NgsAdmix: Difference between revisions
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==Log file== | ==Log file== | ||
<div class="toccolours mw-collapsible mw-collapsed"> | |||
Contents of the file log file | |||
<pre class="mw-collapsible-content"> | |||
<pre> | <pre> | ||
-> Dumping file: tskSim/tsk6GL.beagle.s1.log | -> Dumping file: tskSim/tsk6GL.beagle.s1.log | ||
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[ALL done] cpu-time used = 671.82 sec | [ALL done] cpu-time used = 671.82 sec | ||
[ALL done] walltime used = 114.00 sec | [ALL done] walltime used = 114.00 sec | ||
</pre> | </pre> | ||
</div> | |||
=log= | =log= | ||
* v32 june 25-2013; modified code such that it now compiles on OSX | * v32 june 25-2013; modified code such that it now compiles on OSX | ||
* v31 june 24-2013; First public version. | * v31 june 24-2013; First public version. | ||
Revision as of 10:51, 25 June 2013
This will contain the program called NGSadmix, which is a very nice tool for finding admixture. It is based on genotype likelihoods or genotype probabilities. It is a fancy multithreaded c/c++ program
Installation
wget popgen.dk/software/NGSadmix/ngsadmix32.cpp g++ ngsadmix32.cpp -O3 -lpthread -lz -o NGSadmix
Input Files
The current input files are the widely used beagle inputfiles, or beagle imputed outputfiles [1]. We recommend ANGSD for easy transformation of Next-generation sequencing data to beagle format.
Options
./NGSadmix Arguments: -likes Beagle likelihood filename -K Number of ancestral populations Optional: -fname Ancestral population frequencies -qname Admixture proportions -outfiles Prefix for output files -printInfo print ID and mean maf for the SNPs that were analysed Setup: -seed Seed for initial guess in EM -P Number of threads -method If 0 no acceleration of EM algorithm -misTol Tolerance for considering site as missing Stop chriteria: -tolLike50 Loglikelihood difference in 50 iterations -tol Tolerance for convergence -dymBound Use dymamic boundaries (1: yes (default) 0: no) -maxiter Maximum number of EM iterations Filtering -minMaf Minimum minor allele frequency -minLrt Minimum likelihood ratio value for maf>0 -minInd Minumum number of informative individuals
Output Files
Program outputs 3 files.
- PREFIX.log
- PREFIX.fopt.gz
- PREFIX.qopt
Log file
Contents of the file log file
<pre> -> Dumping file: tskSim/tsk6GL.beagle.s1.log -> Dumping file: tskSim/tsk6GL.beagle.s1.filter Input: lname=tskSim/tsk6GL.beagle nPop=3, fname=(null) qname=(null) outfiles=tskSim/tsk6GL.beagle.s1 Setup: seed=1 nThreads=10 method=1 Convergence: maxIter=2000 tol=0.000000 tolLike50=0.010000 dymBound=0 Filters: misTol=0.050000 minMaf=0.000000 minLrt=0.000000 minInd=0 Input file has dim: nsites=100000 nind=75 Input file has dim (AFTER filtering): nsites=100000 nind=75 iter[start] like is=9299805.984931 iter[50] like is=-6531138.892608 thres=0.002800 iter[100] like is=-6528710.773349 thres=0.001289 iter[150] like is=-6528405.896951 thres=0.001211 iter[200] like is=-6528306.803820 thres=0.000420 iter[250] like is=-6528277.160993 thres=0.000546 iter[300] like is=-6528271.925055 thres=0.000033 iter[350] like is=-6528271.177692 thres=0.000008 iter[400] like is=-6528270.876315 thres=0.000005 iter[450] like is=-6528270.772894 thres=0.000140 iter[500] like is=-6528270.747721 thres=0.000002 iter[550] like is=-6528270.740654 thres=0.000002 Convergence achived because log likelihooditer difference for 50 iteraction is less than 0.010000 best like=-6528270.740654 after 550 iterations -> Dumping file: tskSim/tsk6GL.beagle.s1.qopt -> Dumping file: tskSim/tsk6GL.beagle.s1.fopt.gz [ALL done] cpu-time used = 671.82 sec [ALL done] walltime used = 114.00 sec
log
- v32 june 25-2013; modified code such that it now compiles on OSX
- v31 june 24-2013; First public version.