pax_global_header 0000666 0000000 0000000 00000000064 12774457224 0014530 g ustar 00root root 0000000 0000000 52 comment=68db449089d255f6d22b30c711929f2beec663d2
seer-1.1.2/ 0000775 0000000 0000000 00000000000 12774457224 0012467 5 ustar 00root root 0000000 0000000 seer-1.1.2/.gitignore 0000664 0000000 0000000 00000000606 12774457224 0014461 0 ustar 00root root 0000000 0000000 # Compiled Object files
*.slo
*.lo
*.o
*.obj
# Precompiled Headers
*.gch
*.pch
# Compiled Dynamic libraries
*.so
*.dylib
*.dll
# Fortran module files
*.mod
# Compiled Static libraries
*.lai
*.la
*.a
*.lib
# Executables
*.exe
*.out
*.app
seer
kmds
map_back
combineKmers
filter_seer
# Editors
.vimrc
.lvimrc
.ycm_extra_conf.py*
# Submodules
armadillo-*
mlpack-1.0.*
boost_1_*
gzstream
seer-1.1.2/.gitmodules 0000664 0000000 0000000 00000000111 12774457224 0014635 0 ustar 00root root 0000000 0000000 [submodule "dlib"]
path = dlib
url = https://github.com/davisking/dlib
seer-1.1.2/.travis.yml 0000664 0000000 0000000 00000001510 12774457224 0014575 0 ustar 00root root 0000000 0000000 sudo: required
language: cpp
before_install:
- sudo add-apt-repository ppa:ubuntu-toolchain-r/test -y
- sudo apt-get update -qq
- sudo apt-get install g++-4.9 liblapack-dev libboost-program-options-dev -y
install:
- wget http://www.cs.unc.edu/Research/compgeom/gzstream/gzstream.tgz
- tar xzf gzstream.tgz
- pushd gzstream && make && popd
- wget http://sourceforge.net/projects/arma/files/armadillo-6.700.6.tar.gz
- tar xzf armadillo-6.700.6.tar.gz
- pushd armadillo-6.700.6 && CXX=/usr/bin/g++-4.9 cmake . && make && sudo make install && popd
script: CXX=/usr/bin/g++-4.9 make CPPFLAGS="-I../gzstream -I../dlib -D DLIB_NO_GUI_SUPPORT=1 -D DLIB_USE_BLAS=1 -D DLIB_USE_LAPACK=1 -D NO_HDF5=1" SEER_LDLIBS="-L../gzstream -lgzstream -lz -larmadillo -lboost_program_options -llapack -lblas -lpthread" && make test
seer-1.1.2/LICENSE 0000664 0000000 0000000 00000043153 12774457224 0013502 0 ustar 00root root 0000000 0000000 GNU GENERAL PUBLIC LICENSE
Version 2, June 1991
Copyright (C) 1989, 1991 Free Software Foundation, Inc.,
51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
Everyone is permitted to copy and distribute verbatim copies
of this license document, but changing it is not allowed.
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How to Apply These Terms to Your New Programs
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the "copyright" line and a pointer to where the full notice is found.
{description}
Copyright (C) {year} {fullname}
This program is free software; you can redistribute it and/or modify
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(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
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Also add information on how to contact you by electronic and paper mail.
If the program is interactive, make it output a short notice like this
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Gnomovision version 69, Copyright (C) year name of author
Gnomovision comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
This is free software, and you are welcome to redistribute it
under certain conditions; type `show c' for details.
The hypothetical commands `show w' and `show c' should show the appropriate
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mouse-clicks or menu items--whatever suits your program.
You should also get your employer (if you work as a programmer) or your
school, if any, to sign a "copyright disclaimer" for the program, if
necessary. Here is a sample; alter the names:
Yoyodyne, Inc., hereby disclaims all copyright interest in the program
`Gnomovision' (which makes passes at compilers) written by James Hacker.
{signature of Ty Coon}, 1 April 1989
Ty Coon, President of Vice
This General Public License does not permit incorporating your program into
proprietary programs. If your program is a subroutine library, you may
consider it more useful to permit linking proprietary applications with the
library. If this is what you want to do, use the GNU Lesser General
Public License instead of this License.
seer-1.1.2/Makefile 0000775 0000000 0000000 00000000401 12774457224 0014125 0 ustar 00root root 0000000 0000000 export PREFIX=${HOME}/software
export BINDIR=$(PREFIX)/bin
all:
cd src && $(MAKE) all
clean:
cd src && $(MAKE) clean
cd test && $(MAKE) clean
install: all
cd src && $(MAKE) install
test: all
cd test && $(MAKE) test
.PHONY: all clean install test
seer-1.1.2/README.md 0000664 0000000 0000000 00000010263 12774457224 0013750 0 ustar 00root root 0000000 0000000 [](https://travis-ci.org/johnlees/seer)
# seer
Sequence element enrichment analysis. This document contains
installation instuctions. Usage can be found on the [wiki](https://github.com/johnlees/seer/wiki/Usage), and more information in the [paper](http://www.nature.com/articles/ncomms12797).
Installation
==============
###Use a pre-compiled release
Head to the [release](https://github.com/johnlees/seer/releases) page and download and unpack the tarball. If you have the dependencies installed use the dynamic version, otherwise use the static version (tested on Ubuntu only; static_all should work on other 64-bit Linux platforms).
###Use on a virtual machine
We have a virtual machine, containing SEER and other useful bioinformatics programs, which is available at
ftp://ftp.sanger.ac.uk/pub/pathogens/pathogens-vm/pathogens-vm.latest.ova
and can be imported as an appliance in [VirtualBox](https://www.virtualbox.org/).
###Compile source code
First clone the repository
git clone --recursive https://github.com/johnlees/seer
If you already have dlib:
git clone https://github.com/johnlees/seer
Currently tested on Linux only, installation should proceed as
make
make install
Full installation instructions are available below
Dependencies
--------------
seer currently depends on
- gzstream
- armadillo
- boost
- dlib
- HDF5
You will also require
- gcc >4.9 or equivalent
- gcc libstdc++ >4.9
You probably already have boost, HDF5 and dlib (as long as you did clone --recursive).
###Installation on Ubuntu/biolinux
Running the following commands will install seer
sudo add-apt-repository ppa:ubuntu-toolchain-r/test
sudo apt-get update
sudo apt-get install g++-4.9 libhdf5-dev libopenblas-dev libboost-program-options-dev
git clone --recursive https://github.com/johnlees/seer
cd seer
wget http://www.cs.unc.edu/Research/compgeom/gzstream/gzstream.tgz
tar xzf gzstream.tgz
cd gzstream &&make CC=/usr/bin/gcc-4.9
cd ..
wget http://sourceforge.net/projects/arma/files/armadillo-6.700.6.tar.gz
tar xzf armadillo-6.700.6.tar.gz
cd armadillo-6.700.6
CXX=/usr/bin/g++-4.9 cmake .
make
sudo make install
sudo ldconfig /usr/lib64
cd ..
cd src && make CXX=/usr/bin/g++-4.9
###General installation instructions
**gzstream**
Download and unpack to a folder gzstream in the root of the repository. Change into the directory and type
make
**HDF5**
Best installed with your distribution's package manager. Otherwise use
a binary from , or
if you wish to compile from source
gunzip < hdf5-X.Y.Z.tar.gz | tar xf -
cd hdf5-X.Y.Z
./configure --prefix=/usr/local/hdf5
make
make check
make install
make check-install
**armadillo**
Make sure HDF5 is installed first.
Download and unpack. Change into directory and type
cmake -DARMA_USE_HDF5=1 .
make
make install
**boost**
Best installed with your distribution's package manager, and you should use the c++11 version if possible.
For a manual installation, see for details on how to use ./b2 to install. I recommend that you create a user-config.jam file in the boost root which modifies the gcc compilation:
using gcc:
: std11
: g++
: -std=c++11
Then run
./bootstrap.sh
./b2 install toolset=gcc-std11
**dlib**
If not installed use the above git clone command to include with the
repository. Otherwise unpack header files to $(PREFIX)/include
**installation**
Currently tested on Linux only, installation should proceed as
make
make install
You may need to explicitly set the current GCC compiler, which you can
do by running
make CXX=g++-4.9
Usage, interpretation of results, and troubleshooting
=============
See the [wiki](https://github.com/johnlees/seer/wiki/Usage)
seer-1.1.2/dlib/ 0000775 0000000 0000000 00000000000 12774457224 0013401 5 ustar 00root root 0000000 0000000 seer-1.1.2/example_wrapper.py 0000775 0000000 0000000 00000014616 12774457224 0016247 0 ustar 00root root 0000000 0000000 #!/usr/bin/env python
#
# This is an example of a wrapper script for the whole seer analysis pipeline,
# based on having a cluster with LSF
#
# imports
import os,sys
from __future__ import print_function
import re
import argparse
import subprocess
# globals
subsampled_list = "kmds_tmp_files.txt"
job_num = re.compile('^Job <(\d+)>')
# subroutines
# Checks, for a list of job numbers, whether they are all done (or failed)
def check_done(jobs):
done = 1
for job in jobs:
bsub_ret = subprocess.check_call("bjobs -a -noheader -o \"stat exit_code delimiter=','\" " + str(job), shell=True)
status = bsub_ret.split(",")
if bsub_ret[1] == "RUN" | bsub_ret[1] == "PEND" | bsub_ret[1] == "WAIT":
done = 0
break
return done
# Command line arguments
parser = argparse.ArgumentParser()
parser.add_argument("infile", help="A file containing a list of dsm output files")
parser.add_argument("pheno", help=".pheno file containing metadata")
parser.add_argument("-o", "--out_prefix", help="Prefix for output files",default="seer")
parser.add_argument("-t", "--threads", help="Threads to use",type=int,default=4)
parser.add_argument("--LSF", help="Submit over LSF",action="store_true",default=False)
parser.add_argument("--pcs", help="Number of principal coordinates to use in population structure",type=int,default=3)
parser.add_argument("--subsample", help="Proportion of total kmers to use in mds calculation",type=float,default=0.001)
parser.add_argument("--maf", help="Minimum minor allele frequency",type=float,default=0.01)
parser.add_argument("--chisq",help="Chi^2 filter cutoff",type=float,default=10e-5)
parser.add_argument("--pval", help="pvalue cutoff",type=float,default=10e-8)
parser.add_argument("--assemble", help="assemble significant kmers, and perform association",action="store_true",default=False)
parser.add_argument("--reference", help="map kmers back to this reference file")
parser.add_argument("--drafts", help="file of annotated draft assemblies to map kmers back to")
args = parser.parse_args()
# Read in dsm files
if not (os.path.isfile(args.infile) & os.path.isfile(args.pheno)):
raise Exception("Mandatory input files do not exist")
with open(args.infile,'r') as f:
dsm_files = f.readlines()
dsm_files = [x.strip('\n') for x in dsm_files]
# Run kmds --no_mds on each file in parallel
# Collect output
print("Filtering " + str(len(dsm_files)) + " files\n")
i = 0
subsampled_output = []
jobs = []
for dsm in dsm_files:
i += 1
length = args.subsample * int(subprocess.check_output("gzip -d -c " + dsm + " | wc -l", shell=True))
try:
kmds_command = ""
if args.LSF:
kmds_command = "bsub -o kmds.step1.%J." + str(i) + ".o -e kmds.step1.%J." + str(i) + ".e "
kmds_command += "kmds -k " + str(dsm) + " -p " + str(args.pheno) + " -o kmds.step1." + str(i) + " --no_mds --maf " + str(args.maf) + " --chisq " + str(args.chi2) + " --size " + str(length)
print(kmds_command)
if args.LSF:
job_return = subprocess.check_output(kmds_command, shell=True)
m = job_num.match(job_return)
jobs.append(m.group())
else:
retcode = subprocess.call(kmds_command, shell=True)
if retcode < 0:
print("kmds step 1 file " + str(i) + " failed with ", -retcode, file=sys.stderr)
subsampled_output.append("kmds.step1." + str(i))
except OSError as e:
print("Execution failed:", e, file=sys.stderr)
# Check all jobs have finished
if args.LSF:
while not (check_done(jobs)):
os.sleep(30)
jobs = []
write_list = open(subsampled_list,'w')
for subsample in subsampled_output:
write_list.write(subsample + "\n")
write_list.close()
# Run kmds --mds_concat on output
print("Calculating MDS components\n")
try:
kmds_command = ""
if args.LSF:
kmds_command = "bsub -o kmds.step2.%J.o -e kmds.step2.%J..e -n" + str(args.threads) + " -R \"span[hosts=1]\" -R \"select[mem>4000] rusage[mem=4000]\" -M4000 "
kmds_command += "kmds --mds_concat " + str(subsampled_list) + " -o all_structure --threads " + str(args.threads) + " --pc " + str(args.pcs)
print(kmds_command)
if args.LSF:
job_return = subprocess.check_output(kmds_command, shell=True)
m = job_num.match(job_return)
jobs.append(m.group())
else:
retcode = subprocess.call(kmds_command, shell=True)
if retcode < 0:
print("kmds step 2 file failed with ", -retcode, file=sys.stderr)
except OSError as e:
print("Execution failed:", e, file=sys.stderr)
if args.LSF:
while not (check_done(jobs)):
os.sleep(30)
jobs = []
# Run seer on each file in parallel
print("Association on " + str(len(dsm_files)) + " files\n")
i = 0
seer_output = []
for dsm in dsm_files:
i += 1
try:
seer_command = ""
if args.LSF:
seer_command = "bsub -o kmds.step1.%J." + str(i) + ".o -e kmds.step1.%J." + str(i) + ".e -n" + str(args.threads) + " -R \"span[hosts=1]\""
seer_command += "'seer -k " + str(dsm) + " -p " + str(args.pheno) + " --no_filtering --pval " + str(args.pval) + " --struct all_structure.dsm --threads " + str(args.threads) + " --print_samples > seer." + str(i) + ".result'"
print(seer_command)
if args.LSF:
job_return = subprocess.check_output(seer_command, shell=True)
m = job_num.match(job_return)
jobs.append(m.group())
else:
retcode = subprocess.call(seer_command, shell=True)
if retcode < 0:
print("seer file " + str(i) + " failed with ", -retcode, file=sys.stderr)
seer_output.append("kmds." + str(i))
except OSError as e:
print("Execution failed:", e, file=sys.stderr)
if args.LSF:
while not (check_done(jobs)):
os.sleep(30)
jobs = []
# TODO post-processing:
subprocess.check_call("cat seer.*.result > seer.result", shell=True)
for i in range(1, len(dsm_files)):
subprocess.check_call("rm seer." + str(i) + ".result", shell=True)
# map back to references
subprocess.call("map_back -k seer.result -r " + args.drafts + " > kmer_draft_locations.txt")
# assembly of significant kmers
if (args.assemble):
subprocess.call("VelvetOptimiser.pl --s 11 --e 71 --t 1 -f '-short -fasta ../../resultErytrhomycin.fa' --m -1 --k max --c tbp")
# use blat by default
# fall back to blast
print("All pans piped\nAssocation results written to seer.result")
seer-1.1.2/scripts/ 0000775 0000000 0000000 00000000000 12774457224 0014156 5 ustar 00root root 0000000 0000000 seer-1.1.2/scripts/R_mds.pl 0000775 0000000 0000000 00000007445 12774457224 0015574 0 ustar 00root root 0000000 0000000 #!/usr/bin/perl -w
use strict;
use warnings;
use Getopt::Long;
my $usage_message = < -p
Projects distance matrix into lower number of dimensions
Requires R and rhdf5 to be installed, see github wiki for more details
Options
Required
-d, --dist Distance matrix output by kmds
-p, --pheno Phenotype file used by seer
Optional
-o, --output Output prefix (default 'all_structure')
--pc Number of dimensions to project into (default 3)
-R R executable (default 'which R')
-h, --help Displays this message
USAGE
my $tmp_file_name = "mds_tmp.Rscript";
#****************************************************************************************#
#* Main *#
#****************************************************************************************#
#* gets input parameters
my ($distance_file, $pheno, $output_prefix, $dimensions, $R_exec, $help);
GetOptions ("dist|d=s" => \$distance_file,
"pheno|p=s" => \$pheno,
"output|o=s" => \$output_prefix,
"pc=i" => \$dimensions,
"R=s" => \$R_exec,
"help|h" => \$help
) or die($usage_message);
if (defined($help) || !defined($distance_file) || !defined($pheno))
{
print $usage_message;
}
else
{
# Set defaults
if (!defined($dimensions))
{
$dimensions = 3;
}
if (!defined($R_exec))
{
$R_exec = `which R`;
chomp $R_exec;
}
if (!defined($output_prefix))
{
$output_prefix = "all_structure";
}
# Check number of samples matches, write .samples if ok
open(PHENO, $pheno) || die("Could not open pheno file $pheno: $!\n");
my @samples;
while (my $line_in = )
{
chomp $line_in;
my @fields = split(/\t/, $line_in);
push(@samples, $fields[1]);
}
my @samples_out = sort @samples;
open(DIST, $distance_file) || die("Could not open distance file $distance_file: $!\n");
my $dist_line = ;
my @dist_cols = split(",", $dist_line);
if(scalar(@dist_cols) != scalar(@samples_out))
{
die("Number of samples in $pheno does not match those in $distance_file\n");
}
my $dist_rows = 1;
while (my $line_in = )
{
$dist_rows++;
}
if($dist_rows != scalar(@dist_cols))
{
die("Distance matrix $distance_file is not square\n");
}
close DIST;
open(SAMPLES, ">$output_prefix.samples") || die("Could not write to $output_prefix.samples\n");
foreach my $sample (@samples_out)
{
print SAMPLES $sample . "\n";
}
close SAMPLES;
#****************************************************************************************#
#* Rscript *#
#****************************************************************************************#
my $Rscript = <$tmp_file_name") || die("Could not open $tmp_file_name: $!\n");
print RSCRIPT $Rscript;
close(RSCRIPT);
print STDERR "Running projection\n";
my $R_command = "$R_exec CMD BATCH --no-save --no-restore $tmp_file_name";
system($R_command);
unlink($tmp_file_name, "$tmp_file_name.Rout");
}
exit(0);
seer-1.1.2/scripts/blast_top_hits.pl 0000775 0000000 0000000 00000007327 12774457224 0017545 0 ustar 00root root 0000000 0000000 #!/usr/bin/perl -w
use strict;
use warnings;
use Getopt::Long;
use Text::CSV;
# Features
# BLAST and BLAT
# Exact/full query matches only
#
my $clean = 0;
my $tmp_sort = "tmp_blat.psl";
my $help_message = <
Reduces a blast or blat output file to the top hit for each query sequence
only
Options
Required
--input BLAST output file in -m 6/-outfmt 6 or BLAT .psl file
--mode Either blast or blat
Optional
--full_query The entire query must be reported as a match to appear
in the output
-h, --help Shows this help.
