Looking for the latest version? Download MOSGWA 1.1.1 (263.5 kB)
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MOSGWA.1.1.1.zip 2014-04-30 263.5 kB 11 weekly downloads
README.txt 2014-03-03 4.3 kB 11 weekly downloads
example.1.1.0.zip 2014-03-03 3.9 kB 11 weekly downloads
MOSGWA.1.1.0.zip 2014-03-03 261.5 kB 11 weekly downloads
MOSGWA.1.0.2.zip 2014-02-19 264.0 kB 11 weekly downloads
example1.0.2.zip 2013-12-17 3.6 kB 11 weekly downloads
Welcome to MOSGWA MOSGWA is a tool meant to be operated from the Linux command line. It is planned to make it compile on other operating systems. In the top directory, you see: README.txt This overview information INSTALL.txt Build instructions CHANGES.txt Change log COPYING.txt GNU General Public License, which applies to this software CMakeLists.txt Top level configuration file for build with the cmake tool src Contains the C++ source and header files and a suitable makefile Installation: Follow the steps described in INSTALL.txt. Running: MOSGWA takes its configuration from files given on the command line. You run MOSGWA with the command syntax: MOSGWA config_file_name[s] Config files look similar to Windows INI-files. They determine files used, and any parameters for the search strategy, in cases when the default values are not deemed optimal. The following is an example: [input] plink_files = "random" [data] trait_index = 0 [output] files = "random_out" [single_marker] test = cochran_armitage [model_preselection] mBIC_expected_causal_SNPs = 25 [model_selection] selection_criterium = mBIC2 nSNPKriterium = 5000 fast_multi_forward = false You see the sections of the file headed by section headings, which are enclosed in square brackets []. Within each section, the names of the parameters are unique. You set parameters with an equals sign. MOSGWA currently uses four types of parameters. boolean (true or false) integer (e.g. 0, 1, 2) floating point (e.g. -9.3e3) string (e.g. "random_out") The [input] section must specify where to read the data from. plink_files = "random" specifies that the input format is plink's binary format, and the files to read are: random.bim contains information about SNPs random.fam contains information about individuals including the phenotype for one trait random.bed contains the fact table of genotypes random.cov if existing contains additional covariates if there are any random.yvm if existing contains phenotypes for additional traits if there are any Concerning the file formats see: * http://pngu.mgh.harvard.edu/~purcell/plink/data.shtml#bed * http://pngu.mgh.harvard.edu/~purcell/plink/binary.shtml The [data] section trait_index = 0 specifies that the phenotype for the first trait should be taken. In plink format, it is contained in the file with suffix .fam. The [output] section specifies where log- and other output files will be written with the option files = "random_out" This string will be used as prefix for the output filenames. For fine-tuning model selection, the section [model_selection] contains options expected_causal_snps_MBIC lets the first step with the relaxed selection criterium consider models of up to about the given size. Further useful options: [single_marker] test choice chi_square, cochran_armitage ... which one to use [model_preselection] mBIC_expected_causal_SNPs integer parameter for first round of model search (with mBIC) [model_selection] selection_criterium choice mBIC or mBIC2 (default): criterium to use in second round of model selection mBIC_expected_causal_SNPs integer parameter for second round of model search, when mBIC is chosen maximalModelSize integer limits the search to models of size up to the given; saves time PValueBorder integer only so many SNPs are considered in multi-forward steps, ranked by p-value forward_step_max integer bounds the numer of SNPs in the forward step from the empty model fast_multi_forward integer bounds the numer of SNPs in the forward step from nonempty models nSNPKriterium integer useful for running with a subset of top-ranking SNPs: the original number of SNPs, to be used by mBIC Upon successful run, you will find (assuming output filename prefix "random_out") files with the names random_out_IT.txt results from individual SNP tests random_outYvecout states the phenotype vector used random_out.mod describes the chosen model random_out.log log file from the search random_out_0the_result_Corr.txt information about SNPs which are highly correlated to those in the model random_out0the_resultCorr.h5 similar, but in HDF5 format
Source: README.txt, updated 2014-03-03