/* Genome-wide Efficient Mixed Model Association (GEMMA) Copyright (C) 2011 Xiang Zhou This program 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. This program 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 this program. If not, see <http://www.gnu.org/licenses/>. */ #include <iostream> #include <fstream> #include <string> #include <cstring> #include <sys/stat.h> #include <cmath> #include <algorithm> #ifdef FORCE_FLOAT #include "param_float.h" #include "io_float.h" #else #include "param.h" #include "io.h" #endif using namespace std; PARAM::PARAM(void): mode_silence (false), a_mode (0), k_mode(1), d_pace (100000), file_out("result"), path_out("./output/"), miss_level(0.05), maf_level(0.01), hwe_level(0), r2_level(0.9999), l_min(1e-5), l_max(1e5), n_region(10),p_nr(0.001),em_prec(0.0001),nr_prec(0.0001),em_iter(10000),nr_iter(100),crt(0), pheno_mean(0), h_min(-1), h_max(-1), h_scale(-1), rho_min(0.0), rho_max(1.0), rho_scale(-1), logp_min(0.0), logp_max(0.0), logp_scale(-1), s_min(0), s_max(300), w_step(100000), s_step(1000000), r_pace(10), w_pace(1000), n_accept(0), n_mh(10), geo_mean(2000.0), randseed(-1), error(false), n_cvt(1), n_vc(1), time_total(0.0), time_G(0.0), time_eigen(0.0), time_UtX(0.0), time_UtZ(0.0), time_opt(0.0), time_Omega(0.0) {} //read files //obtain ns_total, ng_total, ns_test, ni_test void PARAM::ReadFiles (void) { string file_str; if (!file_mk.empty()) { if (CountFileLines (file_mk, n_vc)==false) {error=true;} } if (!file_snps.empty()) { if (ReadFile_snps (file_snps, setSnps)==false) {error=true;} } else { setSnps.clear(); } //for prediction if (!file_epm.empty()) { if (ReadFile_est (file_epm, est_column, mapRS2est)==false) {error=true;} if (!file_bfile.empty()) { file_str=file_bfile+".bim"; if (ReadFile_bim (file_str, snpInfo)==false) {error=true;} file_str=file_bfile+".fam"; if (ReadFile_fam (file_str, indicator_pheno, pheno, mapID2num, p_column)==false) {error=true;} } if (!file_geno.empty()) { if (ReadFile_pheno (file_pheno, indicator_pheno, pheno, p_column)==false) {error=true;} if (CountFileLines (file_geno, ns_total)==false) {error=true;} } if (!file_ebv.empty() ) { if (ReadFile_column (file_ebv, indicator_bv, vec_bv, 1)==false) {error=true;} } if (!file_log.empty() ) { if (ReadFile_log (file_log, pheno_mean)==false) {error=true;} } //convert indicator_pheno to indicator_idv int k=1; for (size_t i=0; i<indicator_pheno.size(); i++) { k=1; for (size_t j=0; j<indicator_pheno[i].size(); j++) { if (indicator_pheno[i][j]==0) {k=0;} } indicator_idv.push_back(k); } ns_test=0; return; } //read covariates before the genotype files if (!file_cvt.empty() ) { if (ReadFile_cvt (file_cvt, indicator_cvt, cvt, n_cvt)==false) {error=true;} if ((indicator_cvt).size()==0) { n_cvt=1; } } else { n_cvt=1; } //read genotype and phenotype file for plink format if (!file_bfile.empty()) { file_str=file_bfile+".bim"; if (ReadFile_bim (file_str, snpInfo)==false) {error=true;} file_str=file_bfile+".fam"; if (ReadFile_fam (file_str, indicator_pheno, pheno, mapID2num, p_column)==false) {error=true;} //post-process covariates and phenotypes, obtain ni_test, save all useful covariates ProcessCvtPhen(); //obtain covariate matrix gsl_matrix *W=gsl_matrix_alloc (ni_test, n_cvt); CopyCvt (W); file_str=file_bfile+".bed"; if (ReadFile_bed (file_str, setSnps, W, indicator_idv, indicator_snp, snpInfo, maf_level, miss_level, hwe_level, r2_level, ns_test)==false) {error=true;} gsl_matrix_free(W); ns_total=indicator_snp.size(); } //read genotype and phenotype