HELP
#****************************************************************************************#
#* Main *#
#****************************************************************************************#
#* gets input parameters
my ($file_in, $mode, $full_query, $help);
GetOptions( "input|f=s" => \$file_in,
"mode=s" => \$mode,
"full_query" => \$full_query,
"help|h" => \$help
) or die($help_message);
# Basic error check on inputs
if (defined($help))
{
print STDERR $help_message;
}
elsif (!defined($file_in) || !-e $file_in)
{
print STDERR "Input file not found\n";
print STDERR $help_message;
}
elsif (!defined($mode))
{
print STDERR "Operation mode must be either blast or blat\n";
print STDERR $help_message;
}
else
{
my (@columns, $qseqid, $qstart, $qend, $length);
if ($mode =~ /blat/i)
{
# psl format
# match mis- rep. N's Q gap Q gap T gap T gap strand Q Q Q Q T T T T block blockSizes qStarts tStarts
# match match count bases count bases name size start end name size start end count
@columns = qw( match mismatch repmatch Ns Qgapcount Qgapbases Tgapcount Tgapbases strand Qname Qsize Qstart Qend Tname Tsize Tstart Tend blockcount blocksizes qstarts tstarts );
$mode = "blat";
$qseqid = "Qname";
$qstart = "Qstart";
$qend = "Qend";
$length = "match";
# Ensure sorted by query, then match score
system("sort -k10,10 -k1,1nr $file_in > $tmp_sort");
$file_in = $tmp_sort;
$clean = 1;
}
elsif ($mode =~ /blast/i)
{
# blast outfmt 6
#qseqid sseqid pident length mismatch gapopen qstart qend sstart send evalue bitscore
@columns = qw( qseqid sseqid pident length mismatch gapopen qstart qend sstart send evalue bitscore );
$mode = "blast";
$qseqid = "qseqid";
$qstart = "qstart";
$qend = "qend";
$length = "length";
}
else
{
die("Operation mode $mode not supported. Use either blast or blat\n");
}
my $csv = Text::CSV->new ( { binary => 1, sep_char => "\t", auto_diag => 1, eol => $/ } ) # should set binary attribute.
or die "Cannot use CSV: ".Text::CSV->error_diag ();
my %rec;
$csv->bind_columns (\@rec{@columns});
open my $blast_in, "<:encoding(utf8)", "$file_in" or die "$file_in: $!";
my $previous_query = "";
while ($csv->getline($blast_in))
{
# Using speed advice from http://www.perlmonks.org/?node_id=937023
if ($rec{$qseqid} ne $previous_query)
{
if (!$full_query || ((($mode eq "blast" && $rec{$qstart} == 1) || $rec{$qstart} == 0) && $rec{$qend} == $rec{$length}))
{
$csv->print(*STDOUT, [ @rec{@columns} ]);
}
$previous_query = $rec{$qseqid};
}
}
if ($clean)
{
unlink $file_in;
}
}
exit(0);
seer-1.1.2/scripts/hits_to_fastq.pl 0000775 0000000 0000000 00000003522 12774457224 0017367 0 ustar 00root root 0000000 0000000 #!/usr/bin/perl -w
use strict;
use warnings;
use Getopt::Long;
my $usage_message = < -b
Creates a fastq for mapping from significant kmers
Options
Required
-k, --kmers Significant kmers, as output by seer
-b, --bonf Bonferroni correction to use (try 10e-8)
Optional
-h, --help Displays this message
USAGE
#****************************************************************************************#
#* Main *#
#****************************************************************************************#
#* gets input parameters
my ($kmer_file, $bonf, $help);
GetOptions ("kmers|k=s" => \$kmer_file,
"bonf|b=s" => \$bonf,
"help|h" => \$help
) or die($usage_message);
if (defined($help) || !defined($kmer_file) || !defined($bonf))
{
print $usage_message;
}
else
{
open(KMERS, $kmer_file) || die("Could not open kmer file: $kmer_file\n");
my $header = ;
my $i = 1;
while (my $line_in = )
{
chomp $line_in;
my ($kmer, $maf, $p_unadj, $p_adj, @junk) = split("\t", $line_in);
my $corrected_p = $p_adj / $bonf;
if ($corrected_p > 1)
{
$corrected_p = 1;
}
my $ascii_val;
if ($corrected_p == 0)
{
$ascii_val = 126;
}
else
{
$ascii_val = int(-10*log($corrected_p)/log(10)) + 33;
if ($ascii_val < 33)
{
$ascii_val = 33;
}
elsif ($ascii_val > 126)
{
$ascii_val = 126;
}
}
my $qual = chr($ascii_val) x length($kmer);
print join("\n", "\@$i", $kmer, "+", $qual) . "\n";
$i++;
}
close KMERS
}
exit(0);
seer-1.1.2/scripts/mapping_to_phandango.pl 0000775 0000000 0000000 00000005304 12774457224 0020674 0 ustar 00root root 0000000 0000000 #!/usr/bin/perl -w
use strict;
use warnings;
use Getopt::Long;
my $usage_message = < -k > phandango.plot
Creates a plot file for visualisation in phandango
Options
Required
-s, --sam Sam file of kmers mapped to reference (unsorted)
-k, --kmers Kmers that were mapped
Optional
-m, --map Minimum mapping quality
-h, --help Displays this message
USAGE
#****************************************************************************************#
#* Main *#
#****************************************************************************************#
#* gets input parameters
my ($kmer_file, $sam_file, $min_qual, $help);
GetOptions ("kmers|k=s" => \$kmer_file,
"sam|s=s" => \$sam_file,
"map|m=s" => \$min_qual,
"help|h" => \$help
) or die($usage_message);
if (defined($help) || !defined($kmer_file) || !defined($sam_file))
{
print $usage_message;
}
else
{
if (!defined($min_qual))
{
$min_qual = 0;
}
open(SAM, $sam_file) || die("Could not open sam file $sam_file: $!\n");
open(KMERS, $kmer_file) || die("Could not open kmer file $kmer_file: $!\n");
my $header = ;
my %points;
while (my $sam_line = )
{
chomp $sam_line;
if ($sam_line !~ /^@/)
{
my ($kmer_id, $flag, $chrom, $pos, $map_qual, $cigar, $mate_chrom, $mate_pos, $insert_size, $sequence, $quality, @optional) = split("\t", $sam_line);
# Get position, check for reverse complement
my ($end, $start);
if ($flag & 16)
{
$end = $pos;
$start = $pos - length($sequence) + 1;
}
else
{
$start = $pos;
$end = $pos + length($sequence) - 1;
}
my $kmer = ;
chomp $kmer;
# Don't report unmapped or low quality mapped kmers
if ($map_qual > $min_qual && !($flag & 4))
{
my ($kmer, $maf, $unadj, $adj, @other) = split("\t", $kmer);
my $log_p = 386; # Exponent limit of a double
if ($adj > 0)
{
$log_p = -log($adj)/log(10);
}
$points{$kmer}{pval} = $log_p;
$points{$kmer}{start} = $start;
$points{$kmer}{pos} = "$start..$end";
}
}
}
# Sort and print the output
my @keys = sort { $points{$a}{start} <=> $points{$b}{start} } keys(%points);
foreach my $kmer (@keys)
{
print join("\t", "26", ".", $points{$kmer}{pos}, $points{$kmer}{pval}, "0") . "\n";
}
}
exit(0);
seer-1.1.2/scripts/mash2matrix.pl 0000775 0000000 0000000 00000002275 12774457224 0016763 0 ustar 00root root 0000000 0000000 #!/usr/bin/perl -w
use strict;
use warnings;
my $usage_message = < > all_distances.csv
Uses mash (http://mash.readthedocs.io/) to create all pairwise distances
between samples.
NB: the input to mash should be the same order as the .pheno file
generate with:
cut -f 1 metadata.pheno | tr '\n' ' '
Then run
mash sketch -o reference
mash dist reference.msh reference.msh > mash_distances.txt
./mash2matrix.pl mash_distances.txt > all_distances.csv
USAGE
my $dist_file = $ARGV[0];
if (!-e $dist_file)
{
print STDERR $usage_message;
}
else
{
open(INPUT, $dist_file) || die("Could not open $dist_file\n");
my %row_idx;
my @mat_out;
my $i = 0;
while (my $line_in = )
{
chomp $line_in;
my ($el1, $el2, $dist, @junk) = split("\t", $line_in);
if (!defined($row_idx{$el1}))
{
$row_idx{$el1} = $i++;
}
if (!defined($row_idx{$el2}))
{
$row_idx{$el2} = $i++;
}
$mat_out[$row_idx{$el1}][$row_idx{$el2}] = $dist;
}
for (my $i = 0; $i < scalar(@{$mat_out[0]}); $i++)
{
print join(",", @{$mat_out[$i]}) . "\n";
}
}
exit(0);
seer-1.1.2/scripts/reformat_output.pl 0000775 0000000 0000000 00000004373 12774457224 0017764 0 ustar 00root root 0000000 0000000 #!/usr/bin/perl -w
use strict;
use warnings;
use Getopt::Long;
my $usage_message = < -p
Reformats output for easier reading as tsv. Prints to stdout
Options
Required
-k, --kmers Filtered significant kmers, as output by seer + filter_seer
-p, --pheno Phenotype file used by seer
Optional
-h, --help Displays this message
USAGE
#****************************************************************************************#
#* Main *#
#****************************************************************************************#
#* gets input parameters
my ($kmer_file, $pheno, $help);
GetOptions ("kmers|k=s" => \$kmer_file,
"pheno|p=s" => \$pheno,
"help|h" => \$help
) or die($usage_message);
if (defined($help) || !defined($kmer_file) || !defined($pheno))
{
print $usage_message;
}
else
{
# Read possible samples
open(PHENO, $pheno) || die("Could not open pheno file $pheno\n");
my @samples;
while (my $pheno_line = )
{
chomp $pheno_line;
my ($sample, $junk, $pheno) = split("\t", $pheno_line);
push(@samples, $sample);
}
# Keep everything sorted (as depends on kmer counting program used)
@samples = sort @samples;
close PHENO;
# Print header row to stdout
open(KMERS, $kmer_file) || die("Could not open kmer file $kmer_file\n");
print join("\t", "sequence", "maf", "unadj_p_val", "p_val", "beta", "se", "comments", @samples) . "\n";
while (my $kmer_line = )
{
chomp $kmer_line;
my ($sequence, $maf, $unadj, $adj, $beta, $se, $comment, @kmer_samples) = split("\t", $kmer_line);
@kmer_samples = sort @kmer_samples;
print join("\t", $sequence, $maf, $unadj, $adj, $beta, $se, $comment) . "\t";
my $next_sample = shift(@kmer_samples);
foreach my $sample (@samples)
{
print "\t";
if ($sample eq $next_sample)
{
print $sample;
if (scalar(@kmer_samples) != 0)
{
$next_sample = shift(@kmer_samples);
}
}
}
print "\n";
}
close KMERS;
}
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" "@ "W jB- IENDB` seer-1.1.2/src/ 0000775 0000000 0000000 00000000000 12774457224 0013256 5 ustar 00root root 0000000 0000000 seer-1.1.2/src/Makefile 0000775 0000000 0000000 00000007614 12774457224 0014731 0 ustar 00root root 0000000 0000000 # Default prefix. Can change this to system directories if needed
