[med-svn] [seer] 10/13: Imported Upstream version 1.1.0
Andreas Tille
tille at debian.org
Thu Apr 14 09:49:55 UTC 2016
This is an automated email from the git hooks/post-receive script.
tille pushed a commit to branch master
in repository seer.
commit e7649c6c0cd13c18a322aafbc25793e1f7427089
Author: Andreas Tille <tille at debian.org>
Date: Thu Apr 14 11:46:56 2016 +0200
Imported Upstream version 1.1.0
---
.gitignore | 45 +
.gitmodules | 3 +
LICENSE | 340 +++
Makefile | 19 +
README.md | 91 +
debian/README.Debian | 12 -
debian/changelog | 11 -
debian/compat | 1 -
debian/control | 38 -
debian/copyright | 26 -
debian/examples | 1 -
debian/manpages | 1 -
debian/mans/blast_top_hits.1 | 28 -
debian/mans/combineKmers.1 | 26 -
debian/mans/filter_seer.1 | 41 -
debian/mans/kmds.1 | 74 -
debian/mans/map_back.1 | 26 -
debian/mans/seer.1 | 75 -
debian/patches/fix_lib_location.patch | 20 -
debian/patches/hardening.patch | 39 -
debian/patches/pthread_versus_hardening.patch | 20 -
debian/patches/series | 3 -
debian/rules | 21 -
debian/source/format | 1 -
debian/upstream/metadata | 8 -
debian/watch | 3 -
example_wrapper.py | 185 ++
scripts/blast_top_hits.pl | 130 ++
src/Makefile | 84 +
src/combineCmdLine.cpp | 63 +
src/combineInit.cpp | 47 +
src/combineKmers.cpp | 104 +
src/combineKmers.hpp | 25 +
src/covar.cpp | 28 +
src/covar.hpp | 27 +
src/fasta.cpp | 113 +
src/fasta.hpp | 42 +
src/filterCmdLine.cpp | 160 ++
src/filter_seer.cpp | 148 ++
src/filter_seer.hpp | 34 +
src/kmds.hpp | 39 +
src/kmdsCmdLine.cpp | 95 +
src/kmdsMain.cpp | 264 +++
src/kmdsStruct.cpp | 161 ++
src/kmer.cpp | 149 ++
src/kmer.hpp | 55 +
src/linearFunction.cpp | 39 +
src/linkFunction.hpp | 72 +
src/logitFunction.cpp | 76 +
src/mapCmdLine.cpp | 67 +
src/mapMain.cpp | 157 ++
src/map_back.hpp | 43 +
src/sample.cpp | 44 +
src/sample.hpp | 31 +
src/seer.hpp | 45 +
src/seerBinaryAssoc.cpp | 199 ++
src/seerChiFilter.cpp | 127 +
src/seerCmdLine.cpp | 102 +
src/seerCommon.cpp | 243 ++
src/seerContinuousAssoc.cpp | 100 +
src/seerErr.cpp | 14 +
src/seerFilter.cpp | 82 +
src/seerIO.cpp | 317 +++
src/seerMain.cpp | 209 ++
src/seercommon.hpp | 117 +
src/significant_kmer.cpp | 133 ++
src/significant_kmer.hpp | 73 +
test/Makefile | 10 +
test/example.pheno | 3069 +++++++++++++++++++++++++
test/example_kmers.gz | Bin 0 -> 63488 bytes
test/generate_test.pl | 49 +
test/subset.pheno | 200 ++
72 files changed, 8069 insertions(+), 475 deletions(-)
diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..eb54d6c
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,45 @@
+# 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-4.*
+armadillo-5.*
+mlpack-1.0.*
+boost_1_*
+gzstream
diff --git a/.gitmodules b/.gitmodules
new file mode 100644
index 0000000..8c53a00
--- /dev/null
+++ b/.gitmodules
@@ -0,0 +1,3 @@
+[submodule "dlib"]
+ path = dlib
+ url = git at github.com:davisking/dlib.git
diff --git a/LICENSE b/LICENSE
new file mode 100644
index 0000000..d6a9326
--- /dev/null
+++ b/LICENSE
@@ -0,0 +1,340 @@
+GNU GENERAL PUBLIC LICENSE
+ Version 2, June 1991
+
+ Copyright (C) 1989, 1991 Free Software Foundation, Inc., <http://fsf.org/>
+ 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.
+
+ Preamble
+
+ The licenses for most software are designed to take away your
+freedom to share and change it. By contrast, the GNU General Public
+License is intended to guarantee your freedom to share and change free
+software--to make sure the software is free for all its users. This
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+Foundation's software and to any other program whose authors commit to
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+
diff --git a/Makefile b/Makefile
new file mode 100755
index 0000000..611137e
--- /dev/null
+++ b/Makefile
@@ -0,0 +1,19 @@
+export PREFIX=${HOME}/software
+export BINDIR=$(PREFIX)/bin
+
+all:
+ cd src && $(MAKE) all
+ cd test && $(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
+
diff --git a/README.md b/README.md
new file mode 100644
index 0000000..67f2442
--- /dev/null
+++ b/README.md
@@ -0,0 +1,91 @@
+# seer
+Sequence element enrichment analysis. This document contains
+installation instuctions. Usage can be found on the [wiki](https://github.com/johnlees/seer/wiki/Usage)
+
+Installation
+==============
+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
+
+Dependencies
+--------------
+seer currently depends on
+
+- gzstream <http://www.cs.unc.edu/Research/compgeom/gzstream/>
+- armadillo <http://arma.sourceforge.net/>
+- boost <http://www.boost.org/>
+- dlib <http://www.dlib.net/>
+- HDF5 <https://www.hdfgroup.org/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).
+
+###Brief installation instructions
+
+**gzstream**
+
+Download and unpack to a folder gzstream in the root of the repository. Change into the directory and type
+
+ make
+
+**armadillo**
+
+Download and unpack. Change into directory and type
+
+ cmake .
+ 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 <http://www.boost.org/doc/libs/1_57_0/more/getting_started/unix-variants.html> for details on how to use ./b2 to install. I reccommend that you create a user-config.jam file in the boost root which modifies the gcc compilation:
+
+ using gcc:
+ : std11
+ : g++
+ : <cxxflags>-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
+
+**HDF5**
+
+Best installed with your distribution's package manager. Otherwise use
+a binary from <https://www.hdfgroup.org/HDF5/release/obtain5.html>, 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 <more configure_flags>
+ make
+ make check
+ make install
+ make check-install
+
+Usage, interpretation of results, and troubleshooting
+=============
+See the [wiki](https://github.com/johnlees/seer/wiki/Usage)
diff --git a/debian/README.Debian b/debian/README.Debian
deleted file mode 100644
index 144dcff..0000000
--- a/debian/README.Debian
+++ /dev/null
@@ -1,12 +0,0 @@
-seer for Debian
-===============
-
-For more detailed documentation please see the seer Wiki page:
-
- https://github.com/johnlees/seer/wiki/Usage
-
-There is also an example wrapper script in
-
- examples/example_wrapper.py
-
- -- Andreas Tille <tille at debian.org> Tue, 29 Mar 2016 19:32:29 +0200
diff --git a/debian/changelog b/debian/changelog
deleted file mode 100644
index bd6789a..0000000
--- a/debian/changelog
+++ /dev/null
@@ -1,11 +0,0 @@
-seer (1.1.0-2) UNRELEASED; urgency=medium
-
- * Suggests: fsm-lite
-
- -- Andreas Tille <tille at debian.org> Tue, 12 Apr 2016 10:52:38 +0200
-
-seer (1.1.0-1) unstable; urgency=medium
-
- * Initial release (Closes: #819674)
-
- -- Andreas Tille <tille at debian.org> Thu, 31 Mar 2016 22:35:05 +0200
diff --git a/debian/compat b/debian/compat
deleted file mode 100644
index ec63514..0000000
--- a/debian/compat
+++ /dev/null
@@ -1 +0,0 @@
-9
diff --git a/debian/control b/debian/control
deleted file mode 100644
index 0de04bc..0000000
--- a/debian/control
+++ /dev/null
@@ -1,38 +0,0 @@
-Source: seer
-Maintainer: Debian Med Packaging Team <debian-med-packaging at lists.alioth.debian.org>
-Uploaders: Andreas Tille <tille at debian.org>
-Section: science
-Priority: optional
-Build-Depends: debhelper (>= 9),
- libarmadillo-dev,
- libdlib-dev,
- libhdf5-dev,
-# libhdf5-mpi-dev,
- libgzstream-dev,
- libboost-dev,
- libboost-program-options-dev
-Standards-Version: 3.9.7
-Vcs-Browser: https://anonscm.debian.org/viewvc/debian-med/trunk/packages/seer/trunk/
-Vcs-Svn: svn://anonscm.debian.org/debian-med/trunk/packages/seer/trunk/
-Homepage: https://github.com/johnlees/seer
-
-Package: seer
-Architecture: any
-Depends: ${shlibs:Depends},
- ${misc:Depends},
- libtext-csv-perl
-Suggests: fsm-lite
-Description: genomic sequence element (kmer) enrichment analysis
- Bacterial genomes vary extensively in terms of both gene content and
- gene sequence - this plasticity hampers the use of traditional SNP-based
- methods for identifying all genetic associations with phenotypic
- variation. SEER provides a computationally scalable and widely
- applicable statistical method for the identification of sequence
- elements that are significantly enriched in a phenotype of interest.
- SEER is applicable to even tens of thousands of genomes by counting variable-
- length k-mers using a distributed string-mining algorithm. Robust
- options are provided for association analysis that also correct for the
- clonal population structure of bacteria. Using large collections of
- genomes of the major human pathogen Streptococcus pneumoniae, SEER
- identifies relevant previously characterised resistance determinants for
- several antibiotics.
diff --git a/debian/copyright b/debian/copyright
deleted file mode 100644
index 1c56362..0000000
--- a/debian/copyright
+++ /dev/null
@@ -1,26 +0,0 @@
-Format: http://www.debian.org/doc/packaging-manuals/copyright-format/1.0/
-Upstream-Contact: John Lees <john at johnlees.me>
-Upstream-Name: seer
-Source: https://github.com/johnlees/seer/releases
-
-Files: *
-Copyright: 2015-2016 John Lees <john at johnlees.me>
-License: GPL-2+
-
-Files: debian/*
-Copyright: 2016 Andreas Tille <tille at debian.org>
-License: GPL-2+
-
-License: GPL-2+
- This program is free software; you can redistribute it and/or modify
- it under the terms of the GNU General Public License as published by
- the Free Software Foundation; either version 2 of the License, or
- (at your option) any later version.
- .
- This program is distributed in the hope that it will be useful,
- but WITHOUT ANY WARRANTY; without even the implied warranty of
- MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
- GNU General Public License for more details.
- .
- On Debian systems you can find a copy of the GNU General Public License
- version 2 or later at /usr/share/common-licenses/GPL-2.
diff --git a/debian/examples b/debian/examples
deleted file mode 100644
index 436566d..0000000
--- a/debian/examples
+++ /dev/null
@@ -1 +0,0 @@
-example_wrapper.py
diff --git a/debian/manpages b/debian/manpages
deleted file mode 100644
index 4f4649b..0000000
--- a/debian/manpages
+++ /dev/null
@@ -1 +0,0 @@
-debian/mans/*.1
diff --git a/debian/mans/blast_top_hits.1 b/debian/mans/blast_top_hits.1
deleted file mode 100644
index 15ce157..0000000
--- a/debian/mans/blast_top_hits.1
+++ /dev/null
@@ -1,28 +0,0 @@
-.\" DO NOT MODIFY THIS FILE! It was generated by help2man 1.47.3.
