Evaporative Cooling with ReliefF and Random Jungle
C++ Shell C Other
Switch branches/tags
Nothing to show
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Failed to load latest commit information.
benchmarks
build-aux
doc/latex
examples
m4
nbproject
src
.gitignore
AUTHORS
COPYING
ChangeLog
INSTALL
Makefile-cppec.mk
Makefile.am
Makefile.in
NEWS
README
README.md
acinclude.m4
aclocal.m4
aminclude.am
bootstrap.sh
config.h
config.h.in
configure
configure.ac
doxygen.cfg

README.md

Evaporative Cooling (EC)

A feature selection tool for GWAS and other biological data

Description

EC is a free, open-source command-line tool for analysis of GWAS (SNP) and other types of biological data. Several modes are available for various types of analysis, including:

Evaporative Cooling (EC) is C++ library that provides a flexible feature selection algorithm for SNPs and quantitative data, using ReliefF and Random Jungle for interactions and main effects, respectively. EC is also available as a standalone tool.

EC is being developed by the In Silico Research Group at the Tandy School of Computer Science of the University of Tulsa. Our research is sponsored by the NIH and William K. Warren foundation. For more details, visit our research website.

Dependencies

  • EC library, available as a source release on the EC project page, and its dependencies:

    • Random Jungle

    • gfortran, sometimes installed alongside compiler tools

    • GNU Scientific library (libgsl)

    • libxml2

  • Boost system, filesystem, and program-options libraries

  • The libz/zlib compression library is required, but this is installed by default on most Unix systems. In MinGW libz is installed via mingw-get.

  • OpenMP is required to take advantage of the parallelized tree growth in Random Jungle and distance matrix calculations for ReliefF. This is another library typically installed alongside the compiler toolchain.

Compilation Environment and Instructions

To compile this code, a GNU toolchain and suitable environment are required. GNU g++ has been used to successfully compile the code.

We have successfully built and run EC on:

  • Linux (64-bit Ubuntu) (gcc-4.6)
  • Mac (10.6 - 10.7) (gcc-4.2.1)
  • Windows 7 (32-bit) using the MinGW compiler system (gcc-4.6)

To build EC, first run the bootstrap script

./bootstrap.sh

Ignore any extraneous warnings. This calls autoreconf and generates the configure script. From this point, a standard

./configure && make && sudo make install

will generate the Makefile, compile and link the code, and copy the objects to the installation directory (default of /usr/local). As is convention, headers are installed in $PREFIX/include, binary in $PREFIX/bin, and the library in $PREFIX/lib.

The resulting binary src/ec_static.exe will run as a command-line tool.

