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1 #!/usr/bin/env python
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2
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3 """
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4 Run kernel CCA using kcca() from R 'kernlab' package
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5
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6 usage: %prog [options]
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7 -i, --input=i: Input file
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8 -o, --output1=o: Summary output
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9 -x, --x_cols=x: X-Variable columns
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10 -y, --y_cols=y: Y-Variable columns
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11 -k, --kernel=k: Kernel function
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12 -f, --features=f: Number of canonical components to return
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13 -s, --sigma=s: sigma
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14 -d, --degree=d: degree
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15 -l, --scale=l: scale
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16 -t, --offset=t: offset
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17 -r, --order=r: order
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18
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19 usage: %prog input output1 x_cols y_cols kernel features sigma(or_None) degree(or_None) scale(or_None) offset(or_None) order(or_None)
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20 """
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21
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22 import sys, string
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23 from rpy import *
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24 import numpy
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25 from bx.cookbook import doc_optparse
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26 import logging
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27 log = logging.getLogger('kcca')
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28
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29 def stop_err(msg):
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30 sys.stderr.write(msg)
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31 sys.exit()
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32
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33 #Parse Command Line
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34 options, args = doc_optparse.parse( __doc__ )
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35 #{'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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36
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37 infile = options.input
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38 x_cols = options.x_cols.split(',')
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39 y_cols = options.y_cols.split(',')
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40 kernel = options.kernel
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41 outfile = options.output1
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42 ncomps = int(options.features)
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43 fout = open(outfile,'w')
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44
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45 if ncomps < 1:
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46 print "You chose to return '0' canonical components. Please try rerunning the tool with number of components = 1 or more."
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47 sys.exit()
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48 elems = []
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49 for i, line in enumerate( file ( infile )):
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50 line = line.rstrip('\r\n')
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51 if len( line )>0 and not line.startswith( '#' ):
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52 elems = line.split( '\t' )
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53 break
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54 if i == 30:
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55 break # Hopefully we'll never get here...
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56
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57 if len( elems )<1:
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58 stop_err( "The data in your input dataset is either missing or not formatted properly." )
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59
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60 x_vals = []
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61 for k,col in enumerate(x_cols):
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62 x_cols[k] = int(col)-1
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63 x_vals.append([])
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64 y_vals = []
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65 for k,col in enumerate(y_cols):
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66 y_cols[k] = int(col)-1
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67 y_vals.append([])
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68 NA = 'NA'
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69 skipped = 0
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70 for ind,line in enumerate( file( infile )):
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71 if line and not line.startswith( '#' ):
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72 try:
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73 fields = line.strip().split("\t")
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74 valid_line = True
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75 for col in x_cols+y_cols:
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76 try:
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77 assert float(fields[col])
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78 except:
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79 skipped += 1
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80 valid_line = False
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81 break
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82 if valid_line:
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83 for k,col in enumerate(x_cols):
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84 try:
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85 xval = float(fields[col])
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86 except:
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87 xval = NaN
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88 x_vals[k].append(xval)
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89 for k,col in enumerate(y_cols):
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90 try:
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91 yval = float(fields[col])
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92 except:
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93 yval = NaN
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94 y_vals[k].append(yval)
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95 except:
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96 skipped += 1
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97
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98 x_vals1 = numpy.asarray(x_vals).transpose()
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99 y_vals1 = numpy.asarray(y_vals).transpose()
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100
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101 x_dat= r.list(array(x_vals1))
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102 y_dat= r.list(array(y_vals1))
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103
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104 try:
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105 r.suppressWarnings(r.library('kernlab'))
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106 except:
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107 stop_err('Missing R library kernlab')
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108
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109 set_default_mode(NO_CONVERSION)
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110 if kernel=="rbfdot" or kernel=="anovadot":
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111 pars = r.list(sigma=float(options.sigma))
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112 elif kernel=="polydot":
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113 pars = r.list(degree=float(options.degree),scale=float(options.scale),offset=float(options.offset))
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114 elif kernel=="tanhdot":
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115 pars = r.list(scale=float(options.scale),offset=float(options.offset))
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116 elif kernel=="besseldot":
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117 pars = r.list(degree=float(options.degree),sigma=float(options.sigma),order=float(options.order))
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118 elif kernel=="anovadot":
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119 pars = r.list(degree=float(options.degree),sigma=float(options.sigma))
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120 else:
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121 pars = rlist()
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122
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123 try:
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124 kcc = r.kcca(x=x_dat, y=y_dat, kernel=kernel, kpar=pars, ncomps=ncomps)
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125 except RException, rex:
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126 raise
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127 log.exception( rex )
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128 stop_err("Encountered error while performing kCCA on the input data: %s" %(rex))
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129
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130 set_default_mode(BASIC_CONVERSION)
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131 kcor = r.kcor(kcc)
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132 if ncomps == 1:
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133 kcor = [kcor]
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134 xcoef = r.xcoef(kcc)
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135 ycoef = r.ycoef(kcc)
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136
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137 print >>fout, "#Component\t%s" %("\t".join(["%s" % el for el in range(1,ncomps+1)]))
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138
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139 print >>fout, "#Correlation\t%s" %("\t".join(["%.4g" % el for el in kcor]))
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140
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141 print >>fout, "#Estimated X-coefficients\t%s" %("\t".join(["%s" % el for el in range(1,ncomps+1)]))
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142 for obs,val in enumerate(xcoef):
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143 print >>fout, "%s\t%s" %(obs+1, "\t".join(["%.4g" % el for el in val]))
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144
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145 print >>fout, "#Estimated Y-coefficients\t%s" %("\t".join(["%s" % el for el in range(1,ncomps+1)]))
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146 for obs,val in enumerate(ycoef):
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147 print >>fout, "%s\t%s" %(obs+1, "\t".join(["%.4g" % el for el in val]))
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