-.TH BLAST_TOP_HITS "1" "March 2016" "blast_top_hits 1.1.0" "User Commands"
-.SH NAME
-blast_top_hits \- reduces a blast or blat output file to the top hit for each query sequence only
-.SH SYNOPSIS
-.B blast_top_hits.pl
-\fI\,--input blast_out --mode blast <options>\/\fR
-.SH DESCRIPTION
-Reduces a blast or blat output file to the top hit for each query sequence only.
-.P
-This program belongs to seer(1) (Sequence Element (kmer) Enrichment Analysis).
-.SH OPTIONS
-.SS Required
-.TP
-\fB\-\-input\fR
-BLAST output file in \fB\-m\fR 6/\-outfmt 6 or BLAT .psl file
-.TP
-\fB\-\-mode\fR
-Either blast or blat
-.SS Optional
-.TP
-\fB\-\-full_query\fR
-The entire query must be reported as a match to appear in the output
-.TP
-\fB\-h\fR, \fB\-\-help\fR
-Shows this help.
-.SH AUTHOR
-This manpage was written by Andreas Tille for the Debian distribution and can be used for any other usage of the program.
diff --git a/debian/mans/combineKmers.1 b/debian/mans/combineKmers.1
deleted file mode 100644
index 51aaa5a..0000000
--- a/debian/mans/combineKmers.1
+++ /dev/null
@@ -1,26 +0,0 @@
-.\" DO NOT MODIFY THIS FILE! It was generated by help2man 1.47.3.
-.TH COMBINEKMERS "1" "March 2016" "combineKmers 1.1.0" "User Commands"
-.SH NAME
-combineKmers \- basic algorithm to combine kmer counts across samples
-.P
-This program belongs to seer(1) (Sequence Element (kmer) Enrichment Analysis).
-.SH DESCRIPTION
-Basic algorithm to combine kmer counts across samples
-.SH OPTIONS
-.SS "Required options:"
-.TP
-\fB\-r\fR [ \fB\-\-samples\fR ] arg
-file with tab separated sample name and kmer file
-.TP
-\fB\-o\fR [ \fB\-\-output\fR ] arg
-output file prefix
-.SS "Other options:"
-.TP
-\fB\-\-min_samples\fR arg (=1)
-minimum number of samples kmer must occur in to be
-printed
-.TP
-\fB\-h\fR [ \fB\-\-help\fR ]
-full help message
-.SH AUTHOR
-This manpage was written by Andreas Tille for the Debian distribution and can be used for any other usage of the program.
diff --git a/debian/mans/filter_seer.1 b/debian/mans/filter_seer.1
deleted file mode 100644
index f0c272b..0000000
--- a/debian/mans/filter_seer.1
+++ /dev/null
@@ -1,41 +0,0 @@
-.\" DO NOT MODIFY THIS FILE! It was generated by help2man 1.47.3.
-.TH FILTER_SEER "1" "March 2016" "filter_seer 1.1.0" "User Commands"
-.SH NAME
-filter_seer \- post filtering of significant kmers
-.SH DESCRIPTION
-Post filtering of significant kmers.
-.P
-This program belongs to seer(1) (Sequence Element (kmer) Enrichment Analysis).
-.SH OPTIONS
-.SS "Required options:"
-.TP
-\fB\-k\fR [ \fB\-\-kmers\fR ] arg
-file of output from seer
-.SS "Filtering options:"
-.TP
-\fB\-\-chisq\fR arg
-minimum unadjusted p\-value to output
-.TP
-\fB\-\-pval\fR arg
-minimum adjusted p\-value to output
-.TP
-\fB\-\-maf\fR arg
-minimum maf/max 1\-maf to output
-.TP
-\fB\-\-beta\fR arg
-minimum |beta| to output
-.TP
-\fB\-\-substr\fR
-remove smaller kmers completely represented elsewhere
-.TP
-\fB\-\-pos_beta\fR
-output positive effect sizes only
-.SS "Other options:"
-.TP
-\fB\-s\fR [ \fB\-\-sort\fR ] arg
-field to sort on: chisq, pval, maf or beta
-.TP
-\fB\-h\fR [ \fB\-\-help\fR ]
-full help message
-.SH AUTHOR
-This manpage was written by Andreas Tille for the Debian distribution and can be used for any other usage of the program.
diff --git a/debian/mans/kmds.1 b/debian/mans/kmds.1
deleted file mode 100644
index c02e1c9..0000000
--- a/debian/mans/kmds.1
+++ /dev/null
@@ -1,74 +0,0 @@
-.\" DO NOT MODIFY THIS FILE! It was generated by help2man 1.47.3.
-.TH KMDS "1" "March 2016" "kmds 1.1.0" "User Commands"
-.SH NAME
-kmds \- control for population structure
-.SH DESCRIPTION
-Control for population structure. Filter kmers and create a matrix representing population structure.
-.P
-This program belongs to seer(1) (Sequence Element (kmer) Enrichment Analysis).
-.SH OPTIONS
-1) filter and subsample with \fB\-\-no_mds\fR and \fB\-\-size\fR
-.P
-2) combine, and do metric multidimensional scaling with \fB\-\-combine_mds\fR
-.SS "Required options:"
-.TP
-\fB\-k\fR [ \fB\-\-kmers\fR ] arg
-dsm kmer output file
-.TP
-\fB\-p\fR [ \fB\-\-pheno\fR ] arg
-\&.pheno metadata
-.SS "MDS options:"
-.TP
-\fB\-o\fR [ \fB\-\-output\fR ] arg
-output prefix for new dsm file
-.TP
-\fB\-\-no_mds\fR
-do not perform MDS; output subsampled matrix instead
-.TP
-\fB\-\-write_distances\fR
-write csv of distance matrix
-.TP
-\fB\-\-mds_concat\fR arg
-list of subsampled matrices to use in MDS. Performs
-only MDS; implies \fB\-\-no_filtering\fR
-.TP
-\fB\-\-pc\fR arg (=3)
-number of principal coordinates to output
-.TP
-\fB\-\-size\fR arg (=1000000)
-number of kmers to use in MDS
-.TP
-\fB\-\-threads\fR arg (=1)
-number of threads. Suggested: 4
-.SS "Filtering options:"
-.TP
-\fB\-\-no_filtering\fR
-turn off all filtering and do not output new kmer
-file
-.TP
-\fB\-\-max_length\fR arg (=100)
-maximum kmer length
-.TP
-\fB\-\-maf\fR arg (=0.01)
-minimum kmer frequency
-.TP
-\fB\-\-min_words\fR arg
-minimum kmer occurrences. Overrides \fB\-\-maf\fR
-.TP
-\fB\-\-chisq\fR arg (=10e\-5)
-p\-value threshold for initial chi squared test. Set
-to 1 to show all
-.SS "Other options:"
-.TP
-\fB\-h\fR [ \fB\-\-help\fR ]
-full help message
-.SH EXAMPLE
-Filter kmers and create a matrix representing population structure with kmds
-.IP
-kmds -k dsm_input.txt.gz --pheno metadata.pheno -o filtered
-.P
-To spread this process out, run the following command on each dsm file
-.IP
-kmds -k dsm_input.txt.gz --pheno metadata.pheno --no_mds --size 10000
-.SH AUTHOR
-This manpage was written by Andreas Tille for the Debian distribution and can be used for any other usage of the program.
diff --git a/debian/mans/map_back.1 b/debian/mans/map_back.1
deleted file mode 100644
index ca1c85a..0000000
--- a/debian/mans/map_back.1
+++ /dev/null
@@ -1,26 +0,0 @@
-.\" DO NOT MODIFY THIS FILE! It was generated by help2man 1.47.3.
-.TH MAP_BACK "1" "March 2016" "map_back 1.1.0" "User Commands"
-.SH NAME
-map_back \- context of significant kmers
-.SH DESCRIPTION
-Context of significant kmers.
-.P
-This program belongs to seer(1) (Sequence Element (kmer) Enrichment Analysis).
-.SH OPTIONS
-.SS "Required options:"
-.TP
-\fB\-k\fR [ \fB\-\-kmers\fR ] arg
-seer kmer output file
-.TP
-\fB\-r\fR [ \fB\-\-references\fR ] arg
-file with tab separated reference name and fasta
-file
-.SS "Other options:"
-.TP
-\fB\-\-threads\fR arg (=1)
-number of threads. Suggested: 4
-.TP
-\fB\-h\fR [ \fB\-\-help\fR ]
-full help message
-.SH AUTHOR
-This manpage was written by Andreas Tille for the Debian distribution and can be used for any other usage of the program.
diff --git a/debian/mans/seer.1 b/debian/mans/seer.1
deleted file mode 100644
index 888a132..0000000
--- a/debian/mans/seer.1
+++ /dev/null
@@ -1,75 +0,0 @@
-.\" DO NOT MODIFY THIS FILE! It was generated by help2man 1.47.3.
-.TH SEER "1" "March 2016" "seer 1.1.0" "User Commands"
-.SH NAME
-seer \- sequence element enrichment analysis
-.SH DESCRIPTION
-Sequence Element Enrichment Analysis
-.P
-The .pheno file format is tab separated, two columns with sample name, one with phenotype. Phenotypes of only 0 or 1 will be treated as binary, any other value and the phenotype will be treated as quantitative. Therefore for missing phenotype values the sample should simply be excluded from this file.
-.SH OPTIONS
-.SS "Required options:"
-.TP
-\fB\-k\fR [ \fB\-\-kmers\fR ] arg
-dsm kmer output file
-.TP
-\fB\-p\fR [ \fB\-\-pheno\fR ] arg
-\&.pheno metadata
-.SS "Covariate options:"
-.TP
-\fB\-\-struct\fR arg
-mds values from kmds
-.TP
-\fB\-\-covar_file\fR arg
-file containing covariates
-.TP
-\fB\-\-covar_list\fR arg
-list of columns covariates to use. Format is 1,2q,3
-(use q for quantitative)
-.SS "Performance options:"
-.TP
-\fB\-\-threads\fR arg (=1)
-number of threads. Suggested: 4
-.SS "Filtering options:"
-.TP
-\fB\-\-no_filtering\fR
-turn off all filtering and peform tests on all kmers
-input
-.TP
-\fB\-\-max_length\fR arg (=100)
-maximum kmer length
-.TP
-\fB\-\-maf\fR arg (=0.01)
-minimum kmer frequency
-.TP
-\fB\-\-min_words\fR arg
-minimum kmer occurrences. Overrides \fB\-\-maf\fR
-.TP
-\fB\-\-positive_only\fR
-only test words with a predicted positive effect
-direction
-.TP
-\fB\-\-chisq\fR arg (=10e\-5)
-p\-value threshold for initial chi squared test. Set
-to 1 to show all
-.TP
-\fB\-\-pval\fR arg (=10e\-8)
-p\-value threshold for final logistic test. Set to 1
-to show all
-.SS "Other options:"
-.TP
-\fB\-\-print_samples\fR
-print lists of samples significant kmers were found
-in
-.TP
-\fB\-h\fR [ \fB\-\-help\fR ]
-full help message
-.SH EXAMPLES
-Basic usage:
-.IP
-seer -k dsm_input.txt.gz --pheno metadata.pheno > significant_kmers.txt
-.P
-To use the kmds output, increase execution speed and give the most complete output
-.IP
-seer -k filtered.gz --pheno metadata.pheno --struct filtered.dsm --threads 4 --print_samples
-.SH AUTHOR
-This manpage was written by Andreas Tille for the Debian distribution and can be used for any other usage of the program.