PREFIX=${HOME}/software
BINDIR=$(PREFIX)/bin
# Intel compiler - uncomment if you have icpc and mkl.
# gcc
#CXX=icpc
#CXXFLAGS=-Wall -O3 -parallel -ipo -std=c++11
#SEER_LDLIBS=-L../gzstream -L$(PREFIX)/lib -lhdf5 -lgzstream -lz -larmadillo -lboost_program_options -mkl
#MAP_LDLIBS=-L$(PREFIX)/lib -lboost_program_options -mkl
#COMBINE_LDLIBS=-L../gzstream -L$(PREFIX)/lib -lgzstream -lz -lboost_program_options
#FILTER_LDLIBS=-L$(PREFIX)/lib -lboost_program_options -mkl
# gcc
CXXFLAGS=-Wall -O3 -std=c++11
SEER_LDLIBS=-L../gzstream -L$(PREFIX)/lib -lhdf5 -lgzstream -lz -larmadillo -lboost_program_options -llapack -lblas -lpthread
MAP_LDLIBS=-L$(PREFIX)/lib -lboost_program_options -lpthread
COMBINE_LDLIBS=-L../gzstream -L$(PREFIX)/lib -lgzstream -lz -lboost_program_options
FILTER_LDLIBS=-L$(PREFIX)/lib -lboost_program_options
CPPFLAGS=-I$(PREFIX)/include -I../gzstream -I../dlib -I/usr/local/hdf5/include -D DLIB_NO_GUI_SUPPORT=1 -D DLIB_USE_BLAS=1 -D DLIB_USE_LAPACK=1 -DARMA_USE_HDF5=1
# For static linking. To use type 'make static'
# Requires icpc and mkl
# Dynamically links libm, libz, libc
COMMON_LDLIBS=-L../gzstream -L$(PREFIX)/lib -static-libstdc++ -static-libgcc -static-intel
SEER_STATIC_LDLIBS=$(COMMON_LDLIBS) -Wl,-Bstatic -lhdf5 -lgzstream -larmadillo -lboost_program_options -Wl,-Bdynamic -lz -Wl,--start-group ${MKLROOT}/lib/intel64/libmkl_intel_ilp64.a ${MKLROOT}/lib/intel64/libmkl_core.a ${MKLROOT}/lib/intel64/libmkl_sequential.a -Wl,--end-group
MAP_STATIC_LDLIBS=$(COMMON_LDLIBS) -Wl,-Bstatic -lboost_program_options -Wl,-Bdynamic
COMBINE_STATIC_LDLIBS=$(COMMON_LDLIBS) -Wl,-Bstatic -lgzstream -lboost_program_options -Wl,-Bdynamic -lz
FILTER_STATIC_LDLIBS=-$(COMMON_LDLIBS) -Wl,-Bstatic -lboost_program_options -Wl,-Bdynamic
# Full static linking with gcc
#COMMON_LDLIBS=-L../gzstream -L$(PREFIX)/lib -static -static-libstdc++ -static-libgcc
#SEER_LDLIBS=$(COMMON_LDLIBS) -L../gzstream -L$(PREFIX)/lib -lhdf5 -lgzstream -lz -larmadillo -lboost_program_options -llapack -lblas -lpthread -lgfortran -lquadmath -lm
#MAP_LDLIBS=$(COMMON_LDLIBS) -L$(PREFIX)/lib -lboost_program_options -lpthread
#COMBINE_LDLIBS=$(COMMON_LDLIBS) -L../gzstream -L$(PREFIX)/lib -lgzstream -lz -lboost_program_options
#FILTER_LDLIBS=$(COMMON_LDLIBS) -L$(PREFIX)/lib -lboost_program_options
#
PROGRAMS=seer kmds map_back combineKmers filter_seer
STATIC_PROGRAMS=seer_static kmds_static map_back_static combineKmers_static filter_seer_static
CLASSES=sample.o significant_kmer.o kmer.o covar.o
COMMON_OBJECTS=$(CLASSES) seerCommon.o seerErr.o seerFilter.o seerIO.o seerChiFilter.o
SEER_OBJECTS=$(COMMON_OBJECTS) seerMain.o seerCmdLine.o seerContinuousAssoc.o seerBinaryAssoc.o logitFunction.o linearFunction.o
KMDS_OBJECTS=$(COMMON_OBJECTS) kmdsMain.o kmdsStruct.o kmdsCmdLine.o
MAP_OBJECTS=fasta.o significant_kmer.o mapMain.o mapCmdLine.o
COMBINE_OBJECTS=combineInit.o combineCmdLine.o combineKmers.o
FILTER_OBJECTS=significant_kmer.o filter_seer.o filterCmdLine.o
all: $(PROGRAMS)
static: $(STATIC_PROGRAMS)
clean:
$(RM) *.o ~* $(PROGRAMS)
install: all
install -d $(BINDIR)
install $(PROGRAMS) $(BINDIR)
seer: $(SEER_OBJECTS)
$(LINK.cpp) $^ $(SEER_LDLIBS) -o $@
kmds: $(KMDS_OBJECTS)
$(LINK.cpp) $^ $(SEER_LDLIBS) -o $@
map_back: $(MAP_OBJECTS)
$(LINK.cpp) $^ $(MAP_LDLIBS) -o $@
combineKmers: $(COMBINE_OBJECTS)
$(LINK.cpp) $^ $(COMBINE_LDLIBS) -o $@
filter_seer: $(FILTER_OBJECTS)
$(LINK.cpp) $^ $(FILTER_LDLIBS) -o $@
seer_static: $(SEER_OBJECTS)
$(LINK.cpp) $^ $(SEER_STATIC_LDLIBS) -o seer
kmds_static: $(KMDS_OBJECTS)
$(LINK.cpp) $^ $(SEER_STATIC_LDLIBS) -o kmds
map_back_static: $(MAP_OBJECTS)
$(LINK.cpp) $^ $(MAP_STATIC_LDLIBS) -o map_back
combineKmers_static: $(COMBINE_OBJECTS)
$(LINK.cpp) $^ $(COMBINE_STATIC_LDLIBS) -o combineKmers
filter_seer_static: $(FILTER_OBJECTS)
$(LINK.cpp) $^ $(FILTER_STATIC_LDLIBS) -o filter_seer
.PHONY: all static test clean install
seer-1.1.2/src/combineCmdLine.cpp 0000664 0000000 0000000 00000003065 12774457224 0016636 0 ustar 00root root 0000000 0000000 /*
* combineCmdLine.cpp
* Parses cmd line options for combineKmers
*
*/
#include "combineKmers.hpp"
namespace po = boost::program_options; // Save some typing
// Parse command line options using boost program options
int parseCommandLine (int argc, char *argv[], po::variables_map& vm)
{
int failed = 0;
//Required options
po::options_description required("Required options");
required.add_options()
("samples,r", po::value()->required(), "file with tab separated sample name and kmer file")
("output,o", po::value()->required(), "output file prefix");
po::options_description other("Other options");
other.add_options()
("min_samples", po::value()->default_value(1), "minimum number of samples kmer must occur in to be printed")
("help,h", "full help message");
po::options_description all;
all.add(required).add(other);
try
{
po::store(po::command_line_parser(argc, argv).options(all).run(), vm);
if (vm.count("help"))
{
printHelp(all);
failed = 1;
}
else
{
po::notify(vm);
failed = 0;
}
}
catch (po::error& e)
{
// Report errors from boost library
std::cerr << "Error in command line input: " << e.what() << "\n";
std::cerr << "Run 'combineKmers --help' for full option listing\n\n";
std::cerr << required << "\n" << other << "\n";
failed = 1;
}
return failed;
}
// Print long help message
void printHelp(po::options_description& help)
{
std::cerr << help << "\n";
}
seer-1.1.2/src/combineInit.cpp 0000664 0000000 0000000 00000001704 12774457224 0016224 0 ustar 00root root 0000000 0000000 /*
* combineInit.cpp
* Functions to initialise inpit for combineKmers
*
*/
#include "combineKmers.hpp"
std::vector > readSamples(const std::string& sample_file)
{
// Check input file can be opened, and so so
std::ifstream ist(sample_file.c_str());
if (!ist)
{
throw std::runtime_error("Could not open sample file " + sample_file + "\n");
}
// Structure to store in
std::vector > samples;
while(ist)
{
std::string sample, file;
ist >> sample >> file;
// Don't add after final line
if (ist)
{
samples.push_back(std::make_tuple(sample, file));
}
}
return samples;
}
size_t checkMin(const size_t num_samples, const int input_min_samples)
{
size_t return_min = input_min_samples;
if (input_min_samples < 1 || return_min > num_samples)
{
return_min = 1;
}
return return_min;
}
seer-1.1.2/src/combineKmers.cpp 0000664 0000000 0000000 00000005770 12774457224 0016411 0 ustar 00root root 0000000 0000000 /*
* combineKmers.cpp
* Takes the union of kmer counts generated by dsk
*
*/
//
// Consider transforming bases to a more compact representation
//
// Just use sample index vs. use full sample name
//
// TODO read dsk output directly
#include "combineKmers.hpp"
int main (int argc, char *argv[])
{
// Program description
std::cerr << "combineKmers: basic algorithm to combine kmer counts across samples\n";
// Do parsing and checking of command line params
// If no input options, give quick usage rather than full help
boost::program_options::variables_map vm;
if (argc == 1)
{
std::cerr << "Usage: combineKmers -s samples.txt -o all_kmers --min_samples 2\n\n"
<< "For full option details run combineKmers -h\n";
return 0;
}
else if (parseCommandLine(argc, argv, vm))
{
return 1;
}
// Read in list of sample kmer files and their names
std::vector > samples = readSamples(vm["samples"].as());
std::unordered_map sample_names;
size_t min_samples = checkMin(samples.size(), vm["min_samples"].as());
// Open the output file before counting kmers
ogzstream out_file((vm["output"].as() + ".gz").c_str());
// Map to store kmers
// TODO this would be neater if written with objects
std::unordered_map>> kmer_union;
// Add kmers to map
std::cerr << "Reading and mapping kmers..." << std::endl;
for (unsigned int i = 0; i < samples.size(); ++i)
{
std::string sample_name, filename;
std::tie(sample_name, filename) = samples[i];
// Rather than storing lots of copies of sample names as strings, just
// store hash keys
sample_names[i] = sample_name;
std::ifstream kmer_counts(filename.c_str());
if (kmer_counts)
{
std::cerr << "File " << i + 1 << "/" << samples.size() << "\r";
std::cerr.flush();
std::string kmer, abundance;
while (kmer_counts)
{
kmer_counts >> kmer >> abundance;
kmer_union[kmer].push_back(std::make_tuple(i, stoi(abundance)));
}
}
else
{
std::cerr << "Could not open " + filename << std::endl;
std::cerr << "Skipping..." << std::endl;
}
}
std::cerr << std::endl;
// Print results
std::cerr << "Printing union of kmers" << std::endl;
for(auto kmer_it = kmer_union.cbegin(); kmer_it != kmer_union.cend(); ++kmer_it)
{
if (kmer_it->second.size() >= min_samples)
{
out_file << kmer_it->first;
for (auto sample_it = kmer_it->second.cbegin(); sample_it != kmer_it->second.cend(); ++sample_it)
{
int sample, abundance;
std::tie (sample, abundance) = *sample_it;
out_file << " " << sample_names[sample] + ":" + std::to_string(abundance);
}
out_file << std::endl;
}
}
std::cerr << "Done." << std::endl;
return(0);
}
seer-1.1.2/src/combineKmers.hpp 0000664 0000000 0000000 00000001215 12774457224 0016404 0 ustar 00root root 0000000 0000000 /*
* Header file for combineKmers.cpp
* Takes union of dsk counted kmers
*
*/
// C++ stl includes
#include
#include
#include
#include
#include
#include
// Library includes
#include
#include
// Function prototypes
int parseCommandLine (int argc, char *argv[], boost::program_options::variables_map& vm);
void printHelp(boost::program_options::options_description& help);
std::vector > readSamples(const std::string& sample_file);
size_t checkMin(const size_t num_samples, const int input_min_samples);
seer-1.1.2/src/covar.cpp 0000664 0000000 0000000 00000001306 12774457224 0015074 0 ustar 00root root 0000000 0000000 /*
* covar.chpp
* Helper functions for covar class
*/
#include "covar.hpp"
// Sorts covariates on sample name, returns only values
std::vector Covar::sortCovars()
{
// Sort on sample name
std::sort(covariates.begin(), covariates.end(),
[](const std::tuple& lhs, const std::tuple& rhs)
{
return std::get<0>(lhs) < std::get<0>(rhs);
});
// Return a vector without sample names
std::vector sorted;
sorted.reserve(covariates.size());
for (auto it = covariates.begin(); it != covariates.end(); ++it)
{
sorted.push_back(std::get<1>(*it));
}
return sorted;
}
seer-1.1.2/src/covar.hpp 0000664 0000000 0000000 00000001076 12774457224 0015105 0 ustar 00root root 0000000 0000000 /*
* covar.hpp
* Header file for covar class
*/
#include
#include
#include
#include
// Samples have a name and phenotype
// Covariates will be added later
class Covar
{
public:
// Initialisation
Covar() {}
// Add values, and read back
std::vector sortCovars();
void addCovarValue (const std::string& sample, const std::string& value)
{covariates.push_back(std::make_tuple(sample, value));}
private:
std::vector> covariates;
};
seer-1.1.2/src/fasta.cpp 0000664 0000000 0000000 00000005364 12774457224 0015070 0 ustar 00root root 0000000 0000000 /*
* File: fasta.cpp
*
* Helper functions for the fasta sequence class
*
*/
#include "fasta.hpp"
// Read in sequences to memory from file
Fasta::Fasta(const std::string& obj_name, const std::string& filename)
:name(obj_name)
{
std::ifstream ist(filename.c_str());
if (!ist)
{
throw std::runtime_error("Could not open fasta file " + filename + "\n");
}
std::string line_in;
std::string seq, contig_name = "";
std::getline(ist, line_in);
if (ist.eof())
{
throw std::runtime_error("Could not read fasta file " + filename + "\n");
}
// Read header line, which starts with >
else if (line_in[0] == '>')
{
contig_name = line_in.substr(1);
}
else
{
throw std::runtime_error("Error reading fasta file " + filename + "\n"
"Header should start with '>', but has:\n" + line_in + "\n");
}
while (!ist.eof())
{
// New contig, new sequence
if (ist.peek() == '>')
{
sequence_names.push_back(contig_name);
sequences.push_back(seq);
seq = "";
std::getline(ist, line_in);
contig_name = line_in.substr(1);
}
else
{
// Concatenate sequence lines
std::getline(ist, line_in);
seq += line_in;
}
}
// Add in final contig
sequence_names.push_back(contig_name);
sequences.push_back(seq);
}
// All exact matches to search sequence in all sequences in fasta
std::vector Fasta::hasSeq(const std::string& search)
{
std::vector results;
Mapping hit;
// Search each sequence in fasta
std::vector::iterator name_it = Fasta::sequence_names.begin();
for (std::vector::iterator it = Fasta::sequences.begin(); it != Fasta::sequences.end(); ++it)
{
size_t hit_position = it->find(search);
// Search allowing for multiple hits
while (hit_position != std::string::npos)
{
hit.sequence_name = *name_it;
hit.position = hit_position;
results.push_back(hit);
hit_position = it->find(search, hit_position + 1);
}
// Move forward in name vector
++name_it;
}
return results;
}
// Prints results to hasSeq
// a nicer output than hasSeq: use this function
void Fasta::printMappings(std::ostream &os, const std::string search, std::mutex& mtx)
{
std::vector hits = hasSeq(search);
if (hits.size() > 0)
{
// Lock use of std::out to make sure output isn't mangled
mtx.lock();
for (std::vector::iterator it = hits.begin(); it != hits.end(); ++it)
{
os << "\t" << Fasta::name << ":" << it->sequence_name << ":" << it->position << "-" << it->position + search.length() - 1;
}
mtx.unlock();
}
}
seer-1.1.2/src/fasta.hpp 0000664 0000000 0000000 00000001767 12774457224 0015100 0 ustar 00root root 0000000 0000000 /*
* fasta.hpp
* Header file for fasta class
*/
#include
#include
#include
#include
#include
#include
#include
// Container for a successful mapping result
struct Mapping
{
std::string sequence_name;
long int position;
};
// Contains sequences and contig names
class Fasta
{
public:
// Initialisation
Fasta(const std::string& obj_name, const std::string& file);
// Complex operations
std::vector hasSeq(const std::string& search);
void printMappings(std::ostream &os, const std::string search, std::mutex& mtx);
// nonmodifying operations
std::string get_name() const { return name; }
static bool compareFasta(Fasta lhs, Fasta rhs) { return (lhs.name < rhs.name); }
