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1 #!/usr/bin/env python
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2
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3 from galaxy import eggs
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4
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5 import sys, string
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6 from rpy import *
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7 import numpy
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8
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9 def stop_err(msg):
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10 sys.stderr.write(msg)
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11 sys.exit()
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12
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13 def sscombs(s):
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14 if len(s) == 1:
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15 return [s]
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16 else:
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17 ssc = sscombs(s[1:])
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18 return [s[0]] + [s[0]+comb for comb in ssc] + ssc
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19
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20
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21 infile = sys.argv[1]
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22 y_col = int(sys.argv[2])-1
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23 x_cols = sys.argv[3].split(',')
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24 outfile = sys.argv[4]
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25
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26 print "Predictor columns: %s; Response column: %d" %(x_cols,y_col+1)
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27 fout = open(outfile,'w')
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28
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29 for i, line in enumerate( file ( infile )):
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30 line = line.rstrip('\r\n')
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31 if len( line )>0 and not line.startswith( '#' ):
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32 elems = line.split( '\t' )
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33 break
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34 if i == 30:
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35 break # Hopefully we'll never get here...
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36
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37 if len( elems )<1:
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38 stop_err( "The data in your input dataset is either missing or not formatted properly." )
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39
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40 y_vals = []
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41 x_vals = []
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42
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43 for k,col in enumerate(x_cols):
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44 x_cols[k] = int(col)-1
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45 x_vals.append([])
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46 """
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47 try:
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48 float( elems[x_cols[k]] )
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49 except:
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50 try:
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51 msg = "This operation cannot be performed on non-numeric column %d containing value '%s'." %( col, elems[x_cols[k]] )
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52 except:
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53 msg = "This operation cannot be performed on non-numeric data."
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54 stop_err( msg )
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55 """
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56 NA = 'NA'
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57 for ind,line in enumerate( file( infile )):
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58 if line and not line.startswith( '#' ):
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59 try:
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60 fields = line.split("\t")
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61 try:
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62 yval = float(fields[y_col])
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63 except Exception, ey:
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64 yval = r('NA')
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65 #print >>sys.stderr, "ey = %s" %ey
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66 y_vals.append(yval)
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67 for k,col in enumerate(x_cols):
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68 try:
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69 xval = float(fields[col])
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70 except Exception, ex:
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71 xval = r('NA')
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72 #print >>sys.stderr, "ex = %s" %ex
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73 x_vals[k].append(xval)
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74 except:
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75 pass
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76
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77 x_vals1 = numpy.asarray(x_vals).transpose()
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78 dat= r.list(x=array(x_vals1), y=y_vals)
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79
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80 set_default_mode(NO_CONVERSION)
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81 try:
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82 full = r.lm(r("y ~ x"), data= r.na_exclude(dat)) #full model includes all the predictor variables specified by the user
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83 except RException, rex:
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84 stop_err("Error performing linear regression on the input data.\nEither the response column or one of the predictor columns contain no numeric values.")
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85 set_default_mode(BASIC_CONVERSION)
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86
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87 summary = r.summary(full)
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88 fullr2 = summary.get('r.squared','NA')
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89
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90 if fullr2 == 'NA':
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91 stop_error("Error in linear regression")
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92
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93 if len(x_vals) < 10:
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94 s = ""
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95 for ch in range(len(x_vals)):
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96 s += str(ch)
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97 else:
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98 stop_err("This tool only works with less than 10 predictors.")
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99
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100 print >>fout, "#Model\tR-sq\tRCVE_Terms\tRCVE_Value"
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101 all_combos = sorted(sscombs(s), key=len)
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102 all_combos.reverse()
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103 for j,cols in enumerate(all_combos):
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104 #if len(cols) == len(s): #Same as the full model above
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105 # continue
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106 if len(cols) == 1:
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107 x_vals1 = x_vals[int(cols)]
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108 else:
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109 x_v = []
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110 for col in cols:
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111 x_v.append(x_vals[int(col)])
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112 x_vals1 = numpy.asarray(x_v).transpose()
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113 dat= r.list(x=array(x_vals1), y=y_vals)
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114 set_default_mode(NO_CONVERSION)
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115 red = r.lm(r("y ~ x"), data= dat) #Reduced model
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116 set_default_mode(BASIC_CONVERSION)
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117 summary = r.summary(red)
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118 redr2 = summary.get('r.squared','NA')
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119 try:
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120 rcve = (float(fullr2)-float(redr2))/float(fullr2)
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121 except:
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122 rcve = 'NA'
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123 col_str = ""
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124 for col in cols:
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125 col_str = col_str + str(int(x_cols[int(col)]) + 1) + " "
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126 col_str.strip()
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127 rcve_col_str = ""
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128 for col in s:
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129 if col not in cols:
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130 rcve_col_str = rcve_col_str + str(int(x_cols[int(col)]) + 1) + " "
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131 rcve_col_str.strip()
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132 if len(cols) == len(s): #full model
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133 rcve_col_str = "-"
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134 rcve = "-"
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135 try:
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136 redr2 = "%.4f" %(float(redr2))
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137 except:
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138 pass
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139 try:
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140 rcve = "%.4f" %(float(rcve))
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141 except:
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142 pass
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143 print >>fout, "%s\t%s\t%s\t%s" %(col_str,redr2,rcve_col_str,rcve)
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