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/**
* \file main_functions_dump.cpp
* \author M. Kooyman
* \author L.C. Karssen
* \author Yurii S. Aulchenko (cox, log, lin regressions)
* \author Maksim V. Struchalin
*
* \brief File containing some auxiliary functions that used to be in
* main.cpp. Having them here helps to keep the overview.
*
* Created on: Nov 27, 2013
* Author: mkooyman
*
*
* Copyright (C) 2009--2016 Various members of the GenABEL team. See
* the SVN commit logs for more details.
*
* 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 2
* 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, write to the Free Software
* Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston,
* MA 02110-1301, USA.
*
*/
#include <stdio.h>
#include <iostream>
#include <cstdlib>
#include <fstream>
#include <sstream>
#include <string>
#include <iomanip>
#include <vector>
#include "maskedmatrix.h"
#include "mlinfo.h"
#include "phedata.h"
#include "invsigma.h"
#include "command_line_settings.h"
/**
* \brief Print progress bar of analysis to stdout.
*
* Send a progress update (a percentage) to stdout so that the user
* has a rough indication of the percentage of SNPs that has already
* been completed.
*
* @param csnp Number of the SNP that is currently being analysed.
* @param nsnps Total number of SNPs
*/
void update_progress_to_cmd_line(const int csnp, const int nsnps)
{
std::cout << std::setprecision(2) << std::fixed;
if (csnp % 1000 == 0)
{
if (csnp == 0)
{
std::cout << "Analysis: "
<< std::setw(5)
<< 100. * static_cast<double>(csnp)
/ static_cast<double>(nsnps)
<< "%...";
}
else
{
std::cout << "\b\b\b\b\b\b\b\b\b"
<< std::setw(5)
<< 100. * static_cast<double>(csnp)
/ static_cast<double>(nsnps)
<< "%...";
}
std::cout.flush();
}
std::cout << std::setprecision(6);
}
/**
* \brief Open an output file for each model when using probability
* data (npgreds == 2).
*
* This function creates the _2df.out.txt etc. files.
*
* @param outfile Vector of output streams
* @param outfilename_str Basename of the outputfiles.
*/
void open_files_for_output(std::vector<std::ofstream*>& outfile,
const std::string& outfilename_str)
{
//create a list of filenames
const int amount_of_files = 5;
std::string filenames[amount_of_files] = {
outfilename_str + "_2df.out.txt",
outfilename_str + "_add.out.txt",
outfilename_str + "_domin.out.txt",
outfilename_str + "_recess.out.txt",
outfilename_str + "_over_domin.out.txt" };
for (int i = 0; i < amount_of_files; i++)
{
outfile.push_back(new std::ofstream());
outfile[i]->open((filenames[i]).c_str());
if (!outfile[i]->is_open())
{
std::cerr << "Cannot open file for writing: "
<< filenames[i]
<< "\n";
exit(1);
}
}
}
int create_phenotype(phedata& phd, const cmdvars& input_var)
{
phd.setphedata(input_var.getPhefilename(),
input_var.getNoutcomes(),
input_var.getNpeople(),
input_var.getInteraction(),
input_var.isIscox());
int interaction_cox = input_var.getInteraction();
#if COXPH
interaction_cox--;
#endif
if (input_var.getInteraction() < 0 ||
input_var.getInteraction() > phd.ncov ||
interaction_cox > phd.ncov)
{
std::cerr << "error: Interaction parameter is out of range "
<< "(interaction="
<< input_var.getInteraction()
<< ") \n";
exit(1);
}
return interaction_cox;
}
/**
* \brief Load the inverse variance-covariance matrix into an InvSigma object.
*
* @param input_var Object containing the values of the various
* command line options.
* @param phd Object with phenotype data
* @param invvarmatrix The object of type masked_matrix in which the
* inverse variance-covariance matrix is returned.
*/
void loadInvSigma(const cmdvars& input_var, const phedata& phd,
masked_matrix& invvarmatrix)
{
std::cout << "You are running mmscore...\n";
InvSigma inv(input_var.getInverseFilename(), phd);
// invvarmatrix = inv.get_matrix();
//double par = 1.; //var(phd.Y)*phd.nids/(phd.nids-phd.ncov-1);
invvarmatrix.set_matrix(inv.get_matrix()); // = invvarmatrix * par;
std::cout << " loaded InvSigma...\n" << std::flush;
}
/**
* \brief Create the first part of the output file header.
*
* \param outfile Vector of output file streams. Contains the streams
* of the output file(s). One file when using dosage data (ngpreds==1)
* and one for each genetic model in case probabilities are used
* (ngpreds==2).