diff --git a/debian/patches/fix_lib_location.patch b/debian/patches/fix_lib_location.patch
deleted file mode 100644
index e27b273..0000000
--- a/debian/patches/fix_lib_location.patch
+++ /dev/null
@@ -1,20 +0,0 @@
-Author: Andreas Tille <tille at debian.org>
-Last-Update: Tue, 29 Mar 2016 19:32:29 +0200
-Description: Remove non-existing library pathes
-
---- a/src/Makefile
-+++ b/src/Makefile
-@@ -11,10 +11,10 @@ BINDIR=$(PREFIX)/bin
- #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
-+CXXFLAGS+=-Wall -O3 -std=c++11
-+SEER_LDLIBS=-lhdf5 -lgzstream -lz -larmadillo -lboost_program_options -llapack -lblas $(LDFLAGS)
- MAP_LDLIBS=-L$(PREFIX)/lib -lboost_program_options
--COMBINE_LDLIBS=-L../gzstream -L$(PREFIX)/lib -lgzstream -lz -lboost_program_options
-+COMBINE_LDLIBS=-lgzstream -lz -lboost_program_options
- FILTER_LDLIBS=-L$(PREFIX)/lib -lboost_program_options
-
- CPPFLAGS=-I$(PREFIX)/include -I../gzstream -I../dlib -D DLIB_NO_GUI_SUPPORT=1 -D DLIB_USE_BLAS=1 -D DLIB_USE_LAPACK=1
diff --git a/debian/patches/hardening.patch b/debian/patches/hardening.patch
deleted file mode 100644
index a667367..0000000
--- a/debian/patches/hardening.patch
+++ /dev/null
@@ -1,39 +0,0 @@
-Author: Andreas Tille <tille at debian.org>
-Last-Update: Tue, 29 Mar 2016 19:32:29 +0200
-Description: Propagate hardening options
-
---- a/src/Makefile
-+++ b/src/Makefile
-@@ -13,11 +13,11 @@ BINDIR=$(PREFIX)/bin
- # gcc
- CXXFLAGS+=-Wall -O3 -std=c++11
- SEER_LDLIBS=-lhdf5 -lgzstream -lz -larmadillo -lboost_program_options -llapack -lblas $(LDFLAGS)
--MAP_LDLIBS=-L$(PREFIX)/lib -lboost_program_options
--COMBINE_LDLIBS=-lgzstream -lz -lboost_program_options
--FILTER_LDLIBS=-L$(PREFIX)/lib -lboost_program_options
-+MAP_LDLIBS=-lboost_program_options $(LDFLAGS)
-+COMBINE_LDLIBS=-lgzstream -lz -lboost_program_options $(LDFLAGS)
-+FILTER_LDLIBS=-lboost_program_options $(LDFLAGS)
-
--CPPFLAGS=-I$(PREFIX)/include -I../gzstream -I../dlib -D DLIB_NO_GUI_SUPPORT=1 -D DLIB_USE_BLAS=1 -D DLIB_USE_LAPACK=1
-+CPPFLAGS+=-I$(PREFIX)/include -I../gzstream -I../dlib -D DLIB_NO_GUI_SUPPORT=1 -D DLIB_USE_BLAS=1 -D DLIB_USE_LAPACK=1
-
- # For static linking. To use type 'make static'
- # Requires icpc and mkl
-@@ -56,13 +56,13 @@ kmds: $(KMDS_OBJECTS)
- $(LINK.cpp) $^ $(SEER_LDLIBS) -o $@
-
- map_back: $(MAP_OBJECTS)
-- $(LINK.cpp) $^ $(MAP_LDLIBS) -o $@
-+ $(LINK.cpp) $^ $(MAP_LDLIBS) $(LDFLAGS) -o $@
-
- combineKmers: $(COMBINE_OBJECTS)
-- $(LINK.cpp) $^ $(COMBINE_LDLIBS) -o $@
-+ $(LINK.cpp) $^ $(COMBINE_LDLIBS) $(LDFLAGS) -o $@
-
- filter_seer: $(FILTER_OBJECTS)
-- $(LINK.cpp) $^ $(FILTER_LDLIBS) -o $@
-+ $(LINK.cpp) $^ $(FILTER_LDLIBS) $(LDFLAGS) -o $@
-
- seer_static: $(SEER_OBJECTS)
- $(LINK.cpp) $^ $(SEER_STATIC_LDLIBS) -o $@
diff --git a/debian/patches/pthread_versus_hardening.patch b/debian/patches/pthread_versus_hardening.patch
deleted file mode 100644
index e0864a4..0000000
--- a/debian/patches/pthread_versus_hardening.patch
+++ /dev/null
@@ -1,20 +0,0 @@
-Author: Andreas Tille <tille at debian.org>
-Last-Update: Tue, 29 Mar 2016 19:32:29 +0200
-Description: Set -pthread inside Makefile
- When doing this in debian/rules via
- export LDFLAGS := -pthread $(LDFLAGS)
- the hardening options are overriden
-
---- a/src/Makefile
-+++ b/src/Makefile
-@@ -12,8 +12,8 @@ BINDIR=$(PREFIX)/bin
- #FILTER_LDLIBS=-L$(PREFIX)/lib -lboost_program_options -mkl
- # gcc
- CXXFLAGS+=-Wall -O3 -std=c++11
--SEER_LDLIBS=-lhdf5 -lgzstream -lz -larmadillo -lboost_program_options -llapack -lblas $(LDFLAGS)
--MAP_LDLIBS=-lboost_program_options $(LDFLAGS)
-+SEER_LDLIBS=-lhdf5 -lgzstream -lz -larmadillo -lboost_program_options -llapack -lblas -pthread $(LDFLAGS)
-+MAP_LDLIBS=-lboost_program_options -pthread $(LDFLAGS)
- COMBINE_LDLIBS=-lgzstream -lz -lboost_program_options $(LDFLAGS)
- FILTER_LDLIBS=-lboost_program_options $(LDFLAGS)
-
diff --git a/debian/patches/series b/debian/patches/series
deleted file mode 100644
index 9762ec0..0000000
--- a/debian/patches/series
+++ /dev/null
@@ -1,3 +0,0 @@
-fix_lib_location.patch
-hardening.patch
-pthread_versus_hardening.patch
diff --git a/debian/rules b/debian/rules
deleted file mode 100755
index 46ddc59..0000000
--- a/debian/rules
+++ /dev/null
@@ -1,21 +0,0 @@
-#!/usr/bin/make -f
-
-# DH_VERBOSE := 1
-DEBPKGNAME := $(shell dpkg-parsechangelog | awk '/^Source:/ {print $$2}')
-
-include /usr/share/dpkg/architecture.mk
-export CPATH := /usr/include/hdf5/serial
-export LIBRARY_PATH := /usr/lib/$(DEB_TARGET_MULTIARCH)/hdf5/serial
-
-## The following destroys hardening flags.
-## --> see debian/patches/pthread_versus_hardening.patch
-##export LDFLAGS := -pthread $(LDFLAGS)
-
-export DEB_BUILD_MAINT_OPTIONS = hardening=+all
-
-override_dh_auto_install:
- dh_auto_install -- PREFIX=$(CURDIR)/debian/$(DEBPKGNAME)/usr
- cp -a scripts/blast_top_hits.pl $(CURDIR)/debian/$(DEBPKGNAME)/usr/bin/blast_top_hits
-
-%:
- dh $@
diff --git a/debian/source/format b/debian/source/format
deleted file mode 100644
index 163aaf8..0000000
--- a/debian/source/format
+++ /dev/null
@@ -1 +0,0 @@
-3.0 (quilt)
diff --git a/debian/upstream/metadata b/debian/upstream/metadata
deleted file mode 100644
index 848f276..0000000
--- a/debian/upstream/metadata
+++ /dev/null
@@ -1,8 +0,0 @@
-Reference:
- Author: John A Lees and Minna Vehkala and Niko Välimäki and Simon R Harris and Claire Chewapreecha and Nicholas J Croucher and Pekka Marttinen and Mark R Davies and Andrew C Steer and Stephen Y C Tong and Antti Honkela and Julian Parkhill and Stephen D Bentley and Jukka Corander
- Title: Sequence element enrichment analysis to determine the genetic basis of bacterial phenotypes
- Journal: bioRxiv
- Year: 2016
- DOI: 10.1101/038463
- URL: http://biorxiv.org/content/biorxiv/early/2016/03/02/038463
- eprint: http://biorxiv.org/content/biorxiv/early/2016/03/02/038463.full.pdf
diff --git a/debian/watch b/debian/watch
deleted file mode 100644
index 01bbc15..0000000
--- a/debian/watch
+++ /dev/null
@@ -1,3 +0,0 @@
-version=3
-
-https://github.com/johnlees/seer/releases .*/archive/v(\d[\d.-]+)\.(?:tar(?:\.gz|\.bz2)?|tgz)
diff --git a/example_wrapper.py b/example_wrapper.py
new file mode 100755
index 0000000..59f72ec
--- /dev/null
+++ b/example_wrapper.py
@@ -0,0 +1,185 @@
+#!/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")
+
diff --git a/scripts/blast_top_hits.pl b/scripts/blast_top_hits.pl
new file mode 100755
index 0000000..f0365b6
--- /dev/null
+++ b/scripts/blast_top_hits.pl
@@ -0,0 +1,130 @@
+#!/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 $help_message = <<HELP;
+Usage: ./blast_top_hits.pl --input blast_out --mode blast <options>
+
+Reduces a blast or blat output file to the top hit for each query sequence
+only
+
+ Options
+
+ Required
+ --input BLAST output file in -m 6/-outfmt 6 or BLAT .psl file
+ --mode Either blast or blat
+
+ Optional
+ --full_query The entire query must be reported as a match to appear
+ in the output
+
+ -h, --help Shows this help.
