"""
# oct 2009 - must make a map file in case later usage requires it...
# galaxy tool xml files can define a galaxy supplied output filename
# that must be passed to the tool and used to return output
# here, the plink log file is copied to that file and removed
# took a while to figure this out!
# use exec_before_job to give files sensible names
#
# ross april 14 2007
# plink cleanup script
# ross lazarus March 2007 for camp illumina whole genome data
# note problems with multiple commands being ignored - eg --freq --missing --mendel
# only the first seems to get done...
#
##Summary statistics versus inclusion criteria
##
##Feature As summary statistic As inclusion criteria
##Missingness per individual --missing --mind N
##Missingness per marker --missing --geno N
##Allele frequency --freq --maf N
##Hardy-Weinberg equilibrium --hardy --hwe N
##Mendel error rates --mendel --me N M
#
# this is rgLDIndep.py - main task is to decrease LD by filtering high LD pairs
# remove that function from rgClean.py as it may not be needed.
"""
import sys,shutil,os,subprocess, glob, string, tempfile, time
from rgutils import plinke, timenow, galhtmlprefix
prog = os.path.split(sys.argv[0])[-1]
myversion = 'January 4 2010'
def pruneld(plinktasks=[] ,cd='./',vclbase = []):
"""
plink blathers when doing pruning - ignore
Linkage disequilibrium based SNP pruning
if a million snps in 3 billion base pairs, have mean 3k spacing
assume 40-60k of ld in ceu, a window of 120k width is about 40 snps
so lots more is perhaps less efficient - each window computational cost is
ON^2 unless the code is smart enough to avoid unecessary computation where
allele frequencies make it impossible to see ld > the r^2 cutoff threshold
So, do a window and move forward 20?
from the plink docs at http://pngu.mgh.harvard.edu/~purcell/plink/summary.shtml#prune
Sometimes it is useful to generate a pruned subset of SNPs that are in approximate linkage equilibrium with each other. This can be achieved via two commands: --indep which prunes based on the variance inflation factor (VIF), which recursively removes SNPs within a sliding window; second, --indep-pairwise which is similar, except it is based only on pairwise genotypic correlation.
Hint The output of either of these commands is two lists of SNPs: those that are pruned out and those that are not. A separate command using the --extract or --exclude option is necessary to actually perform the pruning.
The VIF pruning routine is performed:
plink --file data --indep 50 5 2
will create files
plink.prune.in
plink.prune.out
Each is a simlpe list of SNP IDs; both these files can subsequently be specified as the argument for
a --extract or --exclude command.
The parameters for --indep are: window size in SNPs (e.g. 50), the number of SNPs to shift the
window at each step (e.g. 5), the VIF threshold. The VIF is 1/(1-R^2) where R^2 is the multiple correlation coefficient for a SNP being regressed on all other SNPs simultaneously. That is, this considers the correlations between SNPs but also between linear combinations of SNPs. A VIF of 10 is often taken to represent near collinearity problems in standard multiple regression analyses (i.e. implies R^2 of 0.9). A VIF of 1 would imply that the SNP is completely independent of all other SNPs. Practically, values between 1.5 and 2 should probably be used; particularly in small samples, if this threshold is too low and/or the window size is too large, too many SNPs may be removed.
The second procedure is performed:
plink --file data --indep-pairwise 50 5 0.5
This generates the same output files as the first version; the only difference is that a
simple pairwise threshold is used. The first two parameters (50 and 5) are the same as above (window size and step); the third parameter represents the r^2 threshold. Note: this represents the pairwise SNP-SNP metric now, not the multiple correlation coefficient; also note, this is based on the genotypic correlation, i.e. it does not involve phasing.
To give a concrete example: the command above that specifies 50 5 0.5 would a) consider a
window of 50 SNPs, b) calculate LD between each pair of SNPs in the window, b) remove one of a pair of SNPs if the LD is greater than 0.5, c) shift the window 5 SNPs forward and repeat the procedure.
