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134 lines
4.4 KiB
Python
134 lines
4.4 KiB
Python
#!/usr/bin/env python
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"""
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Run kernel PCA using kpca() from R 'kernlab' package
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usage: %prog [options]
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-i, --input=i: Input file
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-o, --output1=o: Summary output
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-p, --output2=p: Figures output
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-c, --var_cols=c: Variable columns
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-k, --kernel=k: Kernel function
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-f, --features=f: Number of principal components to return
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-s, --sigma=s: sigma
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-d, --degree=d: degree
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-l, --scale=l: scale
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-t, --offset=t: offset
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-r, --order=r: order
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usage: %prog input output1 output2 var_cols kernel features sigma(or_None) degree(or_None) scale(or_None) offset(or_None) order(or_None)
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"""
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from galaxy import eggs
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import sys, string
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from rpy import *
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import numpy
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import pkg_resources; pkg_resources.require( "bx-python" )
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from bx.cookbook import doc_optparse
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def stop_err(msg):
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sys.stderr.write(msg)
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sys.exit()
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#Parse Command Line
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options, args = doc_optparse.parse( __doc__ )
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#{'options= kernel': 'rbfdot', 'var_cols': '1,2,3,4', 'degree': 'None', 'output2': '/afs/bx.psu.edu/home/gua110/workspace/galaxy_bitbucket/database/files/000/dataset_260.dat', 'output1': '/afs/bx.psu.edu/home/gua110/workspace/galaxy_bitbucket/database/files/000/dataset_259.dat', 'scale': 'None', 'offset': 'None', 'input': '/afs/bx.psu.edu/home/gua110/workspace/galaxy_bitbucket/database/files/000/dataset_256.dat', 'sigma': '1.0', 'order': 'None'}
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infile = options.input
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x_cols = options.var_cols.split(',')
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kernel = options.kernel
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outfile = options.output1
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outfile2 = options.output2
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ncomps = int(options.features)
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fout = open(outfile,'w')
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elems = []
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for i, line in enumerate( file ( infile )):
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line = line.rstrip('\r\n')
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if len( line )>0 and not line.startswith( '#' ):
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elems = line.split( '\t' )
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break
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if i == 30:
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break # Hopefully we'll never get here...
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if len( elems )<1:
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stop_err( "The data in your input dataset is either missing or not formatted properly." )
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x_vals = []
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for k,col in enumerate(x_cols):
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x_cols[k] = int(col)-1
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x_vals.append([])
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NA = 'NA'
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skipped = 0
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for ind,line in enumerate( file( infile )):
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if line and not line.startswith( '#' ):
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try:
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fields = line.strip().split("\t")
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for k,col in enumerate(x_cols):
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try:
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xval = float(fields[col])
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except:
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#xval = r('NA')
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xval = NaN#
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x_vals[k].append(xval)
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except:
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skipped += 1
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x_vals1 = numpy.asarray(x_vals).transpose()
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dat= r.list(array(x_vals1))
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try:
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r.suppressWarnings(r.library('kernlab'))
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except:
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stop_err('Missing R library kernlab')
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set_default_mode(NO_CONVERSION)
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if kernel=="rbfdot" or kernel=="anovadot":
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pars = r.list(sigma=float(options.sigma))
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elif kernel=="polydot":
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pars = r.list(degree=float(options.degree),scale=float(options.scale),offset=float(options.offset))
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elif kernel=="tanhdot":
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pars = r.list(scale=float(options.scale),offset=float(options.offset))
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elif kernel=="besseldot":
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pars = r.list(degree=float(options.degree),sigma=float(options.sigma),order=float(options.order))
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elif kernel=="anovadot":
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pars = r.list(degree=float(options.degree),sigma=float(options.sigma))
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else:
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pars = r.list()
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try:
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kpc = r.kpca(x=r.na_exclude(dat), kernel=kernel, kpar=pars, features=ncomps)
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except RException, rex:
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stop_err("Encountered error while performing kPCA on the input data: %s" %(rex))
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set_default_mode(BASIC_CONVERSION)
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eig = r.eig(kpc)
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pcv = r.pcv(kpc)
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rotated = r.rotated(kpc)
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comps = eig.keys()
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eigv = eig.values()
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for i in range(ncomps):
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eigv[comps.index('Comp.%s' %(i+1))] = eig.values()[i]
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print >>fout, "#Component\t%s" %("\t".join(["%s" % el for el in range(1,ncomps+1)]))
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print >>fout, "#Eigenvalue\t%s" %("\t".join(["%.4g" % el for el in eig.values()]))
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print >>fout, "#Principal component vectors\t%s" %("\t".join(["%s" % el for el in range(1,ncomps+1)]))
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for obs,val in enumerate(pcv):
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print >>fout, "%s\t%s" %(obs+1, "\t".join(["%.4g" % el for el in val]))
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print >>fout, "#Rotated values\t%s" %("\t".join(["%s" % el for el in range(1,ncomps+1)]))
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for obs,val in enumerate(rotated):
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print >>fout, "%s\t%s" %(obs+1, "\t".join(["%.4g" % el for el in val]))
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r.pdf( outfile2, 8, 8 )
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if ncomps != 1:
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r.pairs(rotated,labels=r.list(range(1,ncomps+1)),main="Scatterplot of rotated values")
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else:
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r.plot(rotated, ylab='Comp.1', main="Scatterplot of rotated values")
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r.dev_off() |