file for bimbam format if (!file_geno.empty()) { //annotation file before genotype file if (!file_anno.empty() ) { if (ReadFile_anno (file_anno, mapRS2chr, mapRS2bp, mapRS2cM)==false) {error=true;} } //phenotype file before genotype file if (ReadFile_pheno (file_pheno, indicator_pheno, pheno, p_column)==false) {error=true;} //post-process covariates and phenotypes, obtain ni_test, save all useful covariates ProcessCvtPhen(); //obtain covariate matrix gsl_matrix *W=gsl_matrix_alloc (ni_test, n_cvt); CopyCvt (W); if (ReadFile_geno (file_geno, setSnps, W, indicator_idv, indicator_snp, maf_level, miss_level, hwe_level, r2_level, mapRS2chr, mapRS2bp, mapRS2cM, snpInfo, ns_test)==false) {error=true;} gsl_matrix_free(W); ns_total=indicator_snp.size(); } if (!file_gene.empty()) { if (ReadFile_pheno (file_pheno, indicator_pheno, pheno, p_column)==false) {error=true;} //convert indicator_pheno to indicator_idv int k=1; for (size_t i=0; i<indicator_pheno.size(); i++) { k=1; for (size_t j=0; j<indicator_pheno[i].size(); j++) { if (indicator_pheno[i][j]==0) {k=0;} } indicator_idv.push_back(k); } if (ReadFile_gene (file_gene, vec_read, snpInfo, ng_total)==false) {error=true;} } //read is after gene file if (!file_read.empty() ) { if (ReadFile_column (file_read, indicator_read, vec_read, 1)==false) {error=true;} ni_test=0; for (vector<int>::size_type i=0; i<(indicator_idv).size(); ++i) { indicator_idv[i]*=indicator_read[i]; ni_test+=indicator_idv[i]; } if (ni_test==0) { error=true; cout<<"error! number of analyzed individuals equals 0. "<<endl; return; } } //for ridge prediction, read phenotype only if (file_geno.empty() && file_gene.empty() && !file_pheno.empty()) { if (ReadFile_pheno (file_pheno, indicator_pheno, pheno, p_column)==false) {error=true;} //post-process covariates and phenotypes, obtain ni_test, save all useful covariates ProcessCvtPhen(); } return; } void PARAM::CheckParam (void) { struct stat fileInfo; string str; //check parameters if (k_mode!=1 && k_mode!=2) {cout<<"error! unknown kinship/relatedness input mode: "<<k_mode<<endl; error=true;} if (a_mode!=1 && a_mode!=2 && a_mode!=3 && a_mode!=4 && a_mode!=5 && a_mode!=11 && a_mode!=12 && a_mode!=13 && a_mode!=21 && a_mode!=22 && a_mode!=31 && a_mode!=41 && a_mode!=42 && a_mode!=43 && a_mode!=51 && a_mode!=52 && a_mode!=53 && a_mode!=54 && a_mode!=61) {cout<<"error! unknown analysis mode: "<<a_mode<<". make sure -gk or -eigen or -lmm or -bslmm or -predict is sepcified correctly."<<endl; error=true;} if (miss_level>1) {cout<<"error! missing level needs to be between 0 and 1. current value = "<<miss_level<<endl; error=true;} if (maf_level>0.5) {cout<<"error! maf level needs to be between 0 and 0.5. current value = "<<maf_level<<endl; error=true;} if (hwe_level>1) {cout<<"error! hwe level needs to be between 0 and 1. current value = "<<hwe_level<<endl; error=true;} if (r2_level>1) {cout<<"error! r2 level needs to be between 0 and 1. current value = "<<r2_level<<endl; error=true;} if (l_max<l_min) {cout<<"error! maximum lambda value must be larger than the minimal value. current values = "<<l_max<<" and "<<l_min<<endl; error=true;} if (h_max<h_min) {cout<<"error! maximum h value must be larger than the minimal value. current values = "<<h_max<<" and "<<h_min<<endl; error=true;} if (s_max<s_min) {cout<<"error! maximum s value must be larger than the minimal value. current values = "<<s_max<<" and "<<s_min<<endl; error=true;} if (rho_max<rho_min) {cout<<"error! maximum rho value must be larger than the