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1 #!/usr/bin/env python
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2
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3 import sys
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4 from rpy import *
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5 import numpy
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6
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7 def stop_err(msg):
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8 sys.stderr.write(msg)
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9 sys.exit()
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10
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11 infile = sys.argv[1]
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12 y_col = int(sys.argv[2])-1
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13 x_cols = sys.argv[3].split(',')
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14 outfile = sys.argv[4]
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15
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16
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17 print "Predictor columns: %s; Response column: %d" % ( x_cols, y_col+1 )
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18 fout = open(outfile,'w')
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19 elems = []
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20 for i, line in enumerate( file( infile ) ):
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21 line = line.rstrip('\r\n')
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22 if len( line )>0 and not line.startswith( '#' ):
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23 elems = line.split( '\t' )
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24 break
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25 if i == 30:
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26 break # Hopefully we'll never get here...
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27
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28 if len( elems )<1:
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29 stop_err( "The data in your input dataset is either missing or not formatted properly." )
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30
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31 y_vals = []
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32 x_vals = []
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33
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34 for k, col in enumerate(x_cols):
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35 x_cols[k] = int(col)-1
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36 x_vals.append([])
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37
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38 NA = 'NA'
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39 for ind, line in enumerate( file( infile )):
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40 if line and not line.startswith( '#' ):
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41 try:
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42 fields = line.split("\t")
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43 try:
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44 yval = float(fields[y_col])
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45 except:
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46 yval = r('NA')
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47 y_vals.append(yval)
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48 for k, col in enumerate(x_cols):
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49 try:
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50 xval = float(fields[col])
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51 except:
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52 xval = r('NA')
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53 x_vals[k].append(xval)
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54 except:
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55 pass
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56
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57 x_vals1 = numpy.asarray(x_vals).transpose()
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58
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59 check1 = 0
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60 check0 = 0
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61 for i in y_vals:
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62 if i == 1:
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63 check1 = 1
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64 if i == 0:
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65 check0 = 1
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66 if check1 == 0 or check0 == 0:
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67 sys.exit("Warning: logistic regression must have at least two classes")
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68
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69 for i in y_vals:
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70 if i not in [1, 0, r('NA')]:
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71 print >> fout, str(i)
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72 sys.exit("Warning: the current version of this tool can run only with two classes and need to be labeled as 0 and 1.")
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73
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74 dat = r.list(x=array(x_vals1), y=y_vals)
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75 novif = 0
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76 set_default_mode(NO_CONVERSION)
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77 try:
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78 linear_model = r.glm(r("y ~ x"), data=r.na_exclude(dat), family="binomial")
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79 except RException, rex:
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80 stop_err("Error performing logistic regression on the input data.\nEither the response column or one of the predictor columns contain only non-numeric or invalid values.")
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81 if len(x_cols)>1:
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82 try:
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83 r('suppressPackageStartupMessages(library(car))')
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84 r.assign('dat', dat)
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85 r.assign('ncols', len(x_cols))
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86 vif = r.vif(r('glm(dat$y ~ ., data = na.exclude(data.frame(as.matrix(dat$x,ncol=ncols))->datx), family="binomial")'))
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87 except RException, rex:
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88 print rex
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89 else:
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90 novif = 1
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91
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92 set_default_mode(BASIC_CONVERSION)
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93
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94 coeffs = linear_model.as_py()['coefficients']
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95 null_deviance = linear_model.as_py()['null.deviance']
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96 residual_deviance = linear_model.as_py()['deviance']
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97 yintercept = coeffs['(Intercept)']
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98 summary = r.summary(linear_model)
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99 co = summary.get('coefficients', 'NA')
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100 """
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101 if len(co) != len(x_vals)+1:
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102 stop_err("Stopped performing logistic regression on the input data, since one of the predictor columns contains only non-numeric or invalid values.")
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103 """
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104
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105 try:
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106 yintercept = r.round(float(yintercept), digits=10)
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107 pvaly = r.round(float(co[0][3]), digits=10)
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108 except:
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109 pass
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110 print >> fout, "response column\tc%d" % (y_col+1)
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111 tempP = []
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112 for i in x_cols:
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113 tempP.append('c'+str(i+1))
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114 tempP = ','.join(tempP)
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115 print >> fout, "predictor column(s)\t%s" % (tempP)
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116 print >> fout, "Y-intercept\t%s" % (yintercept)
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117 print >> fout, "p-value (Y-intercept)\t%s" % (pvaly)
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118
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119 if len(x_vals) == 1: #Simple linear regression case with 1 predictor variable
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120 try:
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121 slope = r.round(float(coeffs['x']), digits=10)
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122 except:
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123 slope = 'NA'
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124 try:
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125 pval = r.round(float(co[1][3]), digits=10)
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126 except:
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127 pval = 'NA'
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128 print >> fout, "Slope (c%d)\t%s" % ( x_cols[0]+1, slope )
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129 print >> fout, "p-value (c%d)\t%s" % ( x_cols[0]+1, pval )
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130 else: #Multiple regression case with >1 predictors
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131 ind = 1
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132 while ind < len(coeffs.keys()):
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133 try:
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134 slope = r.round(float(coeffs['x'+str(ind)]), digits=10)
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135 except:
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136 slope = 'NA'
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137 print >> fout, "Slope (c%d)\t%s" % ( x_cols[ind-1]+1, slope )
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138 try:
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139 pval = r.round(float(co[ind][3]), digits=10)
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140 except:
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141 pval = 'NA'
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142 print >> fout, "p-value (c%d)\t%s" % ( x_cols[ind-1]+1, pval )
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143 ind += 1
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144
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145 rsq = summary.get('r.squared','NA')
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146
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147 try:
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148 rsq = r.round(float((null_deviance-residual_deviance)/null_deviance), digits=5)
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149 null_deviance = r.round(float(null_deviance), digits=5)
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150 residual_deviance = r.round(float(residual_deviance), digits=5)
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151 except:
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152 pass
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153
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154 print >> fout, "Null deviance\t%s" % (null_deviance)
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155 print >> fout, "Residual deviance\t%s" % (residual_deviance)
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156 print >> fout, "pseudo R-squared\t%s" % (rsq)
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157 print >> fout, "\n"
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158 print >> fout, 'vif'
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159
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160 if novif == 0:
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161 py_vif = vif.as_py()
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162 count = 0
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163 for i in sorted(py_vif.keys()):
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164 print >> fout, 'c'+str(x_cols[count]+1), str(py_vif[i])
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165 count += 1
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166 elif novif == 1:
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167 print >> fout, "vif can calculate only when model have more than 1 predictor"
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