To make a new, pruned file, then use something like (in this example, we also convert the
standard PED fileset to a binary one):
plink --file data --extract plink.prune.in --make-bed --out pruneddata
"""
logres = ['## Rgenetics %s: http://rgenetics.org Galaxy Tools rgLDIndep.py Plink pruneLD runner\n' % myversion,]
for task in plinktasks: # each is a list
fplog,plog = tempfile.mkstemp()
sto = open(plog,'w') # to catch the blather
vcl = vclbase + task
s = '## ldindep now executing %s\n' % ' '.join(vcl)
print s
logres.append(s)
x = subprocess.Popen(' '.join(vcl),shell=True,stdout=sto,stderr=sto,cwd=cd)
retval = x.wait()
sto.close()
sto = open(plog,'r') # read
try:
lplog = sto.readlines()
lplog = [x for x in lplog if x.find('Pruning SNP') == -1]
logres += lplog
logres.append('\n')
except:
logres.append('### %s Strange - no std out from plink when running command line\n%s' % (timenow(),' '.join(vcl)))
sto.close()
os.unlink(plog) # no longer needed
return logres
def clean():
"""
"""
if len(sys.argv) < 14:
print >> sys.stdout, '## %s expected 14 params in sys.argv, got %d - %s' % (prog,len(sys.argv),sys.argv)
print >> sys.stdout, """this script will filter a linkage format ped
and map file containing genotypes. It takes 14 parameters - the plink --f parameter and"
a new filename root for the output clean data followed by the mind,geno,hwe,maf, mef and mei"
documented in the plink docs plus the file to be returned to Galaxy
Called as:
rgLDIndep.py '$input_file.extra_files_path' '$input_file.metadata.base_name' '$title' '$mind'
'$geno' '$hwe' '$maf' '$mef' '$mei' '$out_file1'
'$out_file1.extra_files_path' '$window' '$step' '$r2'
"""
sys.exit(1)
plog = ['## Rgenetics: http://rgenetics.org Galaxy Tools rgLDIndep.py started %s\n' % timenow()]
inpath = sys.argv[1]
inbase = sys.argv[2]
killme = string.punctuation + string.whitespace
trantab = string.maketrans(killme,'_'*len(killme))
title = sys.argv[3].translate(trantab)
mind = sys.argv[4]
geno = sys.argv[5]
hwe = sys.argv[6]
maf = sys.argv[7]
me1 = sys.argv[8]
me2 = sys.argv[9]
outfname = sys.argv[10]
outfpath = sys.argv[11]
winsize = sys.argv[12]
step = sys.argv[13]
r2 = sys.argv[14]
output = os.path.join(outfpath,outfname)
outpath = os.path.join(outfpath,title)
outprunepath = os.path.join(outfpath,'ldprune_%s' % title)
try:
os.makedirs(outfpath)
except:
pass
bfile = os.path.join(inpath,inbase)
filterout = os.path.join(outpath,'filtered_%s' % inbase)
outf = file(outfname,'w')
outf.write(galhtmlprefix % prog)
ldin = bfile
plinktasks = [['--bfile',ldin,'--indep-pairwise %s %s %s' % (winsize,step,r2),'--out',outpath,
'--mind',mind,'--geno',geno,'--maf',maf,'--hwe',hwe,'--me',me1,me2,],
['--bfile',ldin,'--extract %s.prune.in --make-bed --out %s' % (outpath,outpath)],
['--bfile',outpath,'--recode --out',outpath]] # make map file - don't really need ped but...
# subset of ld independent markers for eigenstrat and other requirements
vclbase = [plinke,'--noweb']
prunelog = pruneld(plinktasks=plinktasks,cd=outfpath,vclbase = vclbase)
"""This generates the same output files as the first version;
the only difference is that a simple pairwise threshold is used.
The first two parameters (50 and 5) are the same as above (window size and step);
the third parameter represents the r^2 threshold.
Note: this represents the pairwise SNP-SNP metric now, not the
multiple correlation coefficient; also note, this is based on the
genotypic correlation, i.e. it does not involve phasing.
"""
plog += prunelog
flog = '%s.log' % outpath
flogf = open(flog,'w')
flogf.write(''.join(plog))
flogf.write('\n')
flogf.close()
globme = os.path.join(outfpath,'*')
flist = glob.glob(globme)
flist.sort()
for i, data in enumerate( flist ):
outf.write('%s\n' % (os.path.split(data)[-1],os.path.split(data)[-1]))
outf.write('\n')
outf.write("