minimal value. current values = "<<rho_max<<" and "<<rho_min<<endl; error=true;} if (logp_max<logp_min) {cout<<"error! maximum logp value must be larger than the minimal value. current values = "<<logp_max/log(10)<<" and "<<logp_min/log(10)<<endl; error=true;} if (h_max>1) {cout<<"error! h values must be bewtween 0 and 1. current values = "<<h_max<<" and "<<h_min<<endl; error=true;} if (rho_max>1) {cout<<"error! rho values must be between 0 and 1. current values = "<<rho_max<<" and "<<rho_min<<endl; error=true;} if (logp_max>0) {cout<<"error! maximum logp value must be smaller than 0. current values = "<<logp_max/log(10)<<" and "<<logp_min/log(10)<<endl; error=true;} if (l_max<l_min) {cout<<"error! maximum lambda value must be larger than the minimal value. current values = "<<l_max<<" and "<<l_min<<endl; error=true;} if (h_scale>1.0) {cout<<"error! hscale value must be between 0 and 1. current value = "<<h_scale<<endl; error=true;} if (rho_scale>1.0) {cout<<"error! rscale value must be between 0 and 1. current value = "<<rho_scale<<endl; error=true;} if (logp_scale>1.0) {cout<<"error! pscale value must be between 0 and 1. current value = "<<logp_scale<<endl; error=true;} if (rho_max==1 && rho_min==1 && a_mode==12) {cout<<"error! ridge regression does not support a rho parameter. current values = "<<rho_max<<" and "<<rho_min<<endl; error=true;} //check p_column, and (no need to) sort p_column into ascending order if (p_column.size()==0) { p_column.push_back(1); } else { for (size_t i=0; i<p_column.size(); i++) { for (size_t j=0; j<i; j++) { if (p_column[i]==p_column[j]) {cout<<"error! identical phenotype columns: "<<p_column[i]<<endl; error=true;} } } } //sort (p_column.begin(), p_column.end() ); n_ph=p_column.size(); //only lmm option (and one prediction option) can deal with multiple phenotypes //and no gene expression files if (n_ph>1 && a_mode!=1 && a_mode!=2 && a_mode!=3 && a_mode!=4 && a_mode!=43) { cout<<"error! the current analysis mode "<<a_mode<<" can not deal with multiple phenotypes."<<endl; error=true; } if (n_ph>1 && !file_gene.empty() ) { cout<<"error! multiple phenotype analysis option not allowed with gene expression files. "<<endl; error=true; } if (p_nr>1) { cout<<"error! pnr value must be between 0 and 1. current value = "<<p_nr<<endl; error=true; } //check est_column if (est_column.size()==0) { if (file_ebv.empty()) { est_column.push_back(2); est_column.push_back(5); est_column.push_back(6); est_column.push_back(7); } else { est_column.push_back(2); est_column.push_back(0); est_column.push_back(6); est_column.push_back(7); } } if (est_column.size()!=4) {cout<<"error! -en not followed by four numbers. current number = "<<est_column.size()<<endl; error=true;} if (est_column[0]==0) {cout<<"error! -en rs column can not be zero. current number = "<<est_column.size()<<endl; error=true;} //check if files are compatible with each other, and if files exist if (!file_bfile.empty()) { str=file_bfile+".bim"; if (stat(str.c_str(),&fileInfo)==-1) {cout<<"error! fail to open .bim file: "<<str<<endl; error=true;} str=file_bfile+".bed"; if (stat(str.c_str(),&fileInfo)==-1) {cout<<"error! fail to open .bed file: "<<str<<endl; error=true;} str=file_bfile+".fam"; if (stat(str.c_str(),&fileInfo)==-1) {cout<<"error! fail to open .fam file: "<<str<<endl; error=true;} } if ((!file_geno.empty() || !file_gene.empty()) ) { str=file_pheno; if (stat(str.c_str(),&fileInfo)==-1) {cout<<"error! fail to open phenotype file: "<<str<<endl; error=true;} } str=file_geno; if (!str.empty() && stat(str.c_str(),&fileInfo)==-1 ) {cout<<"error! fail to open mean genotype file: "<<str<<endl; error=true;} str=file_gene; if (!str.empty() && stat(str.c_str(),&fileInfo)==-1 ) {cout<<"error! fail to open gene expression file: "<<str<<endl; error=true;} size_t flag=0; if (!file_bfile.empty()) {flag++;} if (!file_geno.empty()) {flag++;} if (!file_gene.empty()) {flag++;} if (flag!