private:
// Sequences and sequence names necessarily in same order as read in
std::vector sequences;
std::vector sequence_names;
std::string name;
};
seer-1.1.2/src/filterCmdLine.cpp 0000664 0000000 0000000 00000007520 12774457224 0016507 0 ustar 00root root 0000000 0000000 /*
* filterCmdLine.cpp
* Parses cmd line options for filter_seer
*
*/
#include "filter_seer.hpp"
namespace po = boost::program_options; // Save some typing
// Parse command line options using boost program options
int parseCommandLine (int argc, char *argv[], po::variables_map& vm)
{
int failed = 0;
//Required options
po::options_description required("Required options");
required.add_options()
("kmers,k", po::value()->required(), "file of output from seer");
po::options_description filters("Filtering options");
filters.add_options()
("chisq", po::value(), "minimum unadjusted p-value to output")
("pval", po::value(), "minimum adjusted p-value to output")
("maf", po::value(), "minimum maf/max 1-maf to output")
("beta", po::value(), "minimum |beta| to output")
("substr", "remove smaller kmers completely represented elsewhere")
("pos_beta", "output positive effect sizes only");
po::options_description other("Other options");
other.add_options()
("sort,s", po::value(), "field to sort on: chisq, pval, maf or beta")
("version", "prints version and exits")
("help,h", "full help message");
po::options_description all;
all.add(required).add(filters).add(other);
try
{
po::store(po::command_line_parser(argc, argv).options(all).run(), vm);
if (vm.count("help"))
{
printHelp(all);
failed = 1;
}
else if (vm.count("version"))
{
std::cout << VERSION << std::endl;
failed = 1;
}
else
{
po::notify(vm);
failed = 0;
}
}
catch (po::error& e)
{
// Report errors from boost library
std::cerr << "Error in command line input: " << e.what() << "\n";
std::cerr << "Run 'combineKmers --help' for full option listing\n\n";
std::cerr << required << "\n" << other << "\n";
failed = 1;
}
return failed;
}
// Makes sure command line options are valid. Casts into correct variables
cmdOptions processCmdLine(po::variables_map& vm)
{
cmdOptions processed_options;
processed_options.input_file = vm["kmers"].as();
if (vm.count("chisq"))
{
processed_options.chi_filter = fractionFilter(vm["chisq"].as());
}
else
{
processed_options.chi_filter = 0;
}
if (vm.count("pval"))
{
processed_options.p_filter = fractionFilter(vm["pval"].as());
}
else
{
processed_options.p_filter = 0;
}
if (vm.count("maf"))
{
processed_options.maf_filter = fractionFilter(vm["maf"].as());
if (processed_options.maf_filter > 0.5)
{
processed_options.maf_filter = 1 - processed_options.maf_filter;
}
}
else
{
processed_options.maf_filter = 0;
}
if (vm.count("beta"))
{
processed_options.beta_filter = stod(vm["beta"].as());
}
else
{
processed_options.beta_filter = 0;
}
if (vm.count("pos_beta"))
{
processed_options.neg_beta = 0;
}
else
{
processed_options.neg_beta = 1;
}
if (vm.count("substr"))
{
processed_options.substr_kmers = 0;
}
else
{
processed_options.substr_kmers = 1;
}
if (vm.count("sort"))
{
processed_options.sort_field = vm["sort"].as();
}
else
{
processed_options.sort_field = "";
}
return processed_options;
}
// Turns a string into a double between 0 and 1
double fractionFilter(const std::string& filter_input)
{
double parsed_filter = stod(filter_input);
if (parsed_filter < 0 || parsed_filter > 1)
{
parsed_filter = 0;
}
return parsed_filter;
}
// Print long help message
void printHelp(po::options_description& help)
{
std::cerr << help << "\n";
}
seer-1.1.2/src/filter_seer.cpp 0000664 0000000 0000000 00000010176 12774457224 0016272 0 ustar 00root root 0000000 0000000 /*
* filter_seer.cpp
* Filters significant kmers produced by seer
*
*/
#include "filter_seer.hpp"
int main (int argc, char *argv[])
{
// Program description
std::cerr << "filter_seer: post filtering of significant kmers\n";
// Do parsing and checking of command line params
// If no input options, give quick usage rather than full help
boost::program_options::variables_map vm;
if (argc == 1)
{
std::cerr << "Usage: filter_seer -k seer_kmers.txt --pos_beta > filtered_output.txt\n\n"
<< "For full option details run 'filter_seer -h'\n";
return 0;
}
else if (parseCommandLine(argc, argv, vm))
{
return 1;
}
cmdOptions options = processCmdLine(vm);
// Define sorting function object
sortSigKmer sort_kmers;
if (options.sort_field != "")
{
sort_kmers.addSortField(options.sort_field);
}
sortSigKmer substr_sort;
if (!options.substr_kmers)
{
substr_sort.addSortField("sequence");
}
std::list filtered_kmers;
// Open input file, check opened ok
try
{
std::ifstream kmers_in(options.input_file.c_str());
if (!kmers_in)
{
throw std::runtime_error("Could not open input file " + options.input_file);
}
else
{
// Read the header
std::string header;
std::getline(kmers_in, header);
int num_covar_fields = parseHeader(header);
while (kmers_in)
{
// Read in each kmer
Significant_kmer kmer(num_covar_fields);
kmers_in >> kmer;
if (kmers_in)
{
// Filter fields. MAF needs to include max, beta needs modulus
if (options.maf_filter && (kmer.maf() < options.maf_filter || 1-kmer.maf() < options.maf_filter))
{
continue;
}
else if (!options.neg_beta && kmer.beta() < 0)
{
continue;
}
else if (options.beta_filter && fabs(kmer.beta()) < options.beta_filter)
{
continue;
}
else if (options.chi_filter && kmer.unadj() > options.chi_filter)
{
continue;
}
else if (options.p_filter && kmer.p_val() > options.p_filter)
{
continue;
}
// If sorted, store in mem. Otherwise print immediately
else if (!sort_kmers && !substr_sort)
{
std::cout << kmer << std::endl;
}
else
{
filtered_kmers.push_back(kmer);
}
}
}
// Remove substr if necessary
if (substr_sort)
{
// Sort largest to smallest
filtered_kmers.sort(substr_sort);
auto list_it = filtered_kmers.begin();
while (list_it != filtered_kmers.end())
{
// Check in all the larger kmers that this one isn't
// a substring
bool found = 0;
for (auto sub_it = filtered_kmers.begin(); sub_it != list_it; ++sub_it)
{
if (sub_it->sequence().find(list_it->sequence()) != std::string::npos)
{
list_it = filtered_kmers.erase(list_it);
found = 1;
break;
}
}
if (!found)
{
++list_it;
}
}
}
// If sorting, sort, then print
if (sort_kmers)
{
filtered_kmers.sort(sort_kmers);
}
if (sort_kmers || substr_sort)
{
for (auto out_it = filtered_kmers.begin(); out_it != filtered_kmers.end(); ++out_it)
{
std::cout << *out_it << std::endl;
}
}
std::cerr << "Done." << std::endl;
}
}
catch (std::exception& e)
{
std::cerr << "Error: " << e.what() << std::endl;
}
return(0);
}
seer-1.1.2/src/filter_seer.hpp 0000664 0000000 0000000 00000001545 12774457224 0016277 0 ustar 00root root 0000000 0000000 /*
* Header file for filter_seer.cpp
* Filters significant kmers from seer
*
*/
// C++ stl includes
#include
#include
#include
#include
#include
#include
#include
// Library includes
#include
#include "significant_kmer.hpp"
// Constants
const std::string VERSION = "1.1.2";
// Structures
struct cmdOptions
{
std::string input_file, output_file, sort_field;
double maf_filter, chi_filter, p_filter, beta_filter;
bool neg_beta, substr_kmers;
};
// Function prototypes
int parseCommandLine (int argc, char *argv[], boost::program_options::variables_map& vm);
cmdOptions processCmdLine(boost::program_options::variables_map& vm);
double fractionFilter(const std::string& filter_input);
void printHelp(boost::program_options::options_description& help);
seer-1.1.2/src/kmds.hpp 0000664 0000000 0000000 00000001764 12774457224 0014735 0 ustar 00root root 0000000 0000000 /*
*
* kmds.hpp
* Header file for kmds
*
*/
// Common headers
#include "seercommon.hpp"
// C/C++ std headers
#include
#include
#include
// Constants
// Default options
const int pc_default = 3;
const long int size_default = 1000000;
// Structs
struct DistanceElement
{
unsigned int row;
unsigned int col;
double distance;
};
// kmdsCmdLine headers
int parseCommandLine (int argc, char *argv[], boost::program_options::variables_map& vm);
void printHelp(boost::program_options::options_description& help);
// kmdsStruct headers
arma::mat metricMDS(const arma::mat& populationMatrix, const int dimensions, const unsigned int threads, const std::string& distances_file = "");
arma::mat dissimiliarityMatrix(const arma::mat& inMat, const unsigned int threads);
std::vector threadDistance(std::vector element_list, const arma::mat& rectangular_matrix);
double distanceFunction(const arma::rowvec& vec_1, const arma::rowvec& vec_2);
seer-1.1.2/src/kmdsCmdLine.cpp 0000664 0000000 0000000 00000006533 12774457224 0016163 0 ustar 00root root 0000000 0000000 /*
* File: kmdsCmdLine.cpp
*
* Reads command line input to kmds
*
*/
#include "kmds.hpp"
namespace po = boost::program_options; // Save some typing
// Use boost::program_options to parse command line input
// This does pretty much all the parameter checking needed
int parseCommandLine (int argc, char *argv[], po::variables_map& vm)
{
int failed = 0;
//Required options
po::options_description required("Required options");
required.add_options()
("kmers,k", po::value(), "dsm kmer output file (not needed if using --mds_concat)")
("pheno,p", po::value(), ".pheno metadata");
po::options_description mds("MDS options");
mds.add_options()
("output,o", po::value(), "output prefix for new dsm file")
("no_mds", "do not perform MDS; output subsampled matrix instead")
("write_distances", "write csv of distance matrix")
("mds_concat", po::value(), "list of subsampled matrices to use in MDS. Performs only MDS; implies --no_filtering")
("pc", po::value()->default_value(pc_default), "number of principal coordinates to output")
("size", po::value()->default_value(size_default), "number of kmers to use in MDS")
("threads", po::value()->default_value(1), ("number of threads. Suggested: " + std::to_string(std::thread::hardware_concurrency())).c_str());
//Optional filtering parameters
//NB pval cutoffs are strings for display, and are converted to floats later
po::options_description filtering("Filtering options");
filtering.add_options()
("no_filtering", "turn off all filtering and do not output new kmer file")
("max_length", po::value()->default_value(max_length_default), "maximum kmer length")
("maf", po::value()->default_value(maf_default), "minimum kmer frequency")
("min_words", po::value(), "minimum kmer occurences. Overrides --maf");
po::options_description other("Other options");
other.add_options()
("version", "prints version and exits")
("help,h", "full help message");
po::options_description all;
all.add(required).add(mds).add(filtering).add(other);
try
{
po::store(po::command_line_parser(argc, argv).options(all).run(), vm);
if (vm.count("help"))
{
printHelp(all);
failed = 1;
}
else if (vm.count("version"))
{
std::cout << VERSION << std::endl;
failed = 1;
}
else
{
po::notify(vm);
failed = 0;
// Check input files exist, and can stat
if ((vm.count("kmers") && !fileStat(vm["kmers"].as())) || (vm.count("pheno") && !fileStat(vm["pheno"].as())))
{
failed = 1;
}
}
}
catch (po::error& e)
{
// Report errors from boost library
std::cerr << "Error in command line input: " << e.what() << "\n";
std::cerr << "Run 'kmds --help' for full option listing\n\n";
std::cerr << required << "\n" << other << "\n";
failed = 1;
}
return failed;
}
// Print long help message
void printHelp(po::options_description& help)
{
std::cerr << "kmds" << "\n";
std::cerr << "\t1) filter and subsample with --no_mds and --size\n";
std::cerr << "\t2) combine, and do metric multidimensional scaling with --mds_concat\n";
std::cerr << help << "\n";
}
seer-1.1.2/src/kmdsMain.cpp 0000664 0000000 0000000 00000020355 12774457224 0015532 0 ustar 00root root 0000000 0000000 /*
* File: kmdsMain.cpp
*
* Control process of kmds (mds of kmers)
*
*/
#include "kmds.hpp"
// globals
std::default_random_engine rand_gen;
// $1 file location, $2 file name, $3 file ending
const std::regex file_format_e ("^(.+)\\/(.+)\\.([^\\.]+)$");
const std::regex file_format_within_e ("^(.+)\\.([^\\.]+)$"); // When in same directory, but not with a ./ prefix
int main (int argc, char *argv[])
{
// STRUCTURE
// Read in command line
// dsm and pheno file
// Matrix size
// No. of prin components
// Have a no_filter option
// Output prefix, with default (same as input)
//
// Read a dsm line, convert to stringstream
// Parse as kmer
// Write out if passes filters
//
// Build large matrix using reservoir sampler
// Reserve enough space
// While under reserved space, add kmer to vector
// Then reservoir sample
//
// Convert kmers into matrix
//
// PCoA on matrix
// Save matrix of top 3 eigenvals (parameter input)
// Program description
std::cerr << "kmds: control for population structure\n";
// Do parsing and checking of command line params
// If no input options, give quick usage rather than full help
boost::program_options::variables_map vm;
if (argc == 1)
{
std::cerr << "Usage: kmds -k dsm.txt.gz -p data.pheno\n\n"
<< "For full option details run kmds -h\n";
return 0;
}
else if (parseCommandLine(argc, argv, vm))
{
return 1;
}
// Open .pheno file, parse into vector of samples
std::vector samples;
std::unordered_map sample_map;
if (vm.count("pheno"))
{
readPheno(vm["pheno"].as(), samples, sample_map);
}
else
{
throw std::runtime_error("--pheno option is compulsory");
}
arma::vec y = constructVecY(samples);
int continuous_phenotype = continuousPhenotype(samples);
// Normal operation
if (!vm.count("mds_concat"))
{
// Check compulsory paramters provided
if (!vm.count("kmers") || !vm.count("pheno"))
{
std::cerr << "Either -k and -p must be provided, or --mds_concat\n";
return 1;
}
cmdOptions parameters = verifyCommandLine(vm, samples);
// Open the dsm kmer ifstream, and read through the whole thing
igzstream kmer_file;
openDsmFile(kmer_file, parameters.kmers.c_str());
// Set up output files
ogzstream filtered_file;
std::string output_file_name, dsm_file_name, distances_file_name;
if (parameters.filter)
{
if (vm.count("output"))
{
output_file_name = parameters.output + ".kmers.gz";
}
else
{