+
+HELP
+#****************************************************************************************#
+#* Main *#
+#****************************************************************************************#
+
+#* gets input parameters
+my ($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 $file_in;
+ }
+}
+
+exit(0);
+
diff --git a/src/Makefile b/src/Makefile
new file mode 100755
index 0000000..d74a82f
--- /dev/null
+++ b/src/Makefile
@@ -0,0 +1,84 @@
+# Default prefix. Can change this to system directories if needed
+PREFIX=${HOME}/software
+BINDIR=$(PREFIX)/bin
+
+# Intel compiler - uncomment if you have icpc and mkl. Comment out lines below
+# gcc
+#CXX=icpc
+#CXXFLAGS=-Wall -O3 -parallel -ipo -std=c++11
+#SEER_LDLIBS=-L../gzstream -L$(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
+MAP_LDLIBS=-L$(PREFIX)/lib -lboost_program_options
+COMBINE_LDLIBS=-L../gzstream -L$(PREFIX)/lib -lgzstream -lz -lboost_program_options
+FILTER_LDLIBS=-L$(PREFIX)/lib -lboost_program_options
+
+CPPFLAGS=-I$(PREFIX)/include -I../gzstream -I../dlib -D DLIB_NO_GUI_SUPPORT=1 -D DLIB_USE_BLAS=1 -D DLIB_USE_LAPACK=1
+
+# For static linking. To use type 'make static'
+# Requires icpc and mkl
+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
+
+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 $@
+
+kmds_static: $(KMDS_OBJECTS)
+ $(LINK.cpp) $^ $(SEER_STATIC_LDLIBS) -o $@
+
+map_back_static: $(MAP_OBJECTS)
+ $(LINK.cpp) $^ $(MAP_STATIC_LDLIBS) -o $@
+
+combineKmers_static: $(COMBINE_OBJECTS)
+ $(LINK.cpp) $^ $(COMBINE_STATIC_LDLIBS) -o $@
+
+filter_seer_static: $(FILTER_OBJECTS)
+ $(LINK.cpp) $^ $(FILTER_STATIC_LDLIBS) -o $@
+
+
+.PHONY: all static test clean install
+
diff --git a/src/combineCmdLine.cpp b/src/combineCmdLine.cpp
new file mode 100644
index 0000000..4cd6345
--- /dev/null
+++ b/src/combineCmdLine.cpp
@@ -0,0 +1,63 @@
+/*
+ * 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<std::string>()->required(), "file with tab separated sample name and kmer file")
+ ("output,o", po::value<std::string>()->required(), "output file prefix");
+
+ po::options_description other("Other options");
+ other.add_options()
+ ("min_samples", po::value<int>()->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";
+}
diff --git a/src/combineInit.cpp b/src/combineInit.cpp
new file mode 100644
index 0000000..8a0b168
--- /dev/null
+++ b/src/combineInit.cpp
@@ -0,0 +1,47 @@
+/*
+ * combineInit.cpp
+ * Functions to initialise inpit for combineKmers
+ *
+ */
+
+#include "combineKmers.hpp"
+
+std::vector<std::tuple<std::string, std::string> > 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<std::tuple<std::string, std::string> > 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;
+}
+
diff --git a/src/combineKmers.cpp b/src/combineKmers.cpp
new file mode 100644
index 0000000..caa5dd3
--- /dev/null
+++ b/src/combineKmers.cpp
@@ -0,0 +1,104 @@
+/*
+ * 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<std::tuple<std::string, std::string> > samples = readSamples(vm["samples"].as<std::string>());
+ std::unordered_map<int, std::string> sample_names;
+ size_t min_samples = checkMin(samples.size(), vm["min_samples"].as<int>());
+
+ // Open the output file before counting kmers
+ ogzstream out_file((vm["output"].as<std::string>() + ".gz").c_str());
+
+ // Map to store kmers
+ // TODO this would be neater if written with objects
+ std::unordered_map<std::string, std::vector<std::tuple<int, int>>> 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);
+}
+
diff --git a/src/combineKmers.hpp b/src/combineKmers.hpp
new file mode 100644
index 0000000..f12caaa
--- /dev/null
+++ b/src/combineKmers.hpp
@@ -0,0 +1,25 @@
+/*
+ * Header file for combineKmers.cpp
+ * Takes union of dsk counted kmers
+ *
+ */
+
+// C++ stl includes
+#include <iostream>
+#include <fstream>
+#include <vector>
+#include <tuple>
+#include <unordered_map>
+#include <iterator>
+
+// Library includes
+#include <boost/program_options.hpp>
+#include <gzstream.h>
+
+// Function prototypes
+int parseCommandLine (int argc, char *argv[], boost::program_options::variables_map& vm);
+void printHelp(boost::program_options::options_description& help);
+
+std::vector<std::tuple<std::string, std::string> > readSamples(const std::string& sample_file);
+size_t checkMin(const size_t num_samples, const int input_min_samples);
+
diff --git a/src/covar.cpp b/src/covar.cpp
new file mode 100644
index 0000000..07d0129
--- /dev/null
+++ b/src/covar.cpp
@@ -0,0 +1,28 @@
+/*
+ * covar.chpp
+ * Helper functions for covar class
+ */
+
+#include "covar.hpp"
+
+// Sorts covariates on sample name, returns only values
+std::vector<std::string> Covar::sortCovars()
+{
+ // Sort on sample name
+ std::sort(covariates.begin(), covariates.end(),
+ [](const std::tuple<std::string, std::string>& lhs, const std::tuple<std::string, std::string>& rhs)
+ {
+ return std::get<0>(lhs) < std::get<0>(rhs);
+ });
+
+ // Return a vector without sample names
+ std::vector<std::string> sorted;
+ sorted.reserve(covariates.size());
+ for (auto it = covariates.begin(); it != covariates.end(); ++it)
+ {
+ sorted.push_back(std::get<1>(*it));
+ }
+
+ return sorted;
+}
+
diff --git a/src/covar.hpp b/src/covar.hpp
new file mode 100644
index 0000000..2289806
--- /dev/null
+++ b/src/covar.hpp
@@ -0,0 +1,27 @@
+/*
+ * covar.hpp
+ * Header file for covar class
+ */
+
+#include <string>
+#include <vector>
+#include <tuple>
+#include <algorithm>
+
+// Samples have a name and phenotype
+// Covariates will be added later
+class Covar
+{
+ public:
+ // Initialisation
+ Covar() {}
+
+ // Add values, and read back
+ std::vector<std::string> sortCovars();
+ void addCovarValue (const std::string& sample, const std::string& value)
+ {covariates.push_back(std::make_tuple(sample, value));}
+
+ private:
+ std::vector<std::tuple<std::string, std::string>> covariates;
+};
+
diff --git a/src/fasta.cpp b/src/fasta.cpp
new file mode 100644
index 0000000..cf15a0d
--- /dev/null
+++ b/src/fasta.cpp
@@ -0,0 +1,113 @@
+/*
+ * 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<Mapping> Fasta::hasSeq(const std::string& search)
+{
+ std::vector<Mapping> results;
+ Mapping hit;
+
+ // Search each sequence in fasta
+ std::vector<std::string>::iterator name_it = Fasta::sequence_names.begin();
+ for (std::vector<std::string>::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<Mapping> hits = hasSeq(search);
+
+ if (hits.size() > 0)
+ {
+ // Lock use of std::out to make sure output isn't mangled
+ mtx.lock();
+ for (std::vector<Mapping>::iterator it = hits.begin(); it != hits.end(); ++it)
+ {
+ os << "\t" << Fasta::name << ":" << it->sequence_name << ":" << it->position << "-" << it->position + search.length() - 1;
+ }
+ mtx.unlock();
+ }
+}
+
diff --git a/src/fasta.hpp b/src/fasta.hpp
new file mode 100644
index 0000000..a053761
--- /dev/null
+++ b/src/fasta.hpp
@@ -0,0 +1,42 @@
+/*
+ * fasta.hpp
+ * Header file for fasta class
+ */
+
+#include <iostream>
+#include <fstream>
+#include <string>
+#include <stdexcept>
+#include <vector>
+#include <iterator>
+#include <mutex>
+
+// 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<Mapping> 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<std::string> sequences;
+ std::vector<std::string> sequence_names;
+
+ std::string name;
+};
diff --git a/src/filterCmdLine.cpp b/src/filterCmdLine.cpp
new file mode 100644
index 0000000..b4ad480
--- /dev/null
+++ b/src/filterCmdLine.cpp
@@ -0,0 +1,160 @@
+/*
+ * 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<std::string>()->required(), "file of output from seer");
+
+ po::options_description filters("Filtering options");
+ filters.add_options()
+ ("chisq", po::value<std::string>(), "minimum unadjusted p-value to output")
+ ("pval", po::value<std::string>(), "minimum adjusted p-value to output")
+ ("maf", po::value<std::string>(), "minimum maf/max 1-maf to output")
+ ("beta", po::value<std::string>(), "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<std::string>(), "field to sort on: chisq, pval, maf or beta")
+ ("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
+ {
+ 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<std::string>();
+
+ if (vm.count("chisq"))
+ {
+ processed_options.chi_filter = fractionFilter(vm["chisq"].as<std::string>());
+ }
+ else
+ {
+ processed_options.chi_filter = 0;
+ }
+
+ if (vm.count("pval"))
+ {
+ processed_options.p_filter = fractionFilter(vm["pval"].as<std::string>());
+ }
+ else
+ {
+ processed_options.p_filter = 0;
+ }
+
+ if (vm.count("maf"))
+ {
+ processed_options.maf_filter = fractionFilter(vm["maf"].as<std::string>());
+ 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<std::string>());
+ }
+ 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<std::string>();
+ }
+ 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";
+}
diff --git a/src/filter_seer.cpp b/src/filter_seer.cpp
new file mode 100644
index 0000000..7355c01
--- /dev/null
+++ b/src/filter_seer.cpp
@@ -0,0 +1,148 @@
+/*
+ * 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<Significant_kmer> 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
+ {
+ while (kmers_in)
+ {
+ // Read in each kmer
+ Significant_kmer kmer;
+ 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);
+}
+
diff --git a/src/filter_seer.hpp b/src/filter_seer.hpp
new file mode 100644
index 0000000..86e2895
--- /dev/null
+++ b/src/filter_seer.hpp
@@ -0,0 +1,34 @@
+/*
+ * Header file for filter_seer.cpp
+ * Filters significant kmers from seer
+ *
+ */
+
+// C++ stl includes
+#include <iostream>
+#include <fstream>
+#include <algorithm>
+#include <list>
+#include <iterator>
+#include <stdexcept>
+#include <math.h>
+
+// Library includes
+#include <boost/program_options.hpp>
+
+#include "significant_kmer.hpp"
+
+// 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);
+
diff --git a/src/kmds.hpp b/src/kmds.hpp
new file mode 100644
index 0000000..1bd910f
--- /dev/null
+++ b/src/kmds.hpp
@@ -0,0 +1,39 @@
+/*
+ *
+ * kmds.hpp
+ * Header file for kmds
+ *
+ */
+
+// Common headers
+#include "seercommon.hpp"
+
+// C/C++ std headers
+#include <random>
+#include <queue>
+#include <future>
+
+// 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<DistanceElement> threadDistance(std::vector<DistanceElement> element_list, const arma::mat& rectangular_matrix);
+double distanceFunction(const arma::rowvec& vec_1, const arma::rowvec& vec_2);
+
diff --git a/src/kmdsCmdLine.cpp b/src/kmdsCmdLine.cpp
new file mode 100644
index 0000000..0dac636
--- /dev/null
+++ b/src/kmdsCmdLine.cpp
@@ -0,0 +1,95 @@
+/*
+ * 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<std::string>(), "dsm kmer output file")