* \param input_var Object containing the values of the various
* command line options.
* \param phd Object with phenotype data
*/
void create_start_of_header(std::vector<std::ofstream*>& outfile,
const cmdvars& input_var, const phedata& phd)
{
for (unsigned int i = 0; i < outfile.size(); i++)
{
(*outfile[i]) << "name"
<< input_var.getSep()
<< "A1"
<< input_var.getSep()
<< "A2"
<< input_var.getSep()
<< "Freq1"
<< input_var.getSep()
<< "MAF"
<< input_var.getSep()
<< "Quality"
<< input_var.getSep()
<< "Rsq"
<< input_var.getSep()
<< "n"
<< input_var.getSep()
<< "Mean_predictor_allele";
if (input_var.getChrom() != "-1")
(*outfile[i]) << input_var.getSep() << "chrom";
if (input_var.getMapfilename() != NULL)
(*outfile[i]) << input_var.getSep() << "position";
if (input_var.getFlipMAF())
(*outfile[i]) << input_var.getSep() << "allelesFlipped";
}
if (input_var.getAllcov()) //All covariates in output
{
for (unsigned int file = 0; file < outfile.size(); file++)
for (int i = 0; i < phd.n_model_terms - 1; i++)
*outfile[file] << input_var.getSep()
<< "beta_"
<< phd.model_terms[i]
<< input_var.getSep()
<< "sebeta_"
<< phd.model_terms[i];
}
}
/**
* \brief Create the rest of header of the output file(s).
*
* \sa create_start_of_header for the creation of the first few header
* columns.
*
* \param outfile vector of output file streams. Contains the streams
* of the output file(s). One file when using dosage data (ngpreds==1)
* and one for each genetic model in case probabilities are used
* (ngpreds==2).
* \param input_var object containing the values of the various
* command line options.
* \param phd object with phenotype data
* \param interaction_cox are we using the Cox model with interaction?
*/
void create_header(std::vector<std::ofstream*>& outfile,
const cmdvars& input_var, const phedata& phd,
const int& interaction_cox)
{
create_start_of_header(outfile, input_var, phd);
if (input_var.getNgpreds() == 1) // dose data: only additive model
{
*outfile[0] << input_var.getSep()
<< "beta_SNP_addA1"
<< input_var.getSep()
<< "sebeta_SNP_addA1";
if (input_var.getInteraction() != 0)
{
*outfile[0] << input_var.getSep()
<< "beta_SNP_"
<< phd.model_terms[interaction_cox]
<< input_var.getSep()
<< "sebeta_SNP_"
<< phd.model_terms[interaction_cox];
}
if (input_var.getInverseFilename() == NULL)
{
//Han Chen
#if !COXPH
if (input_var.getInteraction() != 0 && !input_var.getAllcov())
{
*outfile[0] << input_var.getSep()
<< "cov_SNP_int_SNP_"
<< phd.model_terms[interaction_cox];
}
#endif
}
*outfile[0] << input_var.getSep() << "chi2_SNP_add";
*outfile[0] << "\n";
} // ngpreds == 1
else if (input_var.getNgpreds() == 2) // prob data: all models
{
*outfile[0] << input_var.getSep()
<< "beta_SNP_A1A2"
<< input_var.getSep()
<< "sebeta_SNP_A1A2"
<< input_var.getSep()
<< "beta_SNP_A1A1"
<< input_var.getSep()
<< "sebeta_SNP_A1A1";
*outfile[1] << input_var.getSep()
<< "beta_SNP_addA1"
<< input_var.getSep()
<< "sebeta_SNP_addA1";
*outfile[2] << input_var.getSep()
<< "beta_SNP_domA1"
<< input_var.getSep()
<< "sebeta_SNP_domA1";
*outfile[3] << input_var.getSep()
<< "beta_SNP_recA1"
<< input_var.getSep()
<< "sebeta_SNP_recA1";
*outfile[4] << input_var.getSep()
<< "beta_SNP_odomA1"
<< input_var.getSep()
<< "sebeta_SNP_odomA1";
if (input_var.getInteraction() != 0)
{
//Han Chen
*outfile[0] << input_var.getSep()
<< "beta_SNP_A1A2_"
<< phd.model_terms[interaction_cox]
<< input_var.getSep()
<< "sebeta_SNP_A1A2_"
<< phd.model_terms[interaction_cox]
<< input_var.getSep()
<< "beta_SNP_A1A1_"
<< phd.model_terms[interaction_cox]
<< input_var.getSep()
<< "sebeta_SNP_A1A1_"
<< phd.model_terms[interaction_cox];
#if !COXPH
if (input_var.getInverseFilename() == NULL &&
!input_var.getAllcov())
{
*outfile[0] << input_var.getSep()