=1 && a_mode!=43 && a_mode!=5 && a_mode!=61) { cout<<"error! either plink binary files, or bimbam mean genotype files, or gene expression files are required."<<endl; error=true; } if (file_pheno.empty() && (a_mode==43 || a_mode==5 || a_mode==61) ) { cout<<"error! phenotype file is required."<<endl; error=true; } if (!file_epm.empty() && file_bfile.empty() && file_geno.empty() ) {cout<<"error! estimated parameter file also requires genotype file."<<endl; error=true;} if (!file_ebv.empty() && file_kin.empty()) {cout<<"error! estimated breeding value file also requires relatedness file."<<endl; error=true;} if (!file_log.empty() && pheno_mean!=0) {cout<<"error! either log file or mu value can be provide."<<endl; error=true;} str=file_snps; if (!str.empty() && stat(str.c_str(),&fileInfo)==-1 ) {cout<<"error! fail to open snps file: "<<str<<endl; error=true;} str=file_log; if (!str.empty() && stat(str.c_str(),&fileInfo)==-1 ) {cout<<"error! fail to open log file: "<<str<<endl; error=true;} str=file_anno; if (!str.empty() && stat(str.c_str(),&fileInfo)==-1 ) {cout<<"error! fail to open annotation file: "<<str<<endl; error=true;} str=file_kin; if (!str.empty() && stat(str.c_str(),&fileInfo)==-1 ) {cout<<"error! fail to open relatedness matrix file: "<<str<<endl; error=true;} str=file_mk; if (!str.empty() && stat(str.c_str(),&fileInfo)==-1 ) {cout<<"error! fail to open relatedness matrix file: "<<str<<endl; error=true;} str=file_cvt; if (!str.empty() && stat(str.c_str(),&fileInfo)==-1 ) {cout<<"error! fail to open covariates file: "<<str<<endl; error=true;} str=file_epm; if (!str.empty() && stat(str.c_str(),&fileInfo)==-1 ) {cout<<"error! fail to open estimated parameter file: "<<str<<endl; error=true;} str=file_ebv; if (!str.empty() && stat(str.c_str(),&fileInfo)==-1 ) {cout<<"error! fail to open estimated breeding value file: "<<str<<endl; error=true;} str=file_read; if (!str.empty() && stat(str.c_str(),&fileInfo)==-1 ) {cout<<"error! fail to open total read file: "<<str<<endl; error=true;} //check if files are compatible with analysis mode if (k_mode==2 && !file_geno.empty() ) {cout<<"error! use \"-km 1\" when using bimbam mean genotype file. "<<endl; error=true;} if ((a_mode==1 || a_mode==2 || a_mode==3 || a_mode==4 || a_mode==5 || a_mode==31) && (file_kin.empty() && (file_ku.empty()||file_kd.empty())) ) {cout<<"error! missing relatedness file. "<<endl; error=true;} if (a_mode==61 && (file_kin.empty() && (file_ku.empty()||file_kd.empty()) && file_mk.empty() ) ) {cout<<"error! missing relatedness file. "<<endl; error=true;} if ((a_mode==43) && file_kin.empty()) {cout<<"error! missing relatedness file. -predict option requires -k option to provide a relatedness file."<<endl; error=true;} if ((a_mode==11 || a_mode==12 || a_mode==13) && !file_cvt.empty() ) {cout<<"error! -bslmm option does not support covariates files."<<endl; error=true;} if (a_mode==41 || a_mode==42) { if (!file_cvt.empty() ) {cout<<"error! -predict option does not support covariates files."<<endl; error=true;} if (file_epm.empty() ) {cout<<"error! -predict option requires estimated parameter files."