+ ("pheno,p", po::value<std::string>(), ".pheno metadata");
+
+ po::options_description mds("MDS options");
+ mds.add_options()
+ ("output,o", po::value<std::string>(), "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<std::string>(), "list of subsampled matrices to use in MDS. Performs only MDS; implies --no_filtering")
+ ("pc", po::value<int>()->default_value(pc_default), "number of principal coordinates to output")
+ ("size", po::value<long int>()->default_value(size_default), "number of kmers to use in MDS")
+ ("threads", po::value<int>()->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<long int>()->default_value(max_length_default), "maximum kmer length")
+ ("maf", po::value<double>()->default_value(maf_default), "minimum kmer frequency")
+ ("min_words", po::value<int>(), "minimum kmer occurences. Overrides --maf")
+ ("chisq", po::value<std::string>()->default_value(chisq_default), "p-value threshold for initial chi squared test. Set to 1 to show all");
+
+ po::options_description other("Other options");
+ other.add_options()
+ ("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
+ {
+ po::notify(vm);
+ failed = 0;
+
+ // Check input files exist, and can stat
+ if ((vm.count("kmers") && !fileStat(vm["kmers"].as<std::string>())) || (vm.count("pheno") && !fileStat(vm["pheno"].as<std::string>())))
+ {
+ 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 --combine_mds\n";
+
+ std::cerr << help << "\n";
+}
+
diff --git a/src/kmdsMain.cpp b/src/kmdsMain.cpp
new file mode 100644
index 0000000..011ad59
--- /dev/null
+++ b/src/kmdsMain.cpp
@@ -0,0 +1,264 @@
+/*
+ * 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;
+ }
+
+ // 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;
+ }
+
+ // Open .pheno file, parse into vector of samples
+ std::vector<Sample> samples;
+ std::unordered_map<std::string,int> sample_map;
+ readPheno(vm["pheno"].as<std::string>(), samples, sample_map);
+ arma::vec y = constructVecY(samples);
+ int continuous_phenotype = continuousPhenotype(samples);
+
+ 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<arma::vec> 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))
+ {
+ 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<int> 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, subsampledMatrix);
+ }
+ else
+ {
+
+ // Run metric MDS, then output to file
+ if (parameters.write_distances)
+ {
+ writeMDS(dsm_file_name, metricMDS(subsampledMatrix, parameters.pc, parameters.num_threads, distances_file_name));
+ }
+ else
+ {
+ writeMDS(dsm_file_name, 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<std::string>() << " --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<std::string>() << " --struct " << dsm_file_name
+ << " > significant_kmers.txt\n";
+ }
+ }
+ }
+ // Matrices already subsampled. Concatenate and perform MDS
+ else
+ {
+ std::string matrix_input = vm["mds_concat"].as<std::string>();
+
+ // 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<std::string>();
+ distances_file_name = vm["output"].as<std::string>() + ".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, metricMDS(readMDSList(matrix_input), mdsOptions.pc, mdsOptions.num_threads, distances_file_name));
+ }
+ else
+ {
+ writeMDS(dsm_file_name, 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";
+ }
+}
+
diff --git a/src/kmdsStruct.cpp b/src/kmdsStruct.cpp
new file mode 100644
index 0000000..534064a
--- /dev/null
+++ b/src/kmdsStruct.cpp
@@ -0,0 +1,161 @@
+/*
+ * 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<arma::mat>(matSize, matSize)
+ - 1/double(matSize)*arma::ones<arma::mat>(matSize, matSize);
+
+ // Step 3)
+ arma::mat B = -0.5 * J * P * J;
+
+ // Step 4)
+ arma::vec eigval;
+ arma::mat eigvec;
+
+ arma::eig_sym(eigval, eigvec, B);
+
+ // 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<arma::mat>(matSize, matSize);
+
+ // Time parallelisation
+ std::chrono::steady_clock::time_point start = std::chrono::steady_clock::now();
+
+ // Create queue for distance calculations
+ std::queue<std::future<std::vector<DistanceElement>>> 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<DistanceElement> 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<DistanceElement> distance_elements = distance_calculations.front().get();
+ distance_calculations.pop();
+
+ for(std::vector<DistanceElement>::iterator it = distance_elements.begin() ; it < distance_elements.end(); ++it)
+ {
+ dist(it->row, it->col) = it->distance;
+ dist(it->col, it->row) = it->distance;
+ }
+ }
+
+ // Print time taken
+ std::chrono::steady_clock::time_point end = std::chrono::steady_clock::now();
+ std::chrono::duration<double> diff = std::chrono::duration_cast<std::chrono::duration<double>>(end - start);
+ std::cerr << "Distance matrix calculated in: " << diff.count() << " s\n";
+
+ return dist;
+}
+
+std::vector<DistanceElement> threadDistance(std::vector<DistanceElement> element_list, const arma::mat& rectangular_matrix)
+{
+ for(std::vector<DistanceElement>::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);
+}
diff --git a/src/kmer.cpp b/src/kmer.cpp
new file mode 100644
index 0000000..d80debc
--- /dev/null
+++ b/src/kmer.cpp
@@ -0,0 +1,149 @@
+/*
+ * File: kmer.cpp
+ *
+ * Helper functions for the kmer class
+ *
+ */
+
+#include "kmer.hpp"
+
+// Initialisation
+Kmer::Kmer(const std::string& sequence, const std::vector<std::string>& 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)
+{
+}
+
+// Initialise without calculated information
+Kmer::Kmer(const std::string& sequence, const std::vector<std::string>& 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)
+{
+}
+
+// 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)
+{
+}
+
+// Output
+std::ostream& operator<<(std::ostream &os, const Kmer& k)
+{
+ return 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() << "\t" << k.comments();
+}
+
+// Input
+std::istream& operator>>(std::istream &is, Kmer& k)
+{
+ std::vector<std::string> 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<std::string,int>& 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;
+}
+
diff --git a/src/kmer.hpp b/src/kmer.hpp
new file mode 100644
index 0000000..6ddd569
--- /dev/null
+++ b/src/kmer.hpp
@@ -0,0 +1,55 @@
+/*
+ * kmer.hpp
+ * Header file for kmer class
+ */
+
+#include <iostream>
+#include <iomanip>
+#include <string>
+#include <vector>
+#include <unordered_map>
+
+#define ARMA_DONT_PRINT_ERRORS
+#include <armadillo>
+
+#include "significant_kmer.hpp"
+
+const std::string kmer_seq_default = "";
+const std::vector<std::string> 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<std::string>& occurrences, const double pvalue, const double beta, const double se, const double maf);
+ Kmer(const std::string& sequence, const std::vector<std::string>& 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<std::string> occurrence_vector() const { return _samples; }
+ arma::vec get_x() const { return _x; }
+ int has_x() const { return _x_set; }
+
+ // Modifying operations
+ void add_comment(const std::string& new_comment); // this is defined in kmer.cpp
+ void add_x(const std::unordered_map<std::string,int>& sample_map, const int num_samples); // this is defined in kmer.cpp
+
+ private:
+ arma::vec _x;
+ int _x_set;
+
+};
+
+// Overload output and input operators
+std::ostream& operator<<(std::ostream &os, const Kmer& k);
+
+std::istream& operator>>(std::istream &is, Kmer& k);
diff --git a/src/linearFunction.cpp b/src/linearFunction.cpp
new file mode 100644
index 0000000..b9c837b
--- /dev/null
+++ b/src/linearFunction.cpp
@@ -0,0 +1,39 @@
+/*
+ * 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);
+}
+
diff --git a/src/linkFunction.hpp b/src/linkFunction.hpp
new file mode 100644
index 0000000..65498f4
--- /dev/null
+++ b/src/linkFunction.hpp
@@ -0,0 +1,72 @@
+/*
+ * 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;
+};
diff --git a/src/logitFunction.cpp b/src/logitFunction.cpp
new file mode 100644
index 0000000..2ae3546
--- /dev/null
+++ b/src/logitFunction.cpp
@@ -0,0 +1,76 @@
+/*
+ * 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);
+}
+
diff --git a/src/mapCmdLine.cpp b/src/mapCmdLine.cpp
new file mode 100644
index 0000000..fc8af12
--- /dev/null
+++ b/src/mapCmdLine.cpp
@@ -0,0 +1,67 @@
+/*
+ * 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<std::string>()->required(), "seer kmer output file")
+ ("references,r", po::value<std::string>()->required(), "file with tab separated reference name and fasta file");
+
+ po::options_description other("Other options");
+ other.add_options()
+ ("threads", po::value<size_t>()->default_value(1), ("number of threads. Suggested: " + std::to_string(std::thread::hardware_concurrency())).c_str())
+ ("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 '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";
+}
+
diff --git a/src/mapMain.cpp b/src/mapMain.cpp
new file mode 100644
index 0000000..b4ac2a9
--- /dev/null
+++ b/src/mapMain.cpp
@@ -0,0 +1,157 @@
+/*
+ * 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<size_t>();
+ 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<Fasta> sequence_cache = readSequences(vm["references"].as<std::string>());
+
+ // Loop through significant kmers
+ std::cerr << "Now mapping significant kmers...\n";
+ std::ifstream kmer_file(vm["kmers"].as<std::string>().c_str());
+ if (!kmer_file)
+ {
+ throw std::runtime_error("Could not open kmer_file " + vm["kmers"].as<std::string>() + "\n");
+ }
+ else
+ {
+ // Set up list of threads, and mutex lock on std out
+ std::mutex out_mtx;
+ std::list<std::future<void>> threads;
+
+ Significant_kmer sig_kmer;
+ 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<std::string> search_names = sig_kmer.samples_found();
+ std::vector<std::string>::iterator search_names_it = search_names.begin();
+
+ for (std::vector<Fasta>::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)));
+ }
+ else
+ {
+ all_names_it->printMappings(std::cout, sig_kmer.sequence(), 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<Fasta> readSequences(const std::string& reference_file)
+{
+ std::vector<Fasta> 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<std::future<void>>& 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();
+ }
+ }
+}
diff --git a/src/map_back.hpp b/src/map_back.hpp
new file mode 100644
index 0000000..357a556
--- /dev/null
+++ b/src/map_back.hpp
@@ -0,0 +1,43 @@
+/*
+ *
+ * map_back.hpp
+ * Header file for map_back
+ *
+ */
+
+// C/C++/C++11 headers
+#include <iostream>
+#include <fstream>
+#include <cstdlib>
+#include <string>
+#include <iterator>
+#include <vector>
+#include <functional>
+#include <stdexcept>