output_file_name = std::regex_replace(parameters.kmers, file_format_e, std::string("$1/filtered.$2.gz"));
if (output_file_name == parameters.kmers) // If first match fails
{
output_file_name = std::regex_replace(parameters.kmers, file_format_within_e, std::string("filtered.$1.gz"));
}
}
filtered_file.open(output_file_name.c_str());
}
if (vm.count("output"))
{
dsm_file_name = parameters.output + ".dsm";
distances_file_name = parameters.output + "distances.csv";
}
else
{
dsm_file_name = std::regex_replace(parameters.kmers, file_format_e, std::string("$1/$2.dsm"));
if (dsm_file_name == parameters.kmers) // If first match fails
{
dsm_file_name = std::regex_replace(parameters.kmers, file_format_within_e, std::string("$1.dsm"));
}
distances_file_name = std::regex_replace(parameters.kmers, file_format_e, std::string("$1/$2.distances.csv"));
if (distances_file_name == parameters.kmers) // If first match fails
{
distances_file_name = std::regex_replace(parameters.kmers, file_format_within_e, std::string("$1.distances.csv"));
}
}
// vector of subsampled kmers
std::vector dsm_kmers;
dsm_kmers.reserve(parameters.size);
long int kmer_index = 0;
while (kmer_file)
{
// Allow output of entire dsm line
std::string dsm_line;
std::getline(kmer_file, dsm_line);
std::stringstream dsm_stream(dsm_line);
Kmer k;
if (kmer_file)
{
dsm_stream >> k;
k.add_x(sample_map, samples.size());
// apply filters here
int passed_filters = 0;
if (parameters.filter && passFilters(parameters, k, samples, y, continuous_phenotype, 0))
{
passed_filters = 1;
filtered_file << dsm_line << "\n";
}
else if (!parameters.filter)
{
passed_filters = 1;
}
// kmer has passed basic filters, so is a candidate for mds
// subsampling
if (passed_filters)
{
// Resevoir sampler for parameters.size kmers
kmer_index++;
if (dsm_kmers.size() < dsm_kmers.capacity())
{
dsm_kmers.push_back(k.get_x());
}
else
{
std::uniform_int_distribution dist(0, kmer_index);
int r = dist(rand_gen);
if (r < parameters.size)
{
dsm_kmers[r] = k.get_x();
}
}
}
}
}
// Convert into arma::mat before MDS
arma::mat subsampledMatrix(samples.size(), dsm_kmers.size());
for (unsigned int i = 0; i < dsm_kmers.size(); ++i)
{
subsampledMatrix.col(i) = dsm_kmers[i];
}
// Write output
if (vm.count("no_mds"))
{
writeMDS(dsm_file_name, samples, subsampledMatrix);
}
else
{
// Run metric MDS, then output to file
if (parameters.write_distances)
{
writeMDS(dsm_file_name, samples, metricMDS(subsampledMatrix, parameters.pc, parameters.num_threads, distances_file_name));
}
else
{
writeMDS(dsm_file_name, samples, metricMDS(subsampledMatrix, parameters.pc, parameters.num_threads));
}
}
std::cerr << "Done.\n";
if (!vm.count("no_mds"))
{
if (parameters.filter)
{
std::cerr << "You may now want to run:\n\tseer -k " << output_file_name
<< " -p " << vm["pheno"].as() << " --struct " << dsm_file_name
<< " > significant_kmers.txt\n";
}
else
{
std::cerr << "You may now want to run:\n\tseer -k " << parameters.kmers
<< " -p " << vm["pheno"].as() << " --struct " << dsm_file_name
<< " > significant_kmers.txt\n";
}
}
}
// Matrices already subsampled. Concatenate and perform MDS
else
{
std::string matrix_input = vm["mds_concat"].as();
// Check number of principal coordinates ok
cmdOptions mdsOptions;
verifyMDSOptions(mdsOptions, vm);
std::string dsm_file_name, distances_file_name;
if (fileStat(matrix_input))
{
if (vm.count("output"))
{
dsm_file_name = vm["output"].as();
distances_file_name = vm["output"].as() + ".distances.csv";
}
else
{
dsm_file_name = matrix_input + ".dsm";
distances_file_name = matrix_input + ".distances.csv";
}
// Run metric MDS, then output to file
if (mdsOptions.write_distances)
{
writeMDS(dsm_file_name, samples, metricMDS(readMDSList(matrix_input), mdsOptions.pc, mdsOptions.num_threads, distances_file_name));
}
else
{
writeMDS(dsm_file_name, samples, metricMDS(readMDSList(matrix_input), mdsOptions.pc, mdsOptions.num_threads));
}
}
else
{
throw std::runtime_error("Couldn't open list of matrices " + matrix_input);
}
std::cerr << "Done.\n";
std::cerr << "Output written to " << dsm_file_name << "\n"
<< "Use this as the --struct option of seer\n";
}
}
seer-1.1.2/src/kmdsStruct.cpp 0000664 0000000 0000000 00000012064 12774457224 0016130 0 ustar 00root root 0000000 0000000 /*
* File: kmdsStruct.cpp
*
* Implements metric MDS (multi-dimensional scaling)
* for kmds kmers
*/
#include "kmds.hpp"
arma::mat metricMDS(const arma::mat& populationMatrix, const int dimensions, const unsigned int threads, const std::string& distances_file)
{
/*
* Metric MDS
*
* 1) P^2 -> matrix with elements which are distances squared
* 2) J = I - n^-1(II') - II' is a square matrix of ones
* 3) B = -0.5JP^2J
* 4) Decompose B into eigenvalues
* 5) MDS components = eigenvectors * eigenvalues
* 6) Normalise components
*/
const unsigned int matSize = populationMatrix.n_rows;
// Step 1)
arma::mat P = arma::square(dissimiliarityMatrix(populationMatrix, threads));
// If supplied as an optional parameter, write distance matrix to file
if (!distances_file.empty())
{
writeDistances(distances_file, P);
}
// Step 2)
arma::mat J = arma::eye(matSize, matSize)
- 1/double(matSize)*arma::ones(matSize, matSize);
// Step 3)
arma::mat B = -0.5 * J * P * J;
// Step 4)
arma::vec eigval;
arma::mat eigvec;
if (!arma::eig_sym(eigval, eigvec, B))
{
throw std::runtime_error("Could not calculate eigenvalues of B matrix in metric MDS");
}
// Step 5)
// Eigenvalues returned are sorted ascending, so want to reverse order
arma::mat mds = fliplr(eigvec * diagmat(sqrt(eigval)));
// Step 6)
// All values will lie in the interval [-1,1]
arma::mat norm_mds(matSize, dimensions);
for (int i = 0; i < dimensions; ++i)
{
if (pow(max(mds.col(i)), 2) > pow(min(mds.col(i)), 2))
{
norm_mds.col(i) = mds.col(i)/max(mds.col(i));
}
else
{
norm_mds.col(i) = mds.col(i)/fabs(min(mds.col(i)));
}
}
return norm_mds;
}
// Distance between all rows. 0/1 elements only
arma::mat dissimiliarityMatrix(const arma::mat& inMat, const unsigned int threads)
{
const unsigned int matSize = inMat.n_rows;
arma::mat dist = arma::zeros(matSize, matSize);
// Time parallelisation
std::chrono::steady_clock::time_point start = std::chrono::steady_clock::now();
// Create queue for distance calculations
std::queue>> distance_calculations;
// Set up number of jobs per thread
const unsigned long int num_calculations = 0.5*matSize*(matSize - 1);
const unsigned long int calc_per_thread = (unsigned long int)num_calculations / threads;
const unsigned int num_big_threads = num_calculations % threads;
// Keep track of row and column outside of outer loop
unsigned int row = 0;
unsigned int col = 0;
for (unsigned int thread_idx = 0; thread_idx < threads; ++thread_idx) // Loop over threads
{
// First 'big' threads have an extra job
unsigned long int thread_jobs;
if (distance_calculations.size() < num_big_threads)
{
thread_jobs = calc_per_thread + 1;
}
else
{
thread_jobs = calc_per_thread;
}
// Each thread is assigned elements to calculated scanning left to right,
// top to bottom on the upper triangle of the matrix
std::vector thread_elements;
thread_elements.reserve(thread_jobs);
for (unsigned int element = 0; element < calc_per_thread; ++element)
{
DistanceElement d;
d.row = row;
d.col = ++col;
thread_elements.push_back(d);
if (col + 1 == matSize)
{
++row;
col = row;
}
}
// Set the thread off
distance_calculations.push(std::async(std::launch::async, threadDistance, thread_elements, std::cref(inMat)));
}
// Wait for thread results to return, and put the results in the distance
// matrix
while(!distance_calculations.empty())
{
std::vector distance_elements = distance_calculations.front().get();
distance_calculations.pop();
for(std::vector::iterator it = distance_elements.begin() ; it < distance_elements.end(); ++it)
{
dist(it->row, it->col) = it->distance;
dist(it->col, it->row) = it->distance;
}
}
// Normalise by total k-mers
dist = dist / inMat.n_cols;
// Print time taken
std::chrono::steady_clock::time_point end = std::chrono::steady_clock::now();
std::chrono::duration diff = std::chrono::duration_cast>(end - start);
std::cerr << "Distance matrix calculated in: " << diff.count() << " s\n";
return dist;
}
std::vector threadDistance(std::vector element_list, const arma::mat& rectangular_matrix)
{
for(std::vector::iterator it = element_list.begin() ; it != element_list.end(); ++it)
{
it->distance = distanceFunction(rectangular_matrix.row(it->row), rectangular_matrix.row(it->col));
}
return element_list;
}
double distanceFunction(const arma::rowvec& vec_1, const arma::rowvec& vec_2)
{
// Slow
// return accu(abs(vec_1 - vec_2));
return dot(vec_1 - vec_2, vec_1 - vec_2);
}
seer-1.1.2/src/kmer.cpp 0000664 0000000 0000000 00000007406 12774457224 0014727 0 ustar 00root root 0000000 0000000 /*
* File: kmer.cpp
*
* Helper functions for the kmer class
*
*/
#include "kmer.hpp"
// Initialisation
Kmer::Kmer(const std::string& sequence, const std::vector& occurrences, const double pvalue, const double beta, const double se, const double maf)
: Significant_kmer(sequence, occurrences, maf, kmer_chi_pvalue_default, pvalue, beta, se, kmer_comment_default), _x_set(0), _use_firth(0)
{
}
// Initialise without calculated information
Kmer::Kmer(const std::string& sequence, const std::vector& occurrences)
: Significant_kmer(sequence, occurrences, kmer_maf_default, kmer_chi_pvalue_default, kmer_pvalue_default, kmer_beta_default, kmer_se_default, kmer_comment_default), _x_set(0), _use_firth(0)
{
}
// Initialise with default info only
Kmer::Kmer()
: Significant_kmer(kmer_seq_default, kmer_occ_default, kmer_maf_default, kmer_chi_pvalue_default, kmer_pvalue_default, kmer_beta_default, kmer_se_default, kmer_comment_default), _x_set(0), _use_firth(0)
{
}
// Output
std::ostream& operator<<(std::ostream &os, const Kmer& k)
{
os << std::fixed << std::setprecision(3) << k.sequence() << "\t" << k.maf()
<< "\t" << std::scientific << k.unadj() << "\t" << k.p_val() << "\t" << k.beta()
<< "\t" << k.se();
std::vector covariates = k.covar_p();
for (auto it = covariates.begin(); it != covariates.end(); ++it)
{
os << "\t" << *it;
}
os << "\t" << k.comments();
return os;
}
// Input
std::istream& operator>>(std::istream &is, Kmer& k)
{
std::vector samples;
std::string word, s_buffer, kmer_line;
char c_buffer;
/*
* Example dsm file line AAAAAAAAAAAAAAAAAATGCATATTTATCTTAG 5.172314 0.175087 100 0 100
* 0.164875 100 | 6925_3#7:9 6823_4#17:26 6871_2#9:8
*
* OR
*
* AAAAAAAAAAAAAAAAAATGCATATTTATCTTAG 5.172314 6925_3#7:9 6823_4#17:26 6871_2#9:8
*
*/
if (is)
{
std::getline(is, kmer_line);
std::stringstream line(kmer_line);
// First field is the kmer
line >> word;
// Skip entropy fields
while (line >> s_buffer)
{
if (s_buffer == "|")
{
break;
}
// Gone one too far in a string w/out | separator
else
{
size_t occurence_pos = s_buffer.find(":");
if (s_buffer.find(":") != std::string::npos)
{
samples.push_back(s_buffer.substr(0, occurence_pos));
break;
}
}
}
s_buffer = "";
while (line >> c_buffer)
{
// Found end of sample. Read forward to next one
if (c_buffer == ':')
{
samples.push_back(s_buffer);
line >> s_buffer;
s_buffer = "";
}
else
{
s_buffer += c_buffer;
}
}
}
k = Kmer(word, samples);
return is;
}
// Set the x and maf of the kmer
void Kmer::add_x(const std::unordered_map& sample_map, const int num_samples)
{
_x.zeros(num_samples);
for (auto it = _samples.begin(); it != _samples.end(); ++it)
{
auto sample_index_it = sample_map.find(*it);
if (sample_index_it != sample_map.end())
{
_x[sample_index_it->second] = 1;
}
}
// Set object values
_x_set = 1;
_maf = (double)num_occurrences()/num_samples;
}
// Add a new comment in
void Kmer::add_comment(const std::string& new_comment)
{
if (_comment == kmer_comment_default)
{
_comment = new_comment;
}
else
{
_comment += "," + new_comment;
}
}
int Kmer::num_occurrences() const
{
int total_occurrences;
if (_x_set)
{
total_occurrences = (int) accu(_x);
}
else
{
total_occurrences = _samples.size();
}
return total_occurrences;
}
seer-1.1.2/src/kmer.hpp 0000664 0000000 0000000 00000003431 12774457224 0014726 0 ustar 00root root 0000000 0000000 /*
* kmer.hpp
* Header file for kmer class
*/
#include
#define ARMA_DONT_PRINT_ERRORS
#include
#include "significant_kmer.hpp"
const std::string kmer_seq_default = "";
const std::vector kmer_occ_default;
const double kmer_pvalue_default = 1;
const double kmer_chi_pvalue_default = 1;
const double kmer_beta_default = 0;
const double kmer_maf_default = 0;
const double kmer_se_default = 0;
const std::string kmer_comment_default = "NA";
class Kmer: public Significant_kmer
{
public:
// Initialisation
Kmer(const std::string& sequence, const std::vector& occurrences, const double pvalue, const double beta, const double se, const double maf);
Kmer(const std::string& sequence, const std::vector& occurrences); // Initialise without calculated information
Kmer(); // defaults
// nonmodifying operations
int length() const { return _word.length(); }
int num_occurrences() const;
std::string occurrence(int i) const { return _samples[i]; }
std::vector occurrence_vector() const { return _samples; }
arma::vec get_x() const { return _x; }
int has_x() const { return _x_set; }
int firth() const { return _use_firth; }
// Modifying operations
void add_comment(const std::string& new_comment); // this is defined in kmer.cpp
void add_x(const std::unordered_map& sample_map, const int num_samples); // this is defined in kmer.cpp
void firth(const int use_firth) { _use_firth = use_firth; }
private:
arma::vec _x;
int _x_set;
int _use_firth;
};
// Overload output and input operators
std::ostream& operator<<(std::ostream &os, const Kmer& k);
std::istream& operator>>(std::istream &is, Kmer& k);
seer-1.1.2/src/linearFunction.cpp 0000664 0000000 0000000 00000002062 12774457224 0016742 0 ustar 00root root 0000000 0000000 /*
* File: linearFunction.cpp
*
* Likelihood, gradient and initialisation of linear function
* Functor, based on mlpack: www.mlpack.org
*/
#include "linkFunction.hpp"
/**
* Evaluate the logistic regression objective function given the estimated
* parameters.
