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/* fsl_glm -
Christian F. Beckmann, FMRIB Image Analysis Group
Copyright (C) 2006-2008 University of Oxford */
/* CCOPYRIGHT */
//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"),
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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> 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;
volumeinfo volinf;
////////////////////////////////////////////////////////////////////////////
// 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,volinf);
}
}
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(),volinf);
// 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(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"))
message(" Using method : PCA" << endl;);
//perform an SVD on data
outMsize(" New Data ", data);
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;
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(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;
}
}