+#include <future>
+#include <thread>
+#include <chrono>
+#include <list>
+#include <assert.h>
+
+// Boost headers
+#include <boost/program_options.hpp>
+
+// Classes
+#include "fasta.hpp"
+#include "significant_kmer.hpp"
+
+// Constants
+const int thread_wait = 100; //time to wait for each thread in ms
+
+// Structs
+
+// Function headers
+// mapMain.cpp
+std::vector<Fasta> readSequences(const std::string& reference_file);
+void waitForThreads(std::list<std::future<void>>& 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);
+
diff --git a/src/sample.cpp b/src/sample.cpp
new file mode 100644
index 0000000..9a551aa
--- /dev/null
+++ b/src/sample.cpp
@@ -0,0 +1,44 @@
+/*
+ * 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;
+}
diff --git a/src/sample.hpp b/src/sample.hpp
new file mode 100644
index 0000000..6812a0b
--- /dev/null
+++ b/src/sample.hpp
@@ -0,0 +1,31 @@
+/*
+ * sample.hpp
+ * Header file for sample class
+ */
+
+#include <iostream>
+#include <string>
+
+// 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);
diff --git a/src/seer.hpp b/src/seer.hpp
new file mode 100644
index 0000000..3273ae0
--- /dev/null
+++ b/src/seer.hpp
@@ -0,0 +1,45 @@
+/*
+ *
+ * seer.hpp
+ * Header file for seer package
+ *
+ */
+
+// Common headers
+//#include "seercommon.hpp"
+#include "linkFunction.hpp" // This includes seercommon.hpp
+
+// dlib headers
+#include <dlib/optimization.h>
+
+// Constants
+// Default options
+const std::string pval_default = "10e-8";
+const double convergence_limit = 10e-8;
+const unsigned int max_nr_iterations = 1000;
+
+// Starting value for beta vectors (except intercept)
+// Should be >0. This value is based on RMS in example study
+const double bfgs_start_beta = 0.12;
+
+// 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);
+
diff --git a/src/seerBinaryAssoc.cpp b/src/seerBinaryAssoc.cpp
new file mode 100644
index 0000000..ec4f128
--- /dev/null
+++ b/src/seerBinaryAssoc.cpp
@@ -0,0 +1,199 @@
+/*
+ * 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);
+}
+
+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);
+
+ 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
+ //
+ double se = pow(varCovarMat(x_design, b_vector)(1,1), 0.5);
+ 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
+ }
+ // Sometimes won't converge, use N-R instead
+ catch (std::exception& e)
+ {
+ 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<arma::vec> parameter_iterations;
+ arma::mat var_covar_mat;
+
+ // 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::vec b1 = b0;
+ var_covar_mat = inv_covar(x_design.t() * diagmat(y_pred % (arma::ones(y_pred.n_rows) - y_pred)) * x_design);
+ if (firth)
+ {
+ // Firth logistic regression
+ // See: DOI: 10.1002/sim.1047
+ arma::mat W = diagmat(y_pred * (1 - y_pred));
+ // Hat matrix
+ // Note: W is diagonal so X.t() * W * X is still sympd
+ arma::mat H = sqrt(W) * x_design * inv_covar(x_design.t() * W * x_design) * x_design.t() * sqrt(W);
+
+ arma::vec correction(y_train.n_rows);
+ correction.fill(0.5);
+
+ b1 += var_covar_mat * (y_train - y_pred + diagmat(H) % (correction - y_pred));
+ }
+ else
+ {
+ b1 += var_covar_mat * x_design.t() * (y_train - y_pred);
+ }
+ 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() << "\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
+ {
+ k.beta(parameter_iterations.back()(1));
+
+ double se = pow(var_covar_mat(1,1), 0.5);
+ k.standard_error(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
+ }
+}
+
+// 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<I.n_cols; ++i)
+ {
+ for (unsigned int j = i; j < j_max; j++)
+ {
+ I(i,j) = accu(y_trans % x.col(i) % x.col(j));
+ if (i != j)
+ {
+ I(j,i) = I(i,j); // I is symmetric - only need to calculate upper triangle
+ }
+ }
+ }
+
+ return inv_covar(I);
+}
+
+// returns y = logit(bx)
+arma::vec predictLogitProbs(const arma::mat& x, const arma::vec& b)
+{
+ const arma::vec exponents = x * b;
+ const arma::vec y = 1.0 / (1.0 + arma::exp(-exponents));
+
+ return y;
+}
diff --git a/src/seerChiFilter.cpp b/src/seerChiFilter.cpp
new file mode 100644
index 0000000..ed7b87f
--- /dev/null
+++ b/src/seerChiFilter.cpp
@@ -0,0 +1,127 @@
+/*
+ * File: seerChiFilter
+ *
+ * Implements chi^2 and welch two sample t test for seer and kmds filtering
+ *
+ */
+
+#include "seercommon.hpp"
+
+const double normalArea = pow(2*M_PI, -0.5);
+
+// Basic chi^2 test, using contingency table
+double chiTest(const arma::vec& x, const arma::vec& y)
+{
+ double chisq = 0;
+
+ // Contigency table
+ // unaffected affected
+ // present a b
+ // absent c d
+ //
+ // Use doubles for compatibility with det function in arma::mat
+ double a = 0, b = 0, c = 0, d = 0;
+
+ arma::vec::const_iterator j = y.begin();
+ for (arma::vec::const_iterator i = x.begin(); i!=x.end(); ++i)
+ {
+ if (*j == 0) {
+ if (*i == 0){
+ c++;
+ } else {
+ a++;
+ }
+ } else {
+ if (*i == 0){
+ d++;
+ } else {
+ b++;
+ }
+ }
+ j++;
+ }
+
+ arma::mat::fixed<2, 2> table = {a, b, c, d};
+#ifdef SEER_DEBUG
+ arma::Mat<int>::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");
+ }
+
+ // 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 arma::vec& x, const arma::vec& y)
+{
+ // 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 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);
+ double 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;
+}
diff --git a/src/seerCmdLine.cpp b/src/seerCmdLine.cpp
new file mode 100644
index 0000000..efb34bb
--- /dev/null
+++ b/src/seerCmdLine.cpp
@@ -0,0 +1,102 @@
+/*
+ * 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<std::string>()->required(), "dsm kmer output file")
+ ("pheno,p", po::value<std::string>()->required(), ".pheno metadata");
+
+ // kmds options
+ po::options_description covar("Covariate options");
+ covar.add_options()
+ ("struct", po::value<std::string>(), "mds values from kmds")
+ ("covar_file", po::value<std::string>(), "file containing covariates")
+ ("covar_list", po::value<std::string>(), "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<int>()->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 peform tests on all kmers input")
+ ("max_length", po::value<long int>()->default_value(max_length_default), "maximum kmer length")
+ ("maf", po::value<double>()->default_value(maf_default), "minimum kmer frequency")
+ ("min_words", po::value<int>(), "minimum kmer occurences. Overrides --maf")
+ ("positive_only", "only test words with a predicted positive effect direction")
+ ("chisq", po::value<std::string>()->default_value(chisq_default), "p-value threshold for initial chi squared test. Set to 1 to show all")
+ ("pval", po::value<std::string>()->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")
+ ("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
+ {
+ po::notify(vm);
+ failed = 0;
+
+ // Check input files exist, and can stat
+ if (!fileStat(vm["kmers"].as<std::string>()) || !fileStat(vm["pheno"].as<std::string>()))
+ {
+ failed = 1;
+ }
+ else if (vm.count("struct") && !fileStat(vm["struct"].as<std::string>()))
+ {
+ 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";
+}
+
diff --git a/src/seerCommon.cpp b/src/seerCommon.cpp
new file mode 100644
index 0000000..b3907a3
--- /dev/null
+++ b/src/seerCommon.cpp
@@ -0,0 +1,243 @@
+/*
+ * 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<Sample>& samples)
+{
+ cmdOptions verified;
+
+ verified.chi_cutoff = stod(vm["chisq"].as<std::string>());
+ verified.max_length = vm["max_length"].as<long int>();
+
+ if(vm.count("kmers"))
+ {
+ verified.kmers = vm["kmers"].as<std::string>();
+ }
+
+ if(vm.count("output"))
+ {
+ verified.output = vm["output"].as<std::string>();
+ }
+
+ if (vm.count("pval"))
+ {
+ verified.log_cutoff = stod(vm["pval"].as<std::string>());
+ }
+
+ // 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
+ if(vm.count("positive_only"))
+ {
+ verified.positive = 1;
+ }
+ else
+ {
+ verified.positive = 0;
+ }
+
+ verified.min_words = 0;
+ if (vm.count("min_words"))
+ {
+ int min_words_in = vm["min_words"].as<int>();
+ 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<double>();
+ if (maf_in >= 0)
+ {
+ verified.min_words = static_cast<unsigned int>(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<int>() > 0)
+ {
+ verified.num_threads = vm["threads"].as<int>();
+ }
+ else
+ {
+ verified.num_threads = 1;
+ }
+
+ if (vm.count("pc"))
+ {
+ if (vm["pc"].as<int>() > 0)
+ {
+ verified.pc = vm["pc"].as<int>();
+ }
+ else
+ {
+ badCommand("pc", std::to_string(vm["pc"].as<int>()));
+ }
+ }
+
+ if (vm.count("size"))
+ {
+ if (vm["size"].as<long int>() > 0)
+ {
+ verified.size = vm["size"].as<long int>();
+ }
+ else
+ {
+ badCommand("size", std::to_string(vm["size"].as<long int>()));
+ }
+ }
+
+ 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>& sample_list)
+{
+ int cont_pheno = 0;
+ for (std::vector<Sample>::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<std::string>& 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;
+}
diff --git a/src/seerContinuousAssoc.cpp b/src/seerContinuousAssoc.cpp
new file mode 100644
index 0000000..2b35b5a
--- /dev/null
+++ b/src/seerContinuousAssoc.cpp
@@ -0,0 +1,100 @@
+/*
+ * 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);
+ double se = pow((inv_covar(x_design.t()*x_design)(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
+}
+
+// returns y' = bx
+arma::vec predictLinearProbs(const arma::mat& x, const arma::vec& b)
+{
+ const arma::vec y = x * b;
+
+ return y;
+}
+
diff --git a/src/seerErr.cpp b/src/seerErr.cpp
new file mode 100644
index 0000000..dce2cd4
--- /dev/null
+++ b/src/seerErr.cpp
@@ -0,0 +1,14 @@
+/*
+ * 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");
+}
+
diff --git a/src/seerFilter.cpp b/src/seerFilter.cpp
new file mode 100644
index 0000000..085b214
--- /dev/null
+++ b/src/seerFilter.cpp
@@ -0,0 +1,82 @@
+/*
+ * 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<Sample>& samples, const arma::vec& y, const int continuous_phenotype)
+{
+ 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;
+
+ try // Some chi^2 tests may diverge - proceed anyway for now
+ {
+ pass = passStatsFilters(k.get_x(), y, filterOptions.chi_cutoff, continuous_phenotype, filterOptions.positive);
+ }
+ 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(const arma::vec& x, const arma::vec& y, const double chi_cutoff, const int continuous_phenotype, const int positive_only)
+{
+ int passed = 1;
+
+ if (continuous_phenotype)
+ {
+ if (welchTwoSamplet(x, y) > chi_cutoff)
+ {
+ passed = 0;
+ }
+ }
+ else
+ {
+ // Test positive effects only
+ // i.e. having kmer associates with having phenotype
+ if (positive_only && dot(y - x, y - x) <= 0.5*y.n_rows)