<< "cov_SNP_A1A2_int_SNP_"
<< phd.model_terms[interaction_cox]
<< input_var.getSep()
<< "cov_SNP_A1A1_int_SNP_"
<< phd.model_terms[interaction_cox];
}
#endif
//Oct 26, 2009
for (unsigned int file = 1; file < outfile.size(); file++)
{
*outfile[file] << input_var.getSep()
<< "beta_SNP_"
<< phd.model_terms[interaction_cox]
<< input_var.getSep()
<< "sebeta_SNP_"
<< phd.model_terms[interaction_cox];
//Han Chen
#if !COXPH
if (input_var.getInverseFilename() == NULL
&& !input_var.getAllcov())
{
*outfile[file] << input_var.getSep()
<< "cov_SNP_int_SNP_"
<< phd.model_terms[interaction_cox];
}
#endif
//Oct 26, 2009
}
}
*outfile[0] << input_var.getSep() << "chi2_SNP_2df\n";
*outfile[1] << input_var.getSep() << "chi2_SNP_add\n";
*outfile[2] << input_var.getSep() << "chi2_SNP_dom\n";
*outfile[3] << input_var.getSep() << "chi2_SNP_rec\n";
*outfile[4] << input_var.getSep() << "chi2_SNP_odom\n";
} // End: ngpreds == 2
else
{
cerr << "Error: create_header(): ngpreds != 1 or 2.\n";
}
}
/**
* \brief Write the information from the mlinfo file to the output
* file(s).
*
* \param outfile Vector of output file(s)
* \param file index number identifying the file in the vector of files
* \param mli mlinfo object
* \param csnp number of the SNP that is currently being analysed
* \param input_var object containing the information of the options
* specified on the command line
* \param gcount The number of non-NaN genotypes
* \param freq The allele frequency based on the non-NaN genotypes
*/
void write_mlinfo(const std::vector<std::ofstream*>& outfile,
const unsigned int file, const mlinfo& mli,
const int csnp, const cmdvars& input_var,
const int gcount, const double freq)
{
*outfile[file] << mli.name[csnp]
<< input_var.getSep()
<< mli.A1[csnp]
<< input_var.getSep()
<< mli.A2[csnp]
<< input_var.getSep()
<< mli.Freq1[csnp]
<< input_var.getSep()
<< mli.MAF[csnp]
<< input_var.getSep()
<< mli.Quality[csnp]
<< input_var.getSep()
<< mli.Rsq[csnp]
<< input_var.getSep()
<< gcount
<< input_var.getSep()
<< freq;
if (input_var.getChrom() != "-1")
{
*outfile[file] << input_var.getSep() << input_var.getChrom();
}
if (input_var.getMapfilename() != NULL)
{
*outfile[file] << input_var.getSep() << mli.map[csnp];
}
if (input_var.getFlipMAF())
{
*outfile[file] << input_var.getSep() << mli.allelesFlipped[csnp];
}
}
/**
* \brief Get the position within a (row or column) vector (the index)
* where a \f$ \beta \f$ (or \f$ se_{\beta} \f$) starts.
*
* This is basically a matter of counting backwards from the end of
* the vector/list.
*
* @param input_var Object containing the values of the various
* command line options.
* @param model Number of the genetic model (additive, etc)
* @param number_of_rows_or_columns Total number of rows or columns in
* the vector.
*
* @return Start position of beta for this model
*/
int get_start_position(const cmdvars& input_var, const int model,
const int number_of_rows_or_columns)
{
int start_pos;
if (!input_var.getAllcov() &&
model == 0 &&
input_var.getInteraction() == 0)
{
if (input_var.getNgpreds() == 2)
{
start_pos = number_of_rows_or_columns - 2;
} else {
start_pos = number_of_rows_or_columns - 1;
}
}
else if (!input_var.getAllcov() && model == 0
&& input_var.getInteraction() != 0)
{
if (input_var.getNgpreds() == 2)
{
start_pos = number_of_rows_or_columns - 4;
} else {
start_pos = number_of_rows_or_columns - 2;
}
}
else if (!input_var.getAllcov() && model != 0
&& input_var.getInteraction() == 0)
{
start_pos = number_of_rows_or_columns - 1;
}
else if (!input_var.getAllcov() && model != 0
&& input_var.getInteraction() != 0)
{
start_pos = number_of_rows_or_columns - 2;
}
else
{
start_pos = 0;
}
return start_pos;
}
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