = svd_X_V *pow(svd_X_D,2) * svd_X_V.t();
	//(X'X)^(-1) = svd_X_V *pow(svd_X_D,-2) * svd_X_V.t()
 	//(X'X)^(-1/2) = svd_X_V *pow(svd_X_D,-1) * svd_X_V.t()
			
	if(approach.value()==string("PLS")) {
		message(" Using method : " << approach.value() << endl;);
		data = design.t() * data;
	} 
	if(approach.value()==string("orthoPLS")) {
		message(" Using method : " << approach.value() << endl;);
		data = (svd_X_V * svd_X_D.i() * svd_X_V.t()) * design.t() * data;
	} 
	if(approach.value()==string("CVA")) {
		message(" Using method : " << approach.value() << endl;);
		data = design.t() * svd_Y_U * svd_Y_V.t();
		data = (svd_X_V * svd_X_D.i() * svd_X_V.t() ) * data;
	}
	if(approach.value()==string("SVD-CVA")) {
		message(" Using method : " << approach.value() << endl;);
		tmpdata = data;
		data = design.t() * svd_Y_U;
		data = (svd_X_V * svd_X_D.i() * svd_X_V.t() ) * data;
	}
	if(approach.value()==string("MLM")) {
		message(" Using method : " << approach.value() << endl;);

		Matrix RE;
	    DiagonalMatrix RD;
	    SymmetricMatrix tmp;
	    tmp << cov(data.t());
	    EigenValues(tmp,RD,RE);
//		S = RE * RD * RE.t()
		
		tmp << sqrtm(svd_X_V * svd_X_D * svd_X_U.t() * RE * RD * RE.t() *svd_X_U * svd_X_D * svd_X_V.t());
		data =  tmp.i()*design.t() * data;
	}

    if( approach.value()!=string("MLM") && approach.value()!=string("CVA") && approach.value()!=string("PLS") &&
		approach.value()!=string("SVD-CVA") && approach.value()!=string("orthoPLS") && approach.value()!=string("NMF"))
		message(" Using method : PCA" << endl;);
	
	//perform an SVD on data	
	outMsize(" New Data ", data);
	
	if(approach.value()!=string("NMF")){	
		if(data.Nrows()>=data.Ncols())
			SVD ( data, svd_Y_D, svd_Y_U, svd_Y_V );
		else{
			SVD ( data.t(), svd_Y_D, svd_Y_V, svd_Y_U );
			svd_Y_U = svd_Y_U.t();
			svd_Y_V = svd_Y_V.t();		
		}	

		dbgmsg(" Finished SVD : ");
		outMsize("svd_Y_U",svd_Y_U);
		outMsize("svd_Y_V",svd_Y_V);
		outMsize("svd_Y_D",svd_Y_D);
	
		svd_Y_V = sqrtm(svd_Y_D) * svd_Y_V;	
		svd_Y_U = svd_Y_U * sqrtm(svd_Y_D);	

		if(approach.value()==string("SVD-CVA"))
			svd_Y_V = svd_Y_V *tmpdata;
	}
	else{ //NMF
		float err, err_old;
		Matrix Ratio, Diff;

		if(nmfdim.value()==0)
			nmfdim.set_T(data.Nrows());
		
		message("Using "<< nmfdim.value() << " dimensions" << endl;); 	
		svd_Y_U =  unifrnd(data.Nrows(), nmfdim.value());
		svd_Y_V =  unifrnd(nmfdim.value(), data.Ncols());
		// re-scale columns of svd_Y_U to unit amplitude
		Ratio = pow(stdev(svd_Y_U),-1);
		svd_Y_U = SP(svd_Y_U,ones(svd_Y_U.Nrows(),1)*Ratio);
				
		Diff = data - svd_Y_U * svd_Y_V;
		err = Diff.SumAbsoluteValue()/(data.Ncols()*data.Nrows());
			
		for(int k=1; k< nmfitt.value(); k++)
		{
		//	Ratio = SP(data,pow(svd_Y_U * svd_Y_V,-1));
		//	svd_Y_U  =  SP(svd_Y_U, Ratio * svd_Y_V.t());
		//	svd_Y_U  =  SP(svd_Y_U, pow( ones(svd_Y_U.Nrows(),1) * sum(svd_Y_U,1),-1));
		//	svd_Y_V  =  SP(svd_Y_V, svd_Y_U.t()* Ratio);
		//	
		// Lee & Seung multiplicatice updates
		
			Ratio = SP(svd_Y_U.t() * data, pow(svd_Y_U.t() * svd_Y_U * svd_Y_V ,-1));
			svd_Y_V  = SP(svd_Y_V,Ratio);

			Ratio = SP(data * svd_Y_V.t(),pow(svd_Y_U * (svd_Y_V * svd_Y_V.t()),-1));
			svd_Y_U  = SP(svd_Y_U,Ratio);

			// re-scale columns of svd_Y_U to unit amplitude
			Ratio = pow(stdev(svd_Y_U),-1);
			svd_Y_U = SP(svd_Y_U,ones(svd_Y_U.Nrows(),1)*Ratio);
			
			Diff = data - svd_Y_U * svd_Y_V;
			err_old = err;
			err = Diff.SumSquare()/(data.Ncols()*data.Nrows());			
			message(" Error " << err << endl;);
		}	
	}
	
	write_res();
	
	dbgmsg(" Leaving <do_work>");
	
	return 0;
}

////////////////////////////////////////////////////////////////////////////

int main(int argc,char *argv[]){
	  Tracer tr("main");
	  OptionParser options(title, examples);
	  try{
	    // must include all wanted options here (the order determines how
	    //  the help message is printed)
			options.add(fnin);
			options.add(fnout);
			options.add(approach);
			options.add(fndesign);
			options.add(fnmask);
			options.add(normdes);
			options.add(perfvn);
			options.add(perf_demean);
			options.add(nmfdim);
			options.add(nmfitt);
			options.add(help);
			options.add(verbose);
			options.add(debug);
			options.add(outres);
			options.add(outdata);
			options.add(outvnscales);

	    	options.parse_command_line(argc, argv);

	    // line below stops the program if the help was requested or 
	    //  a compulsory option was not set
	    if ( (help.value()) || (!options.check_compulsory_arguments(true)) ){
			options.usage();
			exit(EXIT_FAILURE);
	    }else{
	  		// Call the local functions
	  		return do_work(argc,argv);
		}
      }
      catch(X_OptionError& e){
		  options.usage();
	      cerr << endl << e.what() << endl;
	      exit(EXIT_FAILURE);
	  }
	  catch(std::exception &e){
	    cerr << e.what() << endl;
	  } 
}