*/
double LinearLikelihood::operator()(const column_vector& parameters_in)
const
{
// Convert from dlib column matrix to armadillo column matrix
arma::vec parameters = dlib_to_arma(parameters_in);
// L(b) = 1/2*||y-Xb||^2
double result = 0.5 * accu(square(responses - predictors * parameters));
return result;
}
// Evaluate the gradient of the logistic regression objective function.
column_vector LinearLikelihoodGradient::operator()(const column_vector& parameters_in)
const
{
// Convert from dlib column matrix to armadillo column matrix
arma::vec parameters = dlib_to_arma(parameters_in);
arma::vec gradient(parameters.n_elem);
// dL(b)/db = X.t()(Xb - y)
gradient = predictors.t() * (predictors * parameters - responses);
return arma_to_dlib(gradient);
}
seer-1.1.2/src/linkFunction.hpp 0000664 0000000 0000000 00000003607 12774457224 0016440 0 ustar 00root root 0000000 0000000 /*
* linkFunction.hpp
* Header file for logitFunction and linearFunction class
*/
#include "seercommon.hpp"
class LinkFunction
{
public:
// Initialisation
LinkFunction(const arma::mat& _predictors, const arma::vec& _responses, const double _lambda = 0)
: predictors(_predictors), responses(_responses), lambda(_lambda)
{
}
// Likelihood and first derivative
double likelihood(const column_vector& b)
const;
column_vector gradient(const column_vector& b)
const;
protected:
arma::mat predictors;
arma::vec responses;
double lambda;
};
class LogitLikelihood : public LinkFunction
{
public:
LogitLikelihood(const arma::mat& _predictors, const arma::vec& _responses, const double _lambda = 0)
: LinkFunction(_predictors, _responses, _lambda)
{
}
double operator() (const column_vector& parameters_in) const;
};
class LogitLikelihoodGradient : public LinkFunction
{
public:
LogitLikelihoodGradient(const arma::mat& _predictors, const arma::vec& _responses, const double _lambda = 0)
: LinkFunction(_predictors, _responses, _lambda)
{
}
column_vector operator() (const column_vector& parameters_in) const;
};
class LinearLikelihood : public LinkFunction
{
public:
LinearLikelihood(const arma::mat& _predictors, const arma::vec& _responses, const double _lambda = 0)
: LinkFunction(_predictors, _responses, _lambda)
{
}
double operator() (const column_vector& parameters_in) const;
};
class LinearLikelihoodGradient : public LinkFunction
{
public:
LinearLikelihoodGradient(const arma::mat& _predictors, const arma::vec& _responses, const double _lambda = 0)
: LinkFunction(_predictors, _responses, _lambda)
{
}
column_vector operator() (const column_vector& parameters_in) const;
};
seer-1.1.2/src/logitFunction.cpp 0000664 0000000 0000000 00000005412 12774457224 0016610 0 ustar 00root root 0000000 0000000 /*
* File: logitFunction.cpp
*
* Likelihood, gradient and initialisation of logit function
* Functor, based on mlpack: www.mlpack.org
*/
#include "linkFunction.hpp"
/**
* Evaluate the logistic regression objective function given the estimated
* parameters.
*/
double LogitLikelihood::operator()(const column_vector& parameters_in)
const
{
// Convert from dlib column matrix to armadillo column matrix
arma::vec parameters = dlib_to_arma(parameters_in);
// The objective function is the log-likelihood function (w is the parameters
// vector for the model; y is the responses; x is the predictors; sig() is the
// sigmoid function):
// f(w) = sum(y log(sig(w'x)) + (1 - y) log(sig(1 - w'x))).
// We want to minimize this function. L2-regularization is just lambda
// multiplied by the squared l2-norm of the parameters then divided by two.
// For the regularization, we ignore the first term, which is the intercept
// term.
const double regularization = 0.5 * lambda *
arma::dot(parameters.col(0).subvec(1, parameters.n_elem - 1),
parameters.col(0).subvec(1, parameters.n_elem - 1));
// Calculate vectors of sigmoids. The intercept term is parameters(0, 0) and
// does not need to be multiplied by any of the predictors.
const arma::vec exponents = parameters(0) + predictors *
parameters.col(0).subvec(1, parameters.n_elem - 1);
const arma::vec sigmoid = 1.0 / (1.0 + arma::exp(-exponents));
// Assemble full objective function. Often the objective function and the
// regularization as given are divided by the number of features, but this
// doesn't actually affect the optimization result, so we'll just ignore those
// terms for computational efficiency.
double result = 0.0;
for (size_t i = 0; i < responses.n_elem; ++i)
{
if (responses[i] == 1)
result += log(sigmoid[i]);
else
result += log(1.0 - sigmoid[i]);
}
return result - regularization;
}
// Evaluate the gradient of the logistic regression objective function.
column_vector LogitLikelihoodGradient::operator()(const column_vector& parameters_in)
const
{
// Convert from dlib column matrix to armadillo column matrix
arma::vec parameters = dlib_to_arma(parameters_in);
arma::vec gradient(parameters.n_elem);
// Regularization term.
arma::mat regularization;
regularization = lambda * parameters.col(0).subvec(1, parameters.n_elem - 1);
const arma::vec sigmoids = 1 / (1 + arma::exp(-parameters(0, 0)
- predictors * parameters.col(0).subvec(1, parameters.n_elem - 1)));
gradient[0] = arma::accu(responses - sigmoids);
gradient.col(0).subvec(1, parameters.n_elem - 1) = predictors.t() * (responses -
sigmoids) - regularization;
return arma_to_dlib(gradient);
}
seer-1.1.2/src/mapCmdLine.cpp 0000664 0000000 0000000 00000003526 12774457224 0016001 0 ustar 00root root 0000000 0000000 /*
* File: mapCmdLine.cpp
*
* Reads command line input to map_back using boost
* program options
*
*/
#include "map_back.hpp"
namespace po = boost::program_options; // Save some typing
// Use boost::program_options to parse command line input
// This does pretty much all the parameter checking needed
int parseCommandLine (int argc, char *argv[], po::variables_map& vm)
{
int failed = 0;
//Required options
po::options_description required("Required options");
required.add_options()
("kmers,k", po::value()->required(), "seer kmer output file")
("references,r", po::value()->required(), "file with tab separated reference name and fasta file");
po::options_description other("Other options");
other.add_options()
("threads", po::value()->default_value(1), ("number of threads. Suggested: " + std::to_string(std::thread::hardware_concurrency())).c_str())
("version", "prints version and exits")
("help,h", "full help message");
po::options_description all;
all.add(required).add(other);
try
{
po::store(po::command_line_parser(argc, argv).options(all).run(), vm);
if (vm.count("help"))
{
printHelp(all);
failed = 1;
}
else if (vm.count("version"))
{
std::cout << VERSION << std::endl;
failed = 1;
}
else
{
po::notify(vm);
failed = 0;
}
}
catch (po::error& e)
{
// Report errors from boost library
std::cerr << "Error in command line input: " << e.what() << "\n";
std::cerr << "Run 'map_back --help' for full option listing\n\n";
std::cerr << required << "\n" << other << "\n";
failed = 1;
}
return failed;
}
// Print long help message
void printHelp(po::options_description& help)
{
std::cerr << help << "\n";
}
seer-1.1.2/src/mapMain.cpp 0000664 0000000 0000000 00000012467 12774457224 0015356 0 ustar 00root root 0000000 0000000 /*
* File: mapMain.cpp
*
* Reports exact matches of kmers to assemblies
*
*/
#include "map_back.hpp"
int main (int argc, char *argv[])
{
// Program description
std::cerr << "map_back: context of significant kmers\n";
// Do parsing and checking of command line params
// If no input options, give quick usage rather than full help
boost::program_options::variables_map vm;
if (argc == 1)
{
std::cerr << "Usage: map_back -k seer_output.txt -r references.txt > mappings.txt\n\n"
<< "For full option details run map_back -h\n";
return 0;
}
else if (parseCommandLine(argc, argv, vm))
{
return 1;
}
// Set number of threads
size_t num_threads = vm["threads"].as();
if (num_threads < 1)
{
num_threads = 1;
}
// Read all sequences into memory as Fasta objects
std::cerr << "Reading reference sequences into memory...\n";
std::vector sequence_cache = readSequences(vm["references"].as());
// Loop through significant kmers
std::cerr << "Now mapping significant kmers...\n";
std::ifstream kmer_file(vm["kmers"].as().c_str());
if (!kmer_file)
{
throw std::runtime_error("Could not open kmer_file " + vm["kmers"].as() + "\n");
}
else
{
// Set up list of threads, and mutex lock on std out
std::mutex out_mtx;
std::list> threads;
// Read the header
std::string header;
std::getline(kmer_file, header);
int num_covar_fields = parseHeader(header);
Significant_kmer sig_kmer(num_covar_fields);
while(kmer_file)
{
kmer_file >> sig_kmer;
// Check the read into sig_kmer hasn't reached end of file
if (!kmer_file.eof())
{
assert(num_threads >= 1);
std::cout << sig_kmer.sequence();
// sig_kmer samples and sample cache are sorted in the same order, so
// can go through linearly
std::vector search_names = sig_kmer.samples_found();
std::vector::iterator search_names_it = search_names.begin();
for (std::vector::iterator all_names_it = sequence_cache.begin(); all_names_it != sequence_cache.end(); ++all_names_it)
{
// For each sample we know the kmer is in, print all matches to
// the kmer
if (all_names_it->get_name() == *search_names_it)
{
// Thread each search (i.e. per sample per kmer)
if (num_threads > 1)
{
// Check if four searches are running. If so, wait for the
// next one to finish. Wait (for a max of 100ms) so this
// thread doesn't consume processing
waitForThreads(threads, num_threads - 1);
// Start a new thread asynchronously, at the back of the
// queue
threads.push_back(std::async(std::launch::async,
&Fasta::printMappings, *all_names_it, std::ref(std::cout), sig_kmer.sequence(), std::ref(out_mtx)));
// Search for reverse complement too
waitForThreads(threads, num_threads - 1);
threads.push_back(std::async(std::launch::async,
&Fasta::printMappings, *all_names_it, std::ref(std::cout), sig_kmer.rev_comp(), std::ref(out_mtx)));
}
else
{
all_names_it->printMappings(std::cout, sig_kmer.sequence(), out_mtx);
all_names_it->printMappings(std::cout, sig_kmer.rev_comp(), out_mtx);
}
++search_names_it;
if (search_names_it == search_names.end())
{
break;
}
}
}
// Tab between every sample, line break after every kmer
if (num_threads > 1)
{
waitForThreads(threads, 0);
}
std::cout << std::endl;
}
}
std::cerr << "Done.\n";
}
}
// Stores all fasta sequences in a vector of Fasta objects
std::vector readSequences(const std::string& reference_file)
{
std::vector sequences;
std::ifstream ifs(reference_file.c_str());
if (!ifs)
{
throw std::runtime_error("Could not open reference file: " + reference_file + "\n");
}
else
{
std::string name, fasta_file = "";
while(ifs)
{
ifs >> name >> fasta_file;
sequences.push_back(Fasta(name, fasta_file));
}
}
std::sort(sequences.begin(), sequences.end(), Fasta::compareFasta);
return sequences;
}
// Waits until there are #spaces threads left unfinished
void waitForThreads(std::list>& thread_list, const size_t leave_running)
{
auto list_it = thread_list.begin();
while (thread_list.size() > leave_running)
{
auto status = list_it->wait_for(std::chrono::milliseconds(thread_wait));
if (status == std::future_status::ready)
{
list_it = thread_list.erase(list_it);
}
else if (list_it++ == thread_list.end())
{
list_it = thread_list.begin();
}
}
}
seer-1.1.2/src/map_back.hpp 0000664 0000000 0000000 00000001665 12774457224 0015534 0 ustar 00root root 0000000 0000000 /*
*
* map_back.hpp
* Header file for map_back
*
*/
// C/C++/C++11 headers
#include
#include
#include
#include
#include
#include
#include
#include
#include
#include
#include
#include
#include
// Boost headers
#include
// Classes
#include "fasta.hpp"
#include "significant_kmer.hpp"
// Constants
const std::string VERSION = "1.2";
const int thread_wait = 100; //time to wait for each thread in ms
// Structs
// Function headers
// mapMain.cpp
std::vector readSequences(const std::string& reference_file);
void waitForThreads(std::list>& thread_list, const size_t leave_running);
// mapCmdLine.cpp
int parseCommandLine (int argc, char *argv[], boost::program_options::variables_map& vm);
void printHelp(boost::program_options::options_description& help);
seer-1.1.2/src/sample.cpp 0000664 0000000 0000000 00000001274 12774457224 0015247 0 ustar 00root root 0000000 0000000 /*
* File: sample.cpp
*
* Helper functions for the sample class
*
*/
#include "sample.hpp"
const double default_pheno = 0;
const std::string default_name = "";
const int default_continuous = 0;
Sample::Sample(double p, std::string n)
:phenotype(p), name(n)
{
if (p != 0 && p != 1)
{
continuous_pheno = 1;
}
else
{
continuous_pheno = 0;
}
}
Sample::Sample()
:phenotype(default_pheno), continuous_pheno(default_continuous), name(default_name)
{
}
std::istream& operator>>(std::istream &is, Sample& s)
{
double phenotype;
std::string FID, IID;
is >> FID >> IID >> phenotype;
if (is)
{
s = Sample(phenotype, IID);
}
return is;
}
seer-1.1.2/src/sample.hpp 0000664 0000000 0000000 00000001336 12774457224 0015253 0 ustar 00root root 0000000 0000000 /*
* sample.hpp
* Header file for sample class
*/
#include
#include
// Samples have a name and phenotype
// Covariates will be added later
class Sample
{
public:
// Initialisation
Sample(double phenotype, std::string name);
Sample();
// nonmodifying operations
double pheno() const { return phenotype; }
int continuous() const { return continuous_pheno; }
std::string iid() const { return name; }
static bool compareSamples(Sample lhs, Sample rhs) { return (lhs.name < rhs.name); }
private:
double phenotype;
int continuous_pheno;
std::string name;
};
// Overload input operator
std::istream& operator>>(std::istream &is, Sample& s);
seer-1.1.2/src/seer.hpp 0000664 0000000 0000000 00000002740 12774457224 0014730 0 ustar 00root root 0000000 0000000 /*
*
* seer.hpp
* Header file for seer package
*
*/
// Common headers
//#include "seercommon.hpp"
#include "linkFunction.hpp" // This includes seercommon.hpp
// dlib headers
#include
// Constants
// Default options
const std::string pval_default = "10e-8";
const double convergence_limit = 10e-8;
const unsigned int max_nr_iterations = 1000;
const double se_limit = 3;
// Starting value for beta vectors (except intercept)
// Should be >0. This value is based on RMS in example study
const double bfgs_start_beta = 1;
// seerCmdLine headers
int parseCommandLine (int argc, char *argv[], boost::program_options::variables_map& vm);
void printHelp(boost::program_options::options_description& help);
// seerBinaryAssoc headers
void logisticTest(Kmer& k, const arma::vec& y);
void logisticTest(Kmer& k, const arma::vec& y_train, const arma::mat& mds);
void doLogit(Kmer& k, const arma::vec& y_train, const arma::mat& x_train);
void newtonRaphson(Kmer& k, const arma::vec& y_train, const arma::mat& x_design, const bool firth = 0);
arma::mat varCovarMat(const arma::mat& x, const arma::mat& b);
arma::vec predictLogitProbs(const arma::mat& x, const arma::vec& b);
// seerContinuousAssoc headers
void linearTest(Kmer& k, const arma::vec& y_train);
void linearTest(Kmer& k, const arma::vec& y_train, const arma::mat& mds);
void doLinear(Kmer& k, const arma::vec& y_train, const arma::mat& x_train);
arma::vec predictLinearProbs(const arma::mat& x, const arma::vec& b);