<<endl; error=true;} } return; } void PARAM::CheckData (void) { if ((file_cvt).empty() || (indicator_cvt).size()==0) { n_cvt=1; } if ( (indicator_cvt).size()!=0 && (indicator_cvt).size()!=(indicator_idv).size()) { error=true; cout<<"error! number of rows in the covariates file do not match the number of individuals. "<<endl; return; } if ( (indicator_read).size()!=0 && (indicator_read).size()!=(indicator_idv).size()) { error=true; cout<<"error! number of rows in the total read file do not match the number of individuals. "<<endl; return; } //calculate ni_total and ni_test, and set indicator_idv to 0 whenever indicator_cvt=0 //and calculate np_obs and np_miss ni_total=(indicator_idv).size(); ni_test=0; for (vector<int>::size_type i=0; i<(indicator_idv).size(); ++i) { if (indicator_idv[i]==0) {continue;} ni_test++; } ni_cvt=0; for (size_t i=0; i<indicator_cvt.size(); i++) { if (indicator_cvt[i]==0) {continue;} ni_cvt++; } np_obs=0; np_miss=0; for (size_t i=0; i<indicator_pheno.size(); i++) { if (indicator_cvt.size()!=0) { if (indicator_cvt[i]==0) {continue;} } for (size_t j=0; j<indicator_pheno[i].size(); j++) { if (indicator_pheno[i][j]==0) { np_miss++; } else { np_obs++; } } } /* if ((indicator_cvt).size()!=0) { ni_test=0; for (vector<int>::size_type i=0; i<(indicator_idv).size(); ++i) { indicator_idv[i]*=indicator_cvt[i]; ni_test+=indicator_idv[i]; } } if ((indicator_read).size()!=0) { ni_test=0; for (vector<int>::size_type i=0; i<(indicator_idv).size(); ++i) { indicator_idv[i]*=indicator_read[i]; ni_test+=indicator_idv[i]; } } */ if (ni_test==0) { error=true; cout<<"error! number of analyzed individuals equals 0. "<<endl; return; } if (a_mode==43) { if (ni_cvt==ni_test) { error=true; cout<<"error! no individual has missing phenotypes."<<endl; return; } if ((np_obs+np_miss)!=(ni_cvt*n_ph)) { error=true; //cout<<ni_cvt<<"\t"<<ni_test<<"\t"<<ni_total<<"\t"<<np_obs<<"\t"<<np_miss<<"\t"<<indicator_cvt.size()<<endl; cout<<"error! number of phenotypes do not match the summation of missing and observed phenotypes."<<endl; return; } } //output some information cout<<"## number of total individuals = "<<ni_total<<endl; if (a_mode==43) { cout<<"## number of analyzed individuals = "<<ni_cvt<<endl; cout<<"## number of individuals with full phenotypes = "<<ni_test<<endl; } else { cout<<"## number of analyzed individuals = "<<ni_test<<endl; } cout<<"## number of covariates = "<<n_cvt<<endl; cout<<"## number of phenotypes = "<<n_ph<<endl; if (a_mode==43) { cout<<"## number of observed data = "<<np_obs<<endl; cout<<"## number of missing data = "<<np_miss<<endl; } if (!file_gene.empty()) { cout<<"## number of total genes = "<<ng_total<<endl; } else if (file_epm.empty() && a_mode!=43 && a_mode!=5) { cout<<"## number of total SNPs = "<<ns_total<<endl; cout<<"## number of analyzed SNPs = "<<ns_test<<endl; } else {} //set d_pace to 1000 for gene expression if (!file_gene.empty() && d_pace==100000) { d_pace=1000; } //for case-control studies, count #cases and #controls int flag_cc=0; if (a_mode==13) { ni_case=0; ni_control=0; for (size_t i=0; i<indicator_idv.size(); i++) { if (indicator_idv[i]==0) {continue;} if (pheno[i][0]==0) {ni_control++;} else if (pheno[i][0]==1) {ni_case++;} else {flag_cc=1;} } cout<<"## number of cases = "<<ni_case<<endl; cout<<"## number of controls = "<<ni_control<<endl; } if (flag_cc==1) {cout<<"Unexpected non-binary phenotypes for case/control analysis. Use default (BSLMM) analysis instead."