Usage

Allowed options:
  --help                                produce help message
  --verbose                             verbose output
  --convert                             convert data set to data set - no ec
  -T [ --optimize-temp ]                optimize coupling constant T
  -c [ --config-file ] arg              read configuration options from file - 
                                        command line overrides these
  -s [ --snp-data ] arg                 read SNP attributes from genotype 
                                        filename: txt, ARFF, plink (map/ped, 
                                        binary, raw)
  --snp-file-type arg                   Ignore file extension and use type: 
                                        textwhitesp, wekaarff, plinkped, 
                                        plinkbed, plinkraw, mayogeo, birdseed
  -n [ --numeric-data ] arg             read continuous attributes from 
                                        PLINK-style covar file
  -X [ --numeric-transform ] arg        perform numeric transformation: 
                                        normalize, standardize, zscore, log, 
                                        sqrt
  -a [ --alternate-pheno-file ] arg     specifies an alternative 
                                        phenotype/class label file; one value 
                                        per line
  -g [ --ec-algorithm-steps ] arg (=all)
                                        EC steps to run (all|rj|rf)
  -t [ --ec-num-target ] arg (=0)       EC N_target - target number of 
                                        attributes to keep
  -r [ --ec-iter-remove-n ] arg (=0)    Evaporative Cooling number of 
                                        attributes to remove per iteration
  -p [ --ec-iter-remove-percent ] arg   Evaporative Cooling precentage of 
                                        attributes to remove per iteration
  -O [ --out-dataset-filename ] arg     write a new tab-delimited data set with
                                        EC filtered attributes
  -o [ --out-files-prefix ] arg (=ec_run)
                                        use prefix for all output files
  --snp-metric arg (=gm)                metric for determining the difference 
                                        between subjects (gm|am|nca|nca6)
  -B [ --snp-metric-nn ] arg (=gm)      metric for determining the difference 
                                        between subjects (gm|am|nca|nca6|km)
  -W [ --snp-metric-weights ] arg (=gm) metric for determining the difference 
                                        between SNPs (gm|am|nca|nca6)
  -N [ --numeric-metric ] arg (=manhattan)
                                        metric for determining the difference 
                                        between numeric attributes 
                                        (manhattan=|euclidean)
  -R [ --rj-run-mode ] arg (=1)         Random Jungle run mode: 1 
                                        (default=library call) / 2 (system 
                                        call)
  -j [ --rj-num-trees ] arg (=1000)     Random Jungle number of trees to grow
  --rj-mtry arg (=0)                    Random Jungle size of randomly chosen 
                                        variable sets, DEFAULT: sqrt(ncol)
  --rj-nimpvar arg (=1)                 Random Jungle only necessary if 
                                        backsel>0. SIZE=[1-...] how many 
                                        variable should remain
  --rj-impmeasure arg (=1)              Random Jungle importance method (see RJ
                                        docs)
  --rj-backsel arg (=0)                 Random Jungle backward elimination (see
                                        RJ docs)
  -Y [ --rj-tree-type ] arg (=1)        Random Jungle tree type: 1 (default)-5 
                                        (see RJ docs)
  -M [ --rj-memory-mode ] arg (=0)      Random Jungle memory mode: 0 
                                        (default=double) / 1 (float) / 2 (char)
  -x [ --snp-exclusion-file ] arg       file of SNP names to be excluded
  -k [ --k-nearest-neighbors ] arg (=10)
                                        set k nearest neighbors
  -m [ --number-random-samples ] arg (=0)
                                        number of random samples (0=all|1 <= n 
                                        <= number of samples)
  -b [ --weight-by-distance-method ] arg (=equal)
                                        weight-by-distance method 
                                        (equal|one_over_k|exponential)
  --weight-by-distance-sigma arg (=2)   weight by distance sigma
  -d [ --diagnostic-tests ] arg         performs diagnostic tests and sends 
                                        output to filename without running EC
  -D [ --diagnostic-levels-file ] arg   write diagnostic attribute level counts
                                        to filename
  --dge-counts-data arg                 read digital gene expression counts 
                                        from text file
  --dge-norm-factors arg                read digital gene expression 
                                        normalization factors from text file
  --birdseed-snps-data arg              read SNP data from a birdseed formatted
                                        file
  --birdseed-phenos-data arg            read birdseed subjects phenotypes from 
                                        a text file
  --birdseed-subjects-labels arg        read subject labels from filename to 
                                        override names from data file
  --birdseed-include-snps arg           include the SNP IDs listed in the text 
                                        file
  --birdseed-exclude-snps arg           exclude the SNP IDs listed the text 
                                        file
  --distance-matrix arg                 create a distance matrix for the loaded
                                        samples and exit
  --gain-matrix arg                     create a GAIN matrix for the loaded 
                                        samples and exit
  --dump-titv-file arg                  file for dumping SNP 
                                        transition/transversion information

All commands will include an input file (-s/--snp-data), and, optionally, an output file prefix (-o/--output-files-prefix).

To perform a standard, all-default-parameters analysis,

./ec_static -s snpdata.ped -o result

This will use genotype/phenotype information from snpdata.ped, a PLINK plaintext GWAS file, in the feature selection. All of the output files produced will be prepended with 'result'.

This produces a file called result.ec, in which the SNPs are ranked in descending order.

For additional examples, see the EC page on our research website.

Contributors

See AUTHORS file.

References

B.A. McKinney, J.E. Crowe, Jr., J. Guo, and D. Tian, ÒCapturing the spectrum of interaction effects in genetic association studies by simulated evaporative cooling network analysis,Ó PLoS Genetics. 5(3): e1000432. doi:10.1371/journal.pgen.1000432; 2009.

McKinney, B.A., Reif, D.M., White, B.C., Crowe, J.E., Moore, J.H. Evaporative cooling feature selection for genotypic data involving interactions. Bioinformatics 23, 2113-2120 (2007). [PubMed]