seer-1.1.2/src/seerBinaryAssoc.cpp 0000664 0000000 0000000 00000017644 12774457224 0017072 0 ustar 00root root 0000000 0000000 /*
* File: seerBinaryAssoc.cpp
*
* Implements logistic regression association tests for seer
*
*/
#include "seer.hpp"
// Logistic fit without covariates
void logisticTest(Kmer& k, const arma::vec& y_train)
{
// Train classifier
arma::mat x_train = k.get_x();
doLogit(k, y_train, x_train);
}
// Logistic fit with covariates
void logisticTest(Kmer& k, const arma::vec& y_train, const arma::mat& mds)
{
// Train classifier
arma::mat x_train = arma::join_rows(k.get_x(), mds);
doLogit(k, y_train, x_train);
}
// This uses BFGS optimisation by default. Invokes NR or Firth on error
void doLogit(Kmer& k, const arma::vec& y_train, const arma::mat& x_train)
{
arma::mat x_design = join_rows(arma::mat(x_train.n_rows,1,arma::fill::ones), x_train);
column_vector starting_point(x_design.n_cols);
if (k.firth())
{
newtonRaphson(k, y_train, x_design, 1);
}
else
{
starting_point(0) = log(mean(y_train)/(1 - mean(y_train)));
for (size_t i = 1; i < x_design.n_cols; ++i)
{
starting_point(i) = bfgs_start_beta;
}
try
{
// Use BFGS optimiser in dlib to maximise likelihood function by chaging the
// b vector, which will end in starting_point
dlib::find_max(dlib::bfgs_search_strategy(),
dlib::objective_delta_stop_strategy(convergence_limit),
LogitLikelihood(x_train, y_train), LogitLikelihoodGradient(x_train, y_train),
starting_point, -1);
// Extract beta
arma::vec b_vector = dlib_to_arma(starting_point);
k.beta(b_vector(1));
// Extract p-value
//
//
// W = B_1 / SE(B_1) ~ N(0,1)
//
// In the special case of a logistic regression, abs can be taken rather
// than ^2 as responses are 0 or 1
//
arma::mat var_covar_mat = varCovarMat(x_design, b_vector);
double se = pow(var_covar_mat(1,1), 0.5);
// Zeros will result in bad regression with large SE - firth regression helps
if (se > se_limit)
{
throw std::runtime_error("se>limit");
}
else
{
k.standard_error(se);
double W = std::abs(b_vector(1)) / se; // null hypothesis b_1 = 0
k.p_val(normalPval(W));
#ifdef SEER_DEBUG
std::cerr << "Wald statistic: " << W << "\n";
std::cerr << "p-value: " << k.p_val() << "\n";
#endif
// Add in covariate p-values
for (unsigned int i = 2; i < var_covar_mat.n_rows; ++i)
{
se = pow(var_covar_mat(i,i), 0.5);
W = std::abs(b_vector(i)) / se;
k.add_covar_p(normalPval(W));
}
}
}
// Sometimes won't converge, use N-R instead
catch (std::exception& e)
{
#ifdef SEER_DEBUG
std::cerr << "Caught error " << e.what() << std::endl;
#endif
// SE is greater than specified limit - run Firth regression
if (strcmp(e.what(), "se>limit") == 0)
{
k.add_comment("large-se");
newtonRaphson(k, y_train, x_design, 1);
}
// BFGS optimiser did not converge - use NR iterations w/o Firth first
// Could also be matrix inversion failing
else
{
k.add_comment("bfgs-fail");
newtonRaphson(k, y_train, x_design);
}
}
}
}
void newtonRaphson(Kmer& k, const arma::vec& y_train, const arma::mat& x_design, const bool firth)
{
// Keep iterations to track convergence
// Also useful to keep second derivative, for calculating p-value
std::vector parameter_iterations;
arma::mat var_covar_mat;
int failed = 0;
// Could get starting point from a linear regression, which is fast
// and will reduce number of n-r iterations
// Set up design matrix, and calculate (X'X)^-1
// Seems more reliable to go for b = 0, plus a non-zero intercept
// See: doi:10.1016/S0169-2607(02)00088-3
arma::vec starting_point = arma::zeros(x_design.n_cols);
starting_point(0) = log(mean(y_train)/(1 - mean(y_train)));
parameter_iterations.push_back(starting_point);
for (unsigned int i = 0; i < max_nr_iterations; ++i)
{
arma::vec b0 = parameter_iterations.back();
arma::vec y_pred = predictLogitProbs(x_design, b0);
arma::mat U(x_design.n_cols, 1);
arma::mat W = repmat(y_pred % (arma::ones(y_pred.n_rows) - y_pred), 1, x_design.n_cols);
// Perform inversion, which may fail
var_covar_mat = inv_covar(x_design.t() * (W % x_design));
if (var_covar_mat.n_cols == 0 || var_covar_mat.n_rows == 0)
{
k.add_comment("inv-fail");
k.p_val(0);
std::cerr << "Inversion at input line " << k.line_number() << " failed" << std::endl;
failed = 1;
break;
}
if (firth)
{
// Firth logistic regression
// See: DOI: 10.1002/sim.1047
// Hat matrix
// Note: W is diagonal so X.t() * W * X is still sympd
arma::mat H = (sqrt(W) % x_design) * var_covar_mat * (x_design.t() % sqrt(W).t());
arma::vec correction(y_train.n_rows);
correction.fill(0.5);
// Penalised score
U = x_design.t() * (y_train - y_pred + diagvec(H) % (correction - y_pred));
}
else
{
U = x_design.t() * (y_train - y_pred);
}
arma::vec b1 = b0 + var_covar_mat * U;
parameter_iterations.push_back(b1);
if (std::abs(b1(1) - b0(1)) < convergence_limit)
{
break;
}
}
#ifdef SEER_DEBUG
std::cerr << "Number of iterations: " << parameter_iterations.size() - 1 << "\n";
#endif
// If convergence not reached, try Firth logistic regression
if (parameter_iterations.size() == max_nr_iterations)
{
if (!firth)
{
k.add_comment("nr-fail");
newtonRaphson(k, y_train, x_design, 1);
}
else
{
k.add_comment("firth-fail");
}
}
else if (!failed)
{
k.beta(parameter_iterations.back()(1));
double se = pow(var_covar_mat(1,1), 0.5);
k.standard_error(se);
// Deal with large SEs
if (se > se_limit)
{
if (!firth)
{
newtonRaphson(k, y_train, x_design, 1);
}
else
{
k.add_comment("large-se");
}
}
double W = std::abs(k.beta()) / se;
k.p_val(normalPval(W));
#ifdef SEER_DEBUG
std::cerr << "Wald statistic: " << W << "\n";
std::cerr << "p-value: " << k.p_val() << "\n";
#endif
// Add in covariate p-values
for (unsigned int i = 2; i< var_covar_mat.n_rows; ++i)
{
se = pow(var_covar_mat(i,i), 0.5);
W = std::abs(parameter_iterations.back()(i)) / se;
k.add_covar_p(normalPval(W));
}
}
}
// Returns var-covar matrix for logistic function
arma::mat varCovarMat(const arma::mat& x, const arma::mat& b)
{
// var-covar matrix = inv(I)
// where I is the Fisher information matrix
// I = d^2/d(b^2)[log L]
//
// see http://czep.net/stat/mlelr.pdf
// First get logit of x values using parameters from fit, and transform to
// p(1-p)
arma::vec y_pred = predictLogitProbs(x, b);
arma::vec y_trans = y_pred % (1 - y_pred);
// Fill elements of I, which are sums of element by element vector multiples
arma::mat I(b.n_elem, b.n_elem);
unsigned int j_max = I.n_rows;
for (unsigned int i = 0; i table = {a, b, c, d};
#ifdef SEER_DEBUG
arma::Mat::fixed<2, 2> tab_out = {int (a), int (b), int (c), int(d)};
std::cerr << tab_out << "\n";
#endif
int N = accu(table);
if (N == 0)
{
throw std::logic_error("Empty table for chisq test\n");
}
// Treat as invalid if any entry is 0 or 1, or if more than one entry < 5
// Mark as needing to use Firth regression
int low_obs = 0;
for (auto obs = table.begin(); obs != table.end(); ++obs)
{
if (*obs <= 1 || (*obs <= 5 && ++low_obs > 2))
{
k.add_comment("bad-chisq");
k.firth(1);
break;
}
}
// Without Yates' continuity correction
chisq = N * pow(det(table), 2);
for (int i = 0; i < 2; ++i)
{
chisq /= accu(table.row(i)) * accu(table.col(i));
}
// For df = 1, as here, chi^2 == N(0,1)^2 (standard normal dist.)
double p_value = normalPval(pow(chisq, 0.5));
#ifdef SEER_DEBUG
std::cerr << "chisq:" << chisq << "\n";
std::cerr << "chisq p: " << p_value << "\n";
#endif
return p_value;
}
// Welch two sample t-test, for continuous phenotypes
double welchTwoSamplet(const Kmer& k, const arma::vec& y)
{
arma::mat x = k.get_x();
// Subset into present and absent groups
arma::vec group1 = y.elem(find(x==0));
arma::vec group2 = y.elem(find(x==1));
// Calculate group means and variances
double p_val = 0;
if (group1.n_elem != 0 && group2.n_elem != 0)
{
double x1 = mean(group1);
double x2 = mean(group2);
double v1 = var(group1);
double v2 = var(group2);
// t and degrees freedom for test
double t = (x1 - x2)*pow((v1/group1.n_rows + v2/group2.n_rows), -0.5);
double df = pow((v1/group1.n_rows + v2/group2.n_rows), 2) / (pow(v1/group1.n_rows,2)/(group1.n_rows-1) + pow(v2/group2.n_rows,2)/(group2.n_rows-1));
// Calculate p-value from t distribution
boost::math::students_t t_dist(df);
p_val = 2 * (1 - boost::math::cdf(t_dist, t));
#ifdef SEER_DEBUG
std::cerr << "welch t:" << t << "df:" << df << "\n";
std::cerr << "welch p-val:" << p_val << "\n";
#endif
}
return p_val;
}
// Returns p-value for a test statistic that is >0 and standard normally distributed
double normalPval(double testStatistic)
{
double p_val = 0;
if (testStatistic < 5)
{
boost::math::normal s;
p_val = 2 * (1 - boost::math::cdf(s, testStatistic));
}
else
{
// For large z need to use a bound
// See http://stats.stackexchange.com/questions/13690/how-to-compute-the-probability-associated-with-absurdly-large-z-scores
//
// Upper bound
// S(z) <= phi(z)/z
// cdf = 1-(0.5 * S(z))
// At z = 5 correct to +/- 2.5%
#ifdef SEER_DEBUG
std::cerr << "using erfc bound rather than 'exact' function\n";
#endif
p_val = 2 * exp(-0.5*pow(testStatistic,2))*normalArea/testStatistic;
}
return p_val;
}
seer-1.1.2/src/seerCmdLine.cpp 0000664 0000000 0000000 00000007130 12774457224 0016155 0 ustar 00root root 0000000 0000000 /*
* File: seerCmdLine.cpp
*
* Reads command line input to seer using boost
* program options
*
*/
#include "seer.hpp"
namespace po = boost::program_options; // Save some typing
// Use boost::program_options to parse command line input
// This does pretty much all the parameter checking needed
int parseCommandLine (int argc, char *argv[], po::variables_map& vm)
{
int failed = 0;
//Required options
po::options_description required("Required options");
required.add_options()
("kmers,k", po::value()->required(), "dsm kmer output file")
("pheno,p", po::value()->required(), ".pheno metadata");
// kmds options
po::options_description covar("Covariate options");
covar.add_options()
("struct", po::value(), "mds values from kmds")
("covar_file", po::value(), "file containing covariates")
("covar_list", po::value(), "list of columns covariates to use. Format is 1,2q,3 (use q for quantitative)");
//Optional filtering parameters
//NB pval cutoffs are strings for display, and are converted to floats later
po::options_description performance("Performance options");
performance.add_options()
("threads", po::value()->default_value(1), ("number of threads. Suggested: " + std::to_string(std::thread::hardware_concurrency())).c_str());
//Optional filtering parameters
//NB pval cutoffs are strings for display, and are converted to floats later
po::options_description filtering("Filtering options");
filtering.add_options()
("no_filtering", "turn off all filtering and perform tests on all kmers input")
("max_length", po::value()->default_value(max_length_default), "maximum kmer length")
("maf", po::value()->default_value(maf_default), "minimum kmer frequency")
("min_words", po::value(), "minimum kmer occurences. Overrides --maf")
("chisq", po::value()->default_value(chisq_default), "p-value threshold for initial chi squared test. Set to 1 to show all")
("pval", po::value()->default_value(pval_default), "p-value threshold for final logistic test. Set to 1 to show all");
po::options_description other("Other options");
other.add_options()
("print_samples", "print lists of samples significant kmers were found in")
("version", "prints version and exits")
("help,h", "full help message");
po::options_description all;
all.add(required).add(covar).add(performance).add(filtering).add(other);
try
{
po::store(po::command_line_parser(argc, argv).options(all).run(), vm);
if (vm.count("help"))
{
printHelp(all);
failed = 1;
}
else if (vm.count("version"))
{
std::cout << VERSION << std::endl;
failed = 1;
}
else
{
po::notify(vm);
failed = 0;
// Check input files exist, and can stat
if (!fileStat(vm["kmers"].as()) || !fileStat(vm["pheno"].as()))
{
failed = 1;
}
else if (vm.count("struct") && !fileStat(vm["struct"].as()))
{
failed = 1;
}
}
}
catch (po::error& e)
{
// Report errors from boost library
std::cerr << "Error in command line input: " << e.what() << "\n";
std::cerr << "Run 'seer --help' for full option listing\n\n";
std::cerr << required << "\n" << other << "\n";
failed = 1;
}
return failed;
}
// Print long help message
void printHelp(po::options_description& help)
{
std::cerr << help << "\n";
}
seer-1.1.2/src/seerCommon.cpp 0000664 0000000 0000000 00000012465 12774457224 0016101 0 ustar 00root root 0000000 0000000 /*
* File: seerCommon.cpp
*
* Functions common to seer and kmds
* Program options parsing
*
*/
#include "seercommon.hpp"
// Parse command line parameters into usable program parameters
cmdOptions verifyCommandLine(boost::program_options::variables_map& vm, const std::vector& samples)
{
cmdOptions verified;
verified.max_length = vm["max_length"].as();
if(vm.count("kmers"))
{
verified.kmers = vm["kmers"].as();
}
if(vm.count("output"))
{
verified.output = vm["output"].as();
}
if(vm.count("chisq"))
{
verified.chi_cutoff = stod(vm["chisq"].as());
}
if (vm.count("pval"))
{
verified.log_cutoff = stod(vm["pval"].as());
}
// Verify MDS options in a separate function
// This is pc, size and number of threads
verifyMDSOptions(verified, vm);
verified.filter = 1;
if (vm.count("no_filtering"))
{
verified.filter = 0;
}
else
{
// Error check filtering options
verified.min_words = 0;
if (vm.count("min_words"))
{
int min_words_in = vm["min_words"].as();
if (min_words_in >= 0)
{
verified.min_words = min_words_in;
}
else
{
badCommand("min_words", std::to_string(min_words_in));
}
}
else
{
double maf_in = vm["maf"].as();
if (maf_in >= 0)
{
verified.min_words = static_cast(samples.size() * maf_in);
}
else
{
badCommand("maf", std::to_string(maf_in));
}
}
if (verified.min_words > samples.size())
{
badCommand("min_words/maf", std::to_string(verified.min_words));
}
verified.max_words = samples.size() - verified.min_words;
}
verified.print_samples = 0;
if (vm.count("print_samples"))
{
verified.print_samples = 1;
}
return verified;
}
// Check these options in a separate function, which is also usable by kmds
// in mds_concat mode
void verifyMDSOptions(cmdOptions& verified, boost::program_options::variables_map& vm)
{
// Number of threads is also needed by both
if (vm.count("threads") && vm["threads"].as() > 0)
{
verified.num_threads = vm["threads"].as();
}
else
{
verified.num_threads = 1;
}
if (vm.count("pc"))
{
if (vm["pc"].as() > 0)
{
verified.pc = vm["pc"].as();
}
else
{
badCommand("pc", std::to_string(vm["pc"].as()));
}
}
if (vm.count("size"))
{
if (vm["size"].as() > 0)
{
verified.size = vm["size"].as();
}
else
{
badCommand("size", std::to_string(vm["size"].as()));
}
}
if (vm.count("write_distances"))
{
verified.write_distances = 1;
}
else
{
verified.write_distances = 0;
}
}
// Check for continuous phenotype. If even one sample has neither 0 or 1 as
// phenotype
int continuousPhenotype (const std::vector& sample_list)
{
int cont_pheno = 0;
for (std::vector::const_iterator it = sample_list.begin(); it != sample_list.end(); ++it)
{
if (it->continuous())
{
cont_pheno = 1;
break;
}
}
// Write inferred output to terminal
if (cont_pheno)
{
std::cerr << "Detected continuous phenotype\n";
}
else
{
std::cerr << "Detected binary phenotype\n";
}
return cont_pheno;
}
// Conversion functions required as code is a mix of dlib and armadillo
// matrices
// This could obviously be improved...