<<endl; a_mode=11;} //set parameters for BSLMM //and check for predict if (a_mode==11 || a_mode==12 || a_mode==13) { if (a_mode==11) {n_mh=1;} if (logp_min==0) {logp_min=-1.0*log((double)ns_test);} if (h_scale==-1) {h_scale=min(1.0, 10.0/sqrt((double)ni_test) );} if (rho_scale==-1) {rho_scale=min(1.0, 10.0/sqrt((double)ni_test) );} if (logp_scale==-1) {logp_scale=min(1.0, 5.0/sqrt((double)ni_test) );} if (h_min==-1) {h_min=0.0;} if (h_max==-1) {h_max=1.0;} if (s_max>ns_test) {s_max=ns_test; cout<<"s_max is re-set to the number of analyzed SNPs."<<endl;} if (s_max<s_min) {cout<<"error! maximum s value must be larger than the minimal value. current values = "<<s_max<<" and "<<s_min<<endl; error=true;} } else if (a_mode==41 || a_mode==42) { if (indicator_bv.size()!=0) { if (indicator_idv.size()!=indicator_bv.size()) { cout<<"error! number of rows in the phenotype file does not match that in the estimated breeding value file: "<<indicator_idv.size()<<"\t"<<indicator_bv.size()<<endl; error=true; } else { size_t flag_bv=0; for (size_t i=0; i<(indicator_bv).size(); ++i) { if (indicator_idv[i]!=indicator_bv[i]) {flag_bv++;} } if (flag_bv!=0) { cout<<"error! individuals with missing value in the phenotype file does not match that in the estimated breeding value file: "<<flag_bv<<endl; error=true; } } } } //file_mk needs to contain more than one line if (n_vc==1 && !file_mk.empty()) {cout<<"error! -mk file should contain more than one line."<<endl; error=true;} return; } void PARAM::PrintSummary () { if (n_ph==1) { cout<<"pve estimate ="<<pve_null<<endl; cout<<"se(pve) ="<<pve_se_null<<endl; } else { } return; } void PARAM::ReadGenotypes (gsl_matrix *UtX, gsl_matrix *K, const bool calc_K) { string file_str; if (!file_bfile.empty()) { file_str=file_bfile+".bed"; if (ReadFile_bed (file_str, indicator_idv, indicator_snp, UtX, K, calc_K)==false) {error=true;} } else { if (ReadFile_geno (file_geno, indicator_idv, indicator_snp, UtX, K, calc_K)==false) {error=true;} } return; } void PARAM::CalcKin (gsl_matrix *matrix_kin) { string file_str; gsl_matrix_set_zero (matrix_kin); if (!file_bfile.empty() ) { file_str=file_bfile+".bed"; if (PlinkKin (file_str, indicator_snp, a_mode-20, d_pace, matrix_kin)==false) {error=true;} } else { file_str=file_geno; if (BimbamKin (file_str, indicator_snp, a_mode-20, d_pace, matrix_kin)==false) {error=true;} } return; } void PARAM::WriteMatrix (const gsl_matrix *matrix_U, const string suffix) { string file_str; file_str=path_out+"/"+file_out; file_str+="."; file_str+=suffix; file_str+=".txt"; ofstream outfile (file_str.c_str(), ofstream::out); if (!outfile) {cout<<"error writing file: "<<file_str.c_str()<<endl; return;} outfile.precision(10); for (size_t i=0; i<matrix_U->size1; ++i) { for (size_t j=0; j<matrix_U->size2; ++j) { outfile<<gsl_matrix_get (matrix_U, i, j)<<"\t"; } outfile<<endl; } outfile.close(); outfile.clear(); return; } void PARAM::WriteVector (const gsl_vector *vector_D, const string suffix) { string file_str; file_str=path_out+"/"+file_out; file_str+="."; file_str+=suffix; file_str+=".txt"; ofstream outfile (file_str.c_str(), ofstream::out); if (!outfile) {cout<<"error writing file: "<<file_str.c_str()<<endl; return;} outfile.precision(10); for (size_t i=0; i<vector_D->size; ++i) { outfile<<gsl_vector_get (vector_D, i)<<endl; } outfile.close(); outfile.clear(); return; } void PARAM::CheckCvt () { if (indicator_cvt.size()==0) {return;} size_t ci_test=0; gsl_matrix *W=gsl_matrix_alloc (ni_test, n_cvt); for (vector<int>::size_type i=0; i<indicator_idv.size(); ++i) { if (indicator_idv[i]==0 || indicator_cvt[i]==0) {continue;} for (size_t j=0; j<n_cvt; ++j) { gsl_matrix_set (W, ci_test, j, (cvt)[i][j]); } ci_test++; } size_t flag_ipt=0; double v_min, v_max; set<size_t> set_remove; //check if any columns is an intercept for (size_t i=0; i<W->size2; i++) { gsl_vector_view w_col=gsl_matrix_column (W, i); gsl_vector_minmax (&w_col.vector, &v_min, &v_max); if (v_min==v_max) {flag_ipt=1; set_remove.insert (i);} } //add an intecept term if needed if (n_cvt==set_remove.size()) { indicator_cvt.clear(); n_cvt=1; } else if (flag_ipt==0) { cout<<"no intecept term is found in the cvt file. a column of 1s is added."<<endl; for (vector<int>::size_type i=0; i<indicator_idv.size(); ++i) { if (indicator_idv[i]==0 || indicator_cvt[i]==0) {continue;} cvt[i].push_back(1.0); } n_cvt++; } else {} gsl_matrix_free(W); return; } //post-process phentoypes, covariates void PARAM::ProcessCvtPhen () { //convert indicator_pheno to indicator_idv int k=1; indicator_idv.clear(); for (size_t i=0; i<indicator_pheno.size(); i++) { k=1; for (size_t j=0; j<indicator_pheno[i].size(); j++) { if (indicator_pheno[i][j]==0) {k=0;} } indicator_idv.push_back(k); } //remove individuals with missing covariates if ((indicator_cvt).size()!=0) { for (vector<int>::size_type i=0; i<(indicator_idv).size(); ++i) { indicator_idv[i]*=indicator_cvt[i]; } } //obtain ni_test ni_test=0; for (vector<int>::size_type i=0; i<(indicator_idv).size(); ++i) { if (indicator_idv[i]==0) {continue;} ni_test++; } if (ni_test==0) { error=true; cout<<"error! number of analyzed individuals equals 0. "<<endl; return; } //check covariates to see if they are correlated with each other, and to see if the intercept term is included //after getting ni_test //add or remove covariates if (indicator_cvt.size()!=0) { CheckCvt(); } else { vector<double> cvt_row; cvt_row.push_back(1); for (vector<int>::size_type i=0; i<(indicator_idv).size(); ++i) { indicator_cvt.push_back(1); cvt.push_back(cvt_row); } } return; } void PARAM::CopyCvt (gsl_matrix *W) { size_t ci_test=0; for (vector<int>::size_type i=0; i<indicator_idv.size(); ++i) { if (indicator_idv[i]==0 || indicator_cvt[i]==0) {continue;} for (size_t j=0; j<n_cvt; ++j) { gsl_matrix_set (W, ci_test, j, (cvt)[i][j]); } ci_test++; } return; } //if flag=0, then use indicator_idv to load W and Y //else, use indicator_cvt to load them void PARAM::CopyCvtPhen (gsl_matrix *W, gsl_vector *y, size_t flag) { size_t ci_test=0; for (vector<int>::size_type i=0; i<indicator_idv.size(); ++i) { if (flag==0) { if (indicator_idv[i]==0) {continue;} } else { if (indicator_cvt[i]==0) {continue;} } gsl_vector_set (y, ci_test, (pheno)[i][0]); for (size_t j=0; j<n_cvt; ++j) { gsl_matrix_set (W, ci_test, j, (cvt)[i][j]); } ci_test++; } return; } //if flag=0, then use indicator_idv to load W and Y //else, use indicator_cvt to load them void PARAM::CopyCvtPhen (gsl_matrix *W, gsl_matrix *Y, size_t flag) { size_t ci_test=0; for (vector<int>::size_type i=0; i<indicator_idv.size(); ++i) { if (flag==0) { if (indicator_idv[i]==0) {continue;} } else { if (indicator_cvt[i]==0) {continue;} } for (size_t j=0; j<n_ph; ++j) { gsl_matrix_set (Y, ci_test, j, (pheno)[i][j]); } for (size_t j=0; j<n_cvt; ++j) { gsl_matrix_set (W, ci_test, j, (cvt)[i][j]); } ci_test++; } return; } void PARAM::CopyRead (gsl_vector *log_N) { size_t ci_test=0; for (vector<int>::size_type i=0; i<indicator_idv.size(); ++i) { if (indicator_idv[i]==0) {continue;} gsl_vector_set (log_N, ci_test, log(vec_read[i]) ); ci_test++; } return; }