--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/tools/rgenetics/rgLDIndep.py	Fri Mar 09 19:37:19 2012 -0500
@@ -0,0 +1,182 @@
+"""
+# 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:
+        <command interpreter="python">
+        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'
+        </command>
+        """
+        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('<li><a href="%s">%s</a></li>\n' % (os.path.split(data)[-1],os.path.split(data)[-1]))
+    outf.write('</ol></div>\n')
+    outf.write("</div></body></html>")
+    outf.close()
+
+
+if __name__ == "__main__":
+    clean()
+