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80
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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 from galaxy import eggs
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23 import sys, string
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24 #from rpy import *
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25 import rpy2.robjects as robjects
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26 import rpy2.rlike.container as rlc
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27 from rpy2.robjects.packages import importr
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28 r = robjects.r
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29 import numpy
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30 import pkg_resources; pkg_resources.require( "bx-python" )
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31 from bx.cookbook import doc_optparse
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32
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33
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34 def stop_err(msg):
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35 sys.stderr.write(msg)
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36 sys.exit()
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37
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38 #Parse Command Line
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39 options, args = doc_optparse.parse( __doc__ )
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40 #{'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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41
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42 infile = options.input
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43 x_cols = options.x_cols.split(',')
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44 y_cols = options.y_cols.split(',')
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45 kernel = options.kernel
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46 outfile = options.output1
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47 ncomps = int(options.features)
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48 fout = open(outfile,'w')
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49
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50 if ncomps < 1:
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51 print "You chose to return '0' canonical components. Please try rerunning the tool with number of components = 1 or more."
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52 sys.exit()
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53 elems = []
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54 for i, line in enumerate( file ( infile )):
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55 line = line.rstrip('\r\n')
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56 if len( line )>0 and not line.startswith( '#' ):
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57 elems = line.split( '\t' )
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58 break
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59 if i == 30:
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60 break # Hopefully we'll never get here...
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61
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62 if len( elems )<1:
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63 stop_err( "The data in your input dataset is either missing or not formatted properly." )
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64
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65 x_vals = []
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66 for k,col in enumerate(x_cols):
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67 x_cols[k] = int(col)-1
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68 #x_vals.append([])
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69 y_vals = []
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70 for k,col in enumerate(y_cols):
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71 y_cols[k] = int(col)-1
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72 #y_vals.append([])
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73 NA = 'NA'
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74 skipped = 0
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75 for ind,line in enumerate( file( infile )):
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76 if line and not line.startswith( '#' ):
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77 try:
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78 fields = line.strip().split("\t")
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79 valid_line = True
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80 for col in x_cols+y_cols:
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81 try:
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82 assert float(fields[col])
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83 except:
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84 skipped += 1
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85 valid_line = False
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86 break
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87 if valid_line:
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88 for k,col in enumerate(x_cols):
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89 try:
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90 xval = float(fields[col])
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91 except:
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92 xval = NaN#
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93 #x_vals[k].append(xval)
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94 x_vals.append(xval)
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95 for k,col in enumerate(y_cols):
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96 try:
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97 yval = float(fields[col])
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98 except:
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99 yval = NaN#
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100 #y_vals[k].append(yval)
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101 y_vals.append(yval)
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102 except:
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103 skipped += 1
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104
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105 #x_vals1 = numpy.asarray(x_vals).transpose()
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106 #y_vals1 = numpy.asarray(y_vals).transpose()
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107
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108 #x_dat= r.list(array(x_vals1))
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109 #y_dat= r.list(array(y_vals1))
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110
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111 x_dat = r['matrix'](robjects.FloatVector(x_vals),ncol=len(x_cols),byrow=True)
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112 y_dat = r['matrix'](robjects.FloatVector(y_vals),ncol=len(y_cols),byrow=True)
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113
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114 try:
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115 r.suppressWarnings(r.library('kernlab'))
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116 except:
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117 stop_err('Missing R library kernlab')
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118
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119 #set_default_mode(NO_CONVERSION)
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120 if kernel=="rbfdot" or kernel=="anovadot":
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121 pars = r.list(sigma=float(options.sigma))
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122 elif kernel=="polydot":
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123 pars = r.list(degree=float(options.degree),scale=float(options.scale),offset=float(options.offset))
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124 elif kernel=="tanhdot":
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125 pars = r.list(scale=float(options.scale),offset=float(options.offset))
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126 elif kernel=="besseldot":
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127 pars = r.list(degree=float(options.degree),sigma=float(options.sigma),order=float(options.order))
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128 elif kernel=="anovadot":
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129 pars = r.list(degree=float(options.degree),sigma=float(options.sigma))
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130 else:
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131 pars = rlist()
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132
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133 try:
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134 kcc = r.kcca(x=x_dat, y=y_dat, kernel=kernel, kpar=pars, ncomps=ncomps)
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135 except RException, rex:
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136 stop_err("Encountered error while performing kCCA on the input data: %s" %(rex))
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137
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138 #set_default_mode(BASIC_CONVERSION)
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139 kcor = r.kcor(kcc)
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140 if ncomps == 1:
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141 kcor = [kcor]
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142 xcoef = r.xcoef(kcc)
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143 ycoef = r.ycoef(kcc)
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144
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145 print >>fout, "#Component\t%s" %("\t".join(["%s" % el for el in range(1,ncomps+1)]))
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146
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147 print >>fout, "#Correlation\t%s" %("\t".join(["%.4g" % el for el in kcor]))
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148
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149 print >>fout, "#Estimated X-coefficients\t%s" %("\t".join(["%s" % el for el in range(1,ncomps+1)]))
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150 #for obs,val in enumerate(xcoef):
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151 # print >>fout, "%s\t%s" %(obs+1, "\t".join(["%.4g" % el for el in val]))
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152 for i in range(1,xcoef.nrow+1):
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153 vals = []
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154 for j in range(1,xcoef.ncol+1):
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155 vals.append("%.4g" % xcoef.rx2(i,j)[0])
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156 print >>fout, "%s\t%s" %(i, "\t".join(vals))
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157
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158
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159 print >>fout, "#Estimated Y-coefficients\t%s" %("\t".join(["%s" % el for el in range(1,ncomps+1)]))
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160 #for obs,val in enumerate(ycoef):
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161 # print >>fout, "%s\t%s" %(obs+1, "\t".join(["%.4g" % el for el in val]))
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162 for i in range(1,ycoef.nrow+1):
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163 vals = []
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164 for j in range(1,ycoef.ncol+1):
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165 vals.append("%.4g" % ycoef.rx2(i,j)[0])
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166 print >>fout, "%s\t%s" %(i, "\t".join(vals))
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