+ {
+ passed = 0;
+ }
+ else if (chiTest(x, y) > chi_cutoff)
+ {
+ passed = 0;
+ }
+ }
+
+ return passed;
+}
diff --git a/src/seerIO.cpp b/src/seerIO.cpp
new file mode 100644
index 0000000..82e39a2
--- /dev/null
+++ b/src/seerIO.cpp
@@ -0,0 +1,317 @@
+/*
+ * 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
+ * <class>.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<Sample>& samples, std::unordered_map<std::string,int>& sample_map)
+{
+ std::ifstream ist(filename.c_str());
+
+ if (!ist)
+ {
+ throw std::runtime_error("Could not open pheno file " + filename + "\n");
+ }
+
+ Sample s;
+ int sample_index = 0;
+ while (ist >> s)
+ {
+ samples.push_back(s);
+
+ sample_map[s.iid()] = sample_index;
+ sample_index++;
+ }
+
+ // Always keep samples sorted, for consistency between programs
+ std::sort(samples.begin(), samples.end(), Sample::compareSamples);
+}
+
+// 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<Sample>& samples)
+{
+ arma::vec y;
+ y.zeros(samples.size());
+
+ for (unsigned int i = 0; i < samples.size(); ++i)
+ {
+ y[i] = samples[i].pheno();
+ }
+
+ return y;
+}
+
+void writeMDS(const std::string& file_name, const arma::mat& MDS)
+{
+ MDS.save(file_name, arma::hdf5_binary);
+}
+
+void writeDistances(const std::string& file_name, const arma::mat& distances)
+{
+ distances.save(file_name, arma::csv_ascii);
+}
+
+arma::mat readMDS(const std::string& file_name)
+{
+ arma::mat MDS;
+
+ if (fileStat(file_name))
+ {
+ MDS.load(file_name, arma::hdf5_binary);
+ }
+ else
+ {
+ throw std::runtime_error("Problem with loading MDS input file " + file_name);
+ }
+
+ 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, readMDS(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<std::tuple<int,bool>> column_list = parseCovarColumns(columns);
+
+ // Open file, and read in required columns
+ std::vector<Covar> 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<skip_cols; ++i)
+ {
+ line_stream >> 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<std::tuple<int,bool>> parseCovarColumns(const std::string& columns)
+{
+ std::vector<std::tuple<int,bool>> column_list; // A vector of column numbers, and whether they
+ // are categorical and need
+ // dummy encoding
+ std::string col_buf;
+ std::stringstream ss(columns);
+ while(std::getline(ss, col_buf, ','))
+ {
+ if (std::regex_match(col_buf, q_covar_regex))
+ {
+ auto new_col = std::make_tuple(stoi(std::regex_replace(col_buf, q_covar_regex, "$1")), 0);
+ column_list.push_back(new_col);
+ }
+ else if (std::regex_match(col_buf, covar_regex))
+ {
+ auto new_col = std::make_tuple(stoi(std::regex_replace(col_buf, covar_regex, "$1")), 1);
+ column_list.push_back(new_col);
+ }
+ else
+ {
+ std::cerr << "Warning: couldn't parse covariate column " + col_buf << std::endl;
+ }
+ }
+
+ if (column_list.size() == 0)
+ {
+ throw std::runtime_error("No covariate columns selected, but file provided");
+ }
+
+ // Sort by column number. Use a lambda function
+ std::sort(column_list.begin(), column_list.end(),
+ [](const std::tuple<int,bool>& a, const std::tuple<int,bool>& b)
+ {
+ return std::get<0>(a) < std::get<0>(b);
+ });
+
+ return column_list;
+}
+
+// Converts categorical variates into columns using dummy encoding
+arma::mat encodeDummy(const std::vector<std::string>& in_col)
+{
+ // Find the possible categories, and put them into an associative array (unordered
+ // map)
+ std::vector<std::string> col_copy = in_col;
+ std::sort(col_copy.begin(), col_copy.end());
+ auto unique_it = std::unique(col_copy.begin(), col_copy.end());
+
+ std::unordered_map<std::string, int> dummy_map;
+ unsigned int dummy_col = 0;
+ for (auto cat_it = col_copy.begin(); cat_it != unique_it; ++cat_it)
+ {
+ dummy_map[*cat_it] = dummy_col++;
+ }
+
+ // For each variable, find the category and code this column as 1
+ // There will be n-1 categories, as the control is coded as all zeros
+ arma::mat out_mat(in_col.size(), dummy_map.size() - 1);
+ for (unsigned int i = 0; i < in_col.size(); ++i)
+ {
+ unsigned int true_col = dummy_map[in_col[i]];
+ for (unsigned int j = 0; j < dummy_map.size() - 1; ++j)
+ {
+ int out_val = 0;
+ if (true_col > 0 && true_col - 1 == j)
+ {
+ out_val = 1;
+ }
+ out_mat(i, j) = out_val;
+ }
+ }
+
+ return out_mat;
+}
+
+// Check for file existence
+int fileStat(const std::string& filename)
+{
+ struct stat buffer;
+ int success = 1;
+
+ if (stat (filename.c_str(), &buffer) != 0)
+ {
+ std::cerr << "Can't stat input file: " << filename << "\n";
+
+ success = 0;
+ }
+
+ return success;
+}
+
diff --git a/src/seerMain.cpp b/src/seerMain.cpp
new file mode 100644
index 0000000..b35fbbb
--- /dev/null
+++ b/src/seerMain.cpp
@@ -0,0 +1,209 @@
+/*
+ * File: seerMain.cpp
+ *
+ * Reads command line input to seer, and controls relevant functions
+ *
+ */
+
+#include "seer.hpp"
+
+int main (int argc, char *argv[])
+{
+ // Program description
+ std::cerr << "seer: sequence element enrichment analysis\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: seer -k dsm.txt.gz -p data.pheno --struct mds.dsm.txt.gz\n\n"
+ << "For full option details run seer -h\n";
+ return 0;
+ }
+ else if (parseCommandLine(argc, argv, vm))
+ {
+ return 1;
+ }
+
+ // Open .pheno file, parse into vector of samples
+ std::vector<Sample> samples;
+ std::unordered_map<std::string,int> sample_map;
+ readPheno(vm["pheno"].as<std::string>(), samples, sample_map);
+ arma::vec y = constructVecY(samples);
+ int continuous_phenotype = continuousPhenotype(samples);
+
+ // Get mds values
+ arma::mat mds;
+ int use_mds = 0;
+ if (vm.count("struct"))
+ {
+ mds = readMDS(vm["struct"].as<std::string>());
+ use_mds = 1;
+
+ if (mds.n_rows != samples.size())
+ {
+ throw std::runtime_error("Number of rows in MDS matrix does not match number of samples");
+ }
+ }
+
+ // Set up covariates
+ if (vm.count("covar_file") && vm.count("covar_list"))
+ {
+ // File reading/parsing may fail
+ try
+ {
+ arma::mat covariate_matrix =
+ parseCovars(vm["covar_file"].as<std::string>(), vm["covar_list"].as<std::string>());
+
+ if (covariate_matrix.n_rows != samples.size())
+ {
+ throw std::runtime_error("Covariate row size does not match number of samples");
+ }
+
+ if (use_mds)
+ {
+ mds = arma::join_rows(mds, covariate_matrix);
+ }
+ else
+ {
+ mds = covariate_matrix;
+ use_mds = 1;
+ }
+ }
+ catch (std::exception& e)
+ {
+ std::cerr << "Could not process covariates: " << std::endl;
+ std::cerr << e.what() << std::endl;
+ }
+ }
+
+ // Disambiguate overloaded logistic functions by the type of parameter they
+ // take
+ void (*mdsLogitFunc)(Kmer&, const arma::vec&, const arma::mat&) = &logisticTest;
+ void (*logitFunc)(Kmer&, const arma::vec&) = &logisticTest;
+
+ void (*mdsLinearFunc)(Kmer&, const arma::vec&, const arma::mat&) = &linearTest;
+ void (*linearFunc)(Kmer&, const arma::vec&) = &linearTest;
+
+ // Error check command line options
+ cmdOptions parameters = verifyCommandLine(vm, samples);
+
+ // Open the dsm kmer ifstream, and read through the whole thing
+ igzstream kmer_file;
+ openDsmFile(kmer_file, parameters.kmers);
+
+ // Write a header
+ std::cout << "sequence\tmaf\tunadj_p_val\tp_val\tbeta\tse\tcomments";
+ if (parameters.print_samples)
+ {
+ std::cout << "samples_present";
+ }
+ std::cout << std::endl;
+
+ while (kmer_file)
+ {
+ // Parse a set of dsm lines
+ std::vector<Kmer> kmer_lines;
+ kmer_lines.reserve(parameters.num_threads);
+
+ Kmer k;
+ while(kmer_lines.size() < parameters.num_threads && kmer_file)
+ {
+ kmer_file >> k;
+ if (kmer_file)
+ {
+ k.add_x(sample_map, samples.size());
+
+ // apply filters here
+ if (!parameters.filter)
+ {
+ kmer_lines.push_back(k);
+ }
+ else if (passFilters(parameters, k, samples, y, continuous_phenotype))
+ {
+#ifdef SEER_DEBUG
+ std::cerr << "kmer " + k.sequence() + " seems significant\n";
+#endif
+ kmer_lines.push_back(k);
+ }
+ }
+ }
+
+ // Thread from here...
+ std::vector<std::thread> threads;
+ threads.reserve(kmer_lines.size());
+
+ for (unsigned int i = 0; i<kmer_lines.size(); ++i)
+ {
+ // Association test
+ // Note threads must be passed values as they are copied
+ // std::reference_wrapper allows references to be passed
+ if (use_mds)
+ {
+ if (continuous_phenotype)
+ {
+ threads.push_back(std::thread(mdsLinearFunc, std::ref(kmer_lines[i]), std::cref(y), std::cref(mds)));
+ }
+ else
+ {
+ threads.push_back(std::thread(mdsLogitFunc, std::ref(kmer_lines[i]), std::cref(y), std::cref(mds)));
+ }
+ }
+ else
+ {
+ if (continuous_phenotype)
+ {
+ threads.push_back(std::thread(linearFunc, std::ref(kmer_lines[i]), std::cref(y)));
+ }
+ else
+ {
+ threads.push_back(std::thread(logitFunc, std::ref(kmer_lines[i]), std::cref(y)));
+ }
+ }
+ }
+
+ for (unsigned int i = 0; i<threads.size(); ++i)
+ {
+ // Rejoin in order
+ threads[i].join();
+
+ // Print in order when all threads complete
+ if (kmer_lines[i].p_val() < parameters.log_cutoff)
+ {
+ // Caclculate chisq value if not already done so in filtering
+ if (kmer_lines[i].unadj() == kmer_chi_pvalue_default)
+ {
+ if (continuous_phenotype)
+ {
+ kmer_lines[i].unadj_p_val(welchTwoSamplet(kmer_lines[i].get_x(), y));
+ }
+ else
+ {
+ kmer_lines[i].unadj_p_val(chiTest(kmer_lines[i].get_x(), y));
+ }
+ }
+
+ std::cout << kmer_lines[i];
+ if (parameters.print_samples)
+ {
+ std::vector<std::string> samples_found = kmer_lines[i].occurrence_vector();
+ std::cout << "\t";
+ // Doing this for all samples leaves trailing whitespace, so
+ // write the last sample separately
+ if (samples_found.size() > 1)
+ {
+ std::copy(samples_found.begin(), samples_found.end() - 1, std::ostream_iterator<std::string>(std::cout, "\t"));
+ }
+ std::cout << samples_found.back();
+ }
+ std::cout << "\n";
+
+ }
+ }
+ // ...to here
+ }
+
+ std::cerr << "Done.\n";
+}
+
diff --git a/src/seercommon.hpp b/src/seercommon.hpp
new file mode 100644
index 0000000..32f81a6
--- /dev/null
+++ b/src/seercommon.hpp
@@ -0,0 +1,117 @@
+/*
+ *
+ * seercommon.hpp
+ * Header file for seercommon
+ * Shared functions between seer and kmds
+ *
+ */
+
+// C/C++/C++11 headers
+#include <iostream>
+#include <fstream>
+#include <cmath>
+#include <cstdlib>
+#include <string>
+#include <algorithm>
+#include <chrono>
+#include <iterator>
+#include <vector>
+#include <unordered_map>
+#include <thread>
+#include <exception>
+#include <sys/stat.h>
+#include <regex>
+
+// gzstream headers
+#include <gzstream.h>
+
+// Boost headers
+#include <boost/program_options.hpp>
+#include <boost/math/distributions/normal.hpp>
+#include <boost/math/distributions/students_t.hpp>
+
+// Armadillo/dlib headers
+#define ARMA_DONT_PRINT_ERRORS
+#include <armadillo>
+#include <dlib/matrix.h>
+
+// Classes