arma::vec dlib_to_arma(const column_vector& dlib_vec)
{
arma::vec converted(dlib_vec.nr());
for (unsigned int i = 0; i < dlib_vec.nr(); ++i)
{
converted(i) = dlib_vec(i);
}
return converted;
}
column_vector arma_to_dlib(const arma::vec& arma_vec)
{
column_vector converted;
converted.set_size(arma_vec.n_elem);
for (unsigned int i = 0; i < arma_vec.n_elem; ++i)
{
converted(i) = arma_vec(i);
}
return converted;
}
// Converts a stl vector of strings to an arma mat of doubles
arma::mat vecToMat(const std::vector& in_col)
{
arma::mat out_mat(in_col.size(), 1);
for (unsigned int i = 0; i < in_col.size(); ++i)
{
out_mat(i, 0) = std::stof(in_col[i]);
}
return out_mat;
}
// Normalises a matrix's columns: subtract mean, divide by std dev
void normaliseMatCols(arma::mat& matrix_in)
{
arma::mat means = arma::mean(matrix_in);
arma::mat stddevs = arma::stddev(matrix_in);
matrix_in.each_row() -= means;
matrix_in.each_row() /= stddevs;
}
// Inverts a symmetric positive matrix, checking for errors
// Not passed by ref, creates a copy. Right thing to do?
arma::mat inv_covar(arma::mat A)
{
// Try the default. Internally this uses Cholesky decomposition and back
// solves. For large condition numbers it fails.
arma::mat B;
if (!inv_sympd(B, A))
{
// If the Cholesky decomposition fails, try pseudo-inverse
// This uses SVD:
// A = U*S*V.t() => A^-1 = V*S^-1*U.t()
// and ignores small values in the S matrix
if (!arma::pinv(B, A))
{
std::cerr << "A matrix inversion failed!" << std::endl;
}
}
return B;
}
seer-1.1.2/src/seerContinuousAssoc.cpp 0000664 0000000 0000000 00000005571 12774457224 0020010 0 ustar 00root root 0000000 0000000 /*
* File: seerContinuousAssoc.cpp
*
* Implements linear regression association test for seer
*
*/
#include "seer.hpp"
// Linear fit without covariates
void linearTest(Kmer& k, const arma::vec& y_train)
{
// Train classifier
arma::mat x_train = k.get_x();
doLinear(k, y_train, x_train);
}
// Linear fit with covariates
void linearTest(Kmer& k, const arma::vec& y_train, const arma::mat& mds)
{
// Train classifier
arma::mat x_train = arma::join_rows(k.get_x(), mds);
doLinear(k, y_train, x_train);
}
// Run linear fit
void doLinear(Kmer& k, const arma::vec& y_train, const arma::mat& x_train)
{
// Set up design matrix
arma::mat x_design = join_rows(arma::mat(x_train.n_rows,1,arma::fill::ones), x_train);
arma::vec b;
try
{
// A starting point for the optimiser
column_vector starting_point(x_design.n_cols);
starting_point(0) = mean(y_train);
for (size_t i = 1; i < x_design.n_cols; ++i)
{
starting_point(i) = bfgs_start_beta;
}
// Use BFGS optimiser in dlib to minimise OLS difference by chaging the
// b vector, which will end in starting_point
dlib::find_min(dlib::bfgs_search_strategy(),
dlib::objective_delta_stop_strategy(convergence_limit),
LinearLikelihood(x_design, y_train), LinearLikelihoodGradient(x_design, y_train),
starting_point, -1);
// Extract beta
b = dlib_to_arma(starting_point);
}
catch (std::exception& e)
{
#ifdef SEER_DEBUG
std::cerr << "bfgs failed with " << e.what() << std::endl;
#endif
k.add_comment("bfgs-fail");
// Calculate (X'X)^-1. Use QR decomposition to solve.
// Might be slower than Cholesky, but numerically is more stable
arma::mat Q, R;
// Regress beta: b = (X'X)^-1*Xy
// i.e. solve Rb = Q.t() * y where X=QR
arma::qr(Q, R, x_design);
arma::solve(b, R, Q.t() * y_train);
}
k.beta(b(1));
// Extract p-value
//
// W = B_1 / SE(B_1) ~ N(0,1)
//
// SE(B_1) = MSE * (X'X)^-1
// MSE = sum(Y_i-Y'_i)^2 / n-2
//
double MSE = accu(square(y_train - predictLinearProbs(x_design, b))) / (x_train.n_rows - 2);
arma::mat var_covar_mat = inv_covar(x_design.t()*x_design);
double se = pow((var_covar_mat(1,1) * MSE), 0.5);
k.standard_error(se);
double W = std::abs(k.beta()) / (se); // null hypothesis b_1 = 0
k.p_val(normalPval(W));
#ifdef SEER_DEBUG
std::cerr << "Wald statistic: " << W << "\n";
std::cerr << "p-value: " << k.p_val() << "\n";
#endif
// Add in covariate p-values
for (unsigned int i = 2; i < var_covar_mat.n_rows; ++i)
{
se = pow((var_covar_mat(i,i) * MSE), 0.5);
W = std::abs(b(i)) / (se);
k.add_covar_p(normalPval(W));
}
}
// returns y' = bx
arma::vec predictLinearProbs(const arma::mat& x, const arma::vec& b)
{
const arma::vec y = x * b;
return y;
}
seer-1.1.2/src/seerErr.cpp 0000664 0000000 0000000 00000000361 12774457224 0015371 0 ustar 00root root 0000000 0000000 /*
* File: seerErr.cpp
*
* Throws error messages
*
*/
#include "seercommon.hpp"
void badCommand(const std::string& command, const std::string& value)
{
throw std::runtime_error("Bad " + command + " specified: " + value + "\n");
}
seer-1.1.2/src/seerFilter.cpp 0000664 0000000 0000000 00000003512 12774457224 0016067 0 ustar 00root root 0000000 0000000 /*
* File: seerFilter.cpp
*
* Filters kmers that won't be meaningfully associated.
* Saves time over performing regressions on all, and reduces false positives
*
*/
#include "seercommon.hpp"
// Wrapper to all filter functions
int passFilters(const cmdOptions& filterOptions, Kmer& k, const std::vector& samples, const arma::vec& y, const int continuous_phenotype, const bool do_stats)
{
int pass = 0;
if (passBasicFilters(k, filterOptions.max_length, filterOptions.min_words, filterOptions.max_words))
{
// Don't bother with this if not running stats tests
pass = 1;
if (do_stats)
{
try // Some chi^2 tests may diverge - proceed anyway for now
{
pass = passStatsFilters(k, y, filterOptions.chi_cutoff, continuous_phenotype);
}
catch (std::exception& e)
{
std::cerr << "kmer " + k.sequence() + " failed chisq test with error: " + e.what() + "\n";
pass = 1;
}
}
}
return pass;
}
int passBasicFilters(const Kmer& k, const int max_length, const int min_words, const int max_words)
{
int passed = 1;
// Don't test long kmers
if (k.length() > max_length)
{
passed = 0;
}
// Impose min words
// TODO may want to make this more sophisticated, make sure there are at
// least ten words in each category
if (passed && (k.num_occurrences() < min_words || k.num_occurrences() > max_words))
{
passed = 0;
}
return passed;
}
int passStatsFilters(Kmer& k, const arma::vec& y, const double chi_cutoff, const int continuous_phenotype)
{
int passed = 1;
if (continuous_phenotype)
{
k.unadj_p_val(welchTwoSamplet(k, y));
}
else
{
k.unadj_p_val(chiTest(k, y));
}
if (k.unadj() > chi_cutoff)
{
passed = 0;
}
return passed;
}
seer-1.1.2/src/seerIO.cpp 0000664 0000000 0000000 00000026261 12774457224 0015157 0 ustar 00root root 0000000 0000000 /*
* File: seerIO.cpp
*
* Controls: reading in pheno to sample object
* Open (zipped) dsm file
* Internal format conversion
*
* NB: some input and output used overridden operators in
* .cpp files
*
*/
#include "seercommon.hpp"
std::regex gzipped(".+\\.gz"); // matches .gz at the end of a string (as regex match
// matches a whole string)
std::regex covar_regex("^(\\d+)$");
std::regex q_covar_regex("^(\\d+)q$");
/* .pheno files
* space separated:
* FID, IID, phenotype (1 control, 2 case)
*/
void readPheno(const std::string& filename, std::vector& samples, std::unordered_map& sample_map)
{
std::ifstream ist(filename.c_str());
if (!ist)
{
throw std::runtime_error("Could not open pheno file " + filename + "\n");
}
// Read in
Sample s;
while (ist >> s)
{
samples.push_back(s);
}
// Always keep samples sorted, for consistency between programs
std::sort(samples.begin(), samples.end(), Sample::compareSamples);
// Map of sample names to index in vector
for (unsigned int sample_index = 0; sample_index < samples.size(); ++sample_index)
{
sample_map[samples[sample_index].iid()] = sample_index;
}
}
// Open dsm files, which are possibly zipped
void openDsmFile(igzstream& dsm_stream, const std::string& file_name)
{
// Check for a .gz extension
if (!std::regex_match(file_name, gzipped))
{
// Warn
std::cerr << "WARNING: Input file " + file_name
+ " is not gzip compressed, which is recommended\n";
}
dsm_stream.open(file_name.c_str()); // Push binary file into buffer
// Set stream buffer of istream to the one just opened, and check ok
if (!dsm_stream.good())
{
throw std::runtime_error("Could not open kmer file " + file_name + "\n");
}
}
arma::vec constructVecY(const std::vector& samples)
{
arma::vec y;
y.zeros(samples.size());
for (unsigned int i = 0; i < samples.size(); ++i)
{
y[i] = samples[i].pheno();
}
return y;
}
// Also saves sample information
void writeMDS(const std::string& file_name, const std::vector& sample_names, const arma::mat& MDS)
{
if (MDS.n_rows == sample_names.size())
{
#ifdef NO_HDF5
MDS.save(file_name, arma::arma_ascii);
#else
MDS.save(file_name, arma::hdf5_binary);
#endif
std::string sample_file_name = file_name + sample_suffix;
std::ofstream sample_names_file(sample_file_name.c_str());
// Write out the sample name order
if (!sample_names_file)
{
std::cerr << "Could not write used sample names to " << sample_file_name << std::endl;
}
else
{
for (auto sample_it = sample_names.begin(); sample_it != sample_names.end(); ++sample_it)
{
sample_names_file << sample_it->iid() << std::endl;
}
}
}
else
{
throw std::logic_error("When writing matrix to " + file_name + " the number samples in the .pheno file did not match the number of columns or rows");
}
}
void writeDistances(const std::string& file_name, const arma::mat& distances)
{
distances.save(file_name, arma::csv_ascii);
}
arma::mat readHDF5(const std::string& file_name)
{
arma::mat HDF5;
if (fileStat(file_name))
{
HDF5.load(file_name);
}
else
{
throw std::runtime_error("Problem with loading MDS input file " + file_name);
}
return HDF5;
}
arma::mat readMDS(const std::string& file_name, const std::vector& sample_names)
{
arma::mat MDS = readHDF5(file_name);
// Check that the sample names match up
std::string sample_name_file = file_name + sample_suffix;
if (fileStat(sample_name_file))
{
std::ifstream samples_in(sample_name_file.c_str());
if (samples_in)
{
arma::uvec keep_indices(sample_names.size());
unsigned int sample_row = 0;
unsigned int file_row = 0;
while (samples_in)
{
std::string sample_name;
samples_in >> sample_name;
// Must be ordered, and lines in sample_names be a subset of what
// is in the file. Otherwise a non-compatible mds will be returned
// which will throw
if (sample_name == sample_names.at(sample_row).iid())
{
keep_indices(sample_row) = file_row;
if (++sample_row >= sample_names.size())
{
break;
}
}
++file_row;
}
// Only keep the rows where the pheno file has data
if (sample_row == sample_names.size())
{
MDS = MDS.rows(keep_indices);
}
else
{
throw std::runtime_error("Could not find all samples in input .pheno file in --struct mat. Was kmds run with all samples included?");
}
}
else
{
std::cerr << "WARNING: Could not open sample file " + sample_name_file + ". Ensure kmds and seer -p inputs were identical" << std::endl;
}
}
else
{
std::cerr << "WARNING: Could not find sample file " + sample_name_file + ". Ensure kmds and seer -p inputs were identical" << std::endl;
}
return MDS;
}
arma::mat readMDSList(const std::string& filename)
{
std::ifstream ist(filename.c_str());
if (!ist)
{
throw std::runtime_error("Could not open mds list file " + filename + "\n");
}
else
{
std::cerr << "Reading subsampled matrices from " + filename + "\n";
}
std::string matrix_file;
arma::mat combined_matrix;
int i = 0;
while (ist >> matrix_file)
{
combined_matrix = join_rows(combined_matrix, readHDF5(matrix_file));
std::cerr << "Joined matrix " << ++i << "\n";
}
return combined_matrix;
}
// Opens a text file containing covariates, and reads the specified columns
// into a matrix. Categorical covariates use dummy coding
arma::mat parseCovars(const std::string& file, const std::string& columns)
{
// Check file exists
if (!fileStat(file))
{
throw std::runtime_error("Covariate file " + file + " not found");
}
// First parse columns required
std::vector> column_list = parseCovarColumns(columns);
// Open file, and read in required columns
std::vector covars_in(column_list.size()); // Vector of covariates (each of which is internally a vector)
std::ifstream ist(file.c_str());
if (ist)
{
std::string line_buf;
while (std::getline(ist, line_buf)) // Read a line in
{
std::string sample_name;
std::stringstream line_stream(line_buf);
// Read in sample name for the line
line_stream >> sample_name;
auto col_it = column_list.begin();
size_t row = 0;
while (col_it != column_list.end()) // Go through all required columns
{
std::string col_buf; // Work out how many fields to read
int skip_cols = std::get<0>(*col_it);
if (col_it != column_list.begin())
{
skip_cols -= std::get<0>(*(col_it-1));
}
else
{
skip_cols -= 1; // Sample name already read
}
for (int i = 0; i> col_buf;
}
covars_in[row].addCovarValue(sample_name, col_buf);
++col_it; // Move to next column
++row;
}
}
}
else
{
throw std::runtime_error("Could not open covariate file" + file);
}
// Encode categorical columns. Convert strings to doubles
// All in arma::mat
arma::mat covar_matrix;
auto mat_col = covars_in.begin();
for (auto col_it = column_list.begin(); col_it != column_list.end(); ++col_it)
{
// When converting, first sort on sample name so in the same order as
// pheno vector that will be merged with
arma::mat new_col;
if (std::get<1>(*col_it))
{
// Categorical covars need dummy encoding
new_col = encodeDummy(mat_col->sortCovars());
}
else
{
// Quantitative covars need normalising
new_col = vecToMat(mat_col->sortCovars());
normaliseMatCols(new_col);
}
// Add the row in
if (covar_matrix.n_cols > 0)
{
covar_matrix = arma::join_rows(covar_matrix, new_col);
}
else
{
covar_matrix = new_col;
}
++mat_col;
}
return covar_matrix;
}
std::vector> parseCovarColumns(const std::string& columns)
{
std::vector