+#include "kmer.hpp"
+#include "sample.hpp"
+#include "covar.hpp"
+
+// Constants
+// Default options
+const double maf_default = 0.01;
+const long int max_length_default = 100;
+const std::string chisq_default = "10e-5";
+
+typedef dlib::matrix<double,0,1> column_vector;
+
+// Structs
+struct cmdOptions
+{
+ double log_cutoff;
+ double chi_cutoff;
+
+ long int max_length;
+ long int size;
+ int filter;
+ int positive;
+ int pc;
+ int print_samples;
+ int write_distances;
+ unsigned int num_threads;
+ size_t min_words;
+ size_t max_words;
+
+ std::string pheno;
+ std::string kmers;
+ std::string output;
+};
+
+// Function headers
+// seerCommon.cpp
+cmdOptions verifyCommandLine(boost::program_options::variables_map& vm, const std::vector<Sample>& samples);
+void verifyMDSOptions(cmdOptions& verified, boost::program_options::variables_map& vm);
+
+arma::vec dlib_to_arma(const column_vector& dlib_vec);
+column_vector arma_to_dlib(const arma::vec& arma_vec);
+arma::mat vecToMat(const std::vector<std::string>& in_col);
+void normaliseMatCols(arma::mat& matrix_in);
+
+int continuousPhenotype (const std::vector<Sample>& sample_list);
+
+arma::mat inv_covar(arma::mat A);
+
+// seerErr headers
+void badCommand(const std::string& command, const std::string& value);
+
+// seerIO headers
+void readPheno(const std::string& filename, std::vector<Sample>& samples, std::unordered_map<std::string,int>& sample_map);
+void openDsmFile(igzstream& dsm_file, const std::string& file_name);
+
+arma::vec constructVecY(const std::vector<Sample>& samples);
+arma::vec constructVecX(const Kmer& k, const std::vector<Sample>& samples);
+
+void writeMDS(const std::string& file_name, const arma::mat& MDS);
+void writeDistances(const std::string& file_name, const arma::mat& distances);
+arma::mat readMDS(const std::string& file_name);
+arma::mat readMDSList(const std::string& filename);
+
+arma::mat parseCovars(const std::string& file, const std::string& columns);
+std::vector<std::tuple<int,bool>> parseCovarColumns(const std::string& columns);
+arma::mat encodeDummy(const std::vector<std::string>& in_col);
+
+int fileStat(const std::string& filename);
+
+// seerFilter headers
+int passFilters(const cmdOptions& filterOptions, Kmer& k, const std::vector<Sample>& samples, const arma::vec& y, const int continuous_phenotype);
+int passBasicFilters(const Kmer& k, const int max_length, const int min_words, const int max_words);
+int passStatsFilters(const arma::vec& x, const arma::vec& y, const double chi_cutoff, const int continuous_phenotype, const int positive_only);
+
+// seerChiFilter headers
+double chiTest(const arma::vec& x, const arma::vec& y);
+double welchTwoSamplet(const arma::vec& x, const arma::vec& y);
+double normalPval(double testStatistic);
+
diff --git a/src/significant_kmer.cpp b/src/significant_kmer.cpp
new file mode 100644
index 0000000..ccf2223
--- /dev/null
+++ b/src/significant_kmer.cpp
@@ -0,0 +1,133 @@
+/*
+ * File: significant_kmer.cpp
+ *
+ * Helper functions for the significant kmer class
+ * Reads input into class
+ *
+ */
+
+#include "significant_kmer.hpp"
+
+Significant_kmer::Significant_kmer()
+{
+}
+
+Significant_kmer::Significant_kmer(const std::string& word, const std::vector<std::string>& samples, const double maf, const double unadj_p, const double adj_p, const double beta, const double se, const std::string& comments)
+ :_word(word), _samples(samples), _maf(maf), _unadj_p(unadj_p), _adj_p(adj_p), _beta(beta), _se(se), _comment(comments)
+{
+}
+
+// Fills significant kmer object from seer output file
+// Sample vector is returned sorted
+std::istream& operator>>(std::istream &is, Significant_kmer& sk)
+{
+ double maf, unadj_p, adj_p, beta, se;
+ std::string sequence, sample, line_in, comments = "";
+ std::vector<std::string> sample_list;
+
+ // Read the line convert to stringstream, extract sequence and stats fields
+ std::getline(is, line_in);
+ std::stringstream line_stream(line_in);
+
+ line_stream >> sequence;
+
+ line_stream >> maf;
+ line_stream >> unadj_p;
+ line_stream >> adj_p;
+ line_stream >> beta;
+ line_stream >> se;
+
+ line_stream >> comments;
+
+ // Read remainder of line, which is sample names, in the same way they were
+ // written
+ std::copy(std::istream_iterator<std::string>(line_stream), std::istream_iterator<std::string>(), std::back_inserter(sample_list));
+
+ // Ensure vector remains sorted on sample name
+ std::sort(sample_list.begin(), sample_list.end());
+ sk = Significant_kmer(sequence, sample_list, maf, unadj_p, adj_p, beta, se, comments);
+
+ return is;
+}
+
+// Print fields tab sep, identical to input. Doesn't print newline
+std::ostream& operator<<(std::ostream &os, const Significant_kmer& k)
+{
+ os << k.sequence() << "\t"
+ << std::fixed << std::setprecision(3) << k.maf() << "\t"
+ << std::scientific << k.unadj() << "\t" << k.p_val() << "\t" << k.beta() << "\t" << k.se()
+ << k.comments();
+
+ std::vector<std::string> samples_found = k.samples_found();
+ std::copy(samples_found.begin(), samples_found.end() - 1, std::ostream_iterator<std::string>(os, "\t"));
+ os << samples_found.back();
+
+ return os;
+}
+
+sortSigKmer::sortSigKmer()
+ :_sort_field(0)
+{
+}
+
+sortSigKmer::sortSigKmer(const std::string& sort_field)
+ :_sort_field(0)
+{
+ this->addSortField(sort_field);
+}
+
+
+// Sets the sort field for sig kmers
+void sortSigKmer::addSortField(const std::string& sort_field)
+{
+ if (sort_field == "maf")
+ {
+ this->_sort_field = 1;
+ }
+ else if (sort_field == "chisq")
+ {
+ this->_sort_field = 2;
+ }
+ else if (sort_field == "pval")
+ {
+ this->_sort_field = 3;
+ }
+ else if (sort_field == "beta")
+ {
+ this->_sort_field = 4;
+ }
+ else if (sort_field == "sequence")
+ {
+ this->_sort_field = 5;
+ }
+ else
+ {
+ this->_sort_field = 3;
+ }
+}
+
+bool sortSigKmer::operator() (const Significant_kmer& sk1, const Significant_kmer& sk2) const
+{
+ bool compare;
+ switch (_sort_field)
+ {
+ case 1:
+ compare = sk1.maf() < sk2.maf();
+ break;
+ case 2:
+ compare = sk1.unadj() < sk2.unadj();
+ break;
+ case 3:
+ compare = sk1.p_val() < sk2.p_val();
+ break;
+ case 4:
+ compare = sk1.beta() < sk2.beta();
+ break;
+ case 5:
+ compare = sk1.sequence().length() < sk2.sequence().length();
+ break;
+ default:
+ compare = sk1.p_val() < sk2.p_val();
+ }
+ return compare;
+}
diff --git a/src/significant_kmer.hpp b/src/significant_kmer.hpp
new file mode 100644
index 0000000..c9df3de
--- /dev/null
+++ b/src/significant_kmer.hpp
@@ -0,0 +1,73 @@
+/*
+ * significant_kmer.hpp
+ * Header file for significant_kmer class
+ *
+ */
+
+#include <iostream>
+#include <iomanip>
+#include <sstream>
+#include <string>
+#include <vector>
+#include <iterator>
+#include <algorithm>
+
+// kmers have a sequence, and a list of samples they appear in
+class Significant_kmer
+{
+ public:
+ // Initialisation
+ Significant_kmer();
+ Significant_kmer(const std::string& word, const std::vector<std::string>& samples, const double maf, const double unadj_p, const double adj_p, const double beta, const double se, const std::string& comments);
+
+ // nonmodifying operations
+ std::vector<std::string> samples_found() const { return _samples; }
+ std::string sequence() const { return _word; }
+ double maf() const { return _maf; }
+ double unadj() const { return _unadj_p; }
+ double p_val() const { return _adj_p; }
+ double beta() const { return _beta; }
+ double se() const { return _se; }
+ std::string comments() const { return _comment; }
+
+ // Modifying operations
+ void p_val(const double pvalue) { _adj_p = pvalue; }
+ void unadj_p_val(const double chi_pvalue) { _unadj_p = chi_pvalue; }
+ void beta(const double b) { _beta = b; }
+ void standard_error(const double se) { _se = se; }
+ void set_maf(const double maf) { _maf = maf; }
+
+
+ protected:
+ std::string _word;
+ std::vector<std::string> _samples;
+
+ double _maf;
+ double _unadj_p;
+ double _adj_p;
+ double _beta;
+ double _se;
+ std::string _comment;
+};
+
+class sortSigKmer
+{
+ public:
+ sortSigKmer();
+ sortSigKmer(const std::string& sort_field);
+
+ void addSortField(const std::string& sort_field);
+
+ // Overload for sort
+ bool operator() (const Significant_kmer& sk1, const Significant_kmer& sk2) const;
+ explicit operator bool() const { return (_sort_field > 0); };
+
+ private:
+ int _sort_field;
+};
+
+// Overload input and output operators
+std::istream& operator>>(std::istream &is, Significant_kmer& sk);
+
+std::ostream& operator<<(std::ostream &os, const Significant_kmer& k);
+
diff --git a/test/Makefile b/test/Makefile
new file mode 100755
index 0000000..074d155
--- /dev/null
+++ b/test/Makefile
@@ -0,0 +1,10 @@
+all:
+ echo "Tests not yet implemented" > tests_passed.txt
+
+clean:
+ $(RM) *.o ~* tests_passed.txt
+
+test: all
+
+.PHONY: all clean install test
+
diff --git a/test/example.pheno b/test/example.pheno
new file mode 100644
index 0000000..066dbe4
--- /dev/null
+++ b/test/example.pheno
@@ -0,0 +1,3069 @@
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diff --git a/test/example_kmers.gz b/test/example_kmers.gz
new file mode 100644
index 0000000..ea1b273
Binary files /dev/null and b/test/example_kmers.gz differ
diff --git a/test/generate_test.pl b/test/generate_test.pl
new file mode 100755
index 0000000..bcc7315
--- /dev/null
+++ b/test/generate_test.pl
@@ -0,0 +1,49 @@
+#!/usr/bin/perl -w
+
+use strict;
+use warnings;
+
+my $sample_file = $ARGV[0];
+my $sites = $ARGV[1];
+
+my (@samples, %phenotypes);
+open(PHENO, $sample_file) || die("Could not open $sample_file\n");
+while (my $pheno_line = <PHENO>)
+{
+ chomp $pheno_line;
+ my ($FID, $IID, $pheno) = split("\t", $pheno_line);
+
+ push(@samples, "$IID:1");
+ $phenotypes{"$IID:1"} = $pheno;
+}
+close PHENO;
+
+# Sites increase in minor allele count (mac)
+for (my $site_idx = 1; $site_idx <= $sites; $site_idx++)
+{
+ my @sampled_samples;
+
+ my $mac = int(scalar(@samples) * ($site_idx/$sites));
+
+ # Reservoir sampler over all samples for each site
+ for (my $sample_idx = 0; $sample_idx < scalar(@samples); $sample_idx++)
+ {
+ if (scalar(@sampled_samples) < $mac)
+ {
+ push(@sampled_samples, $samples[$sample_idx]);
+ }
+ else
+ {
+ my $r = int(rand($sample_idx));
+ if ($r < $mac)
+ {
+ $sampled_samples[$r] = $samples[$sample_idx];
+ }
+ }
+ }
+
+ print join(" ", $site_idx, "5.033430 0.161246 100 0 100 0.151841 100 |", @sampled_samples) . "\n";
+}
+
+exit(0);
+
diff --git a/test/subset.pheno b/test/subset.pheno
new file mode 100644
index 0000000..223df0c
--- /dev/null
+++ b/test/subset.pheno
@@ -0,0 +1,200 @@
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--
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