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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
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8 import rpy2.robjects as robjects
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9 import rpy2.rlike.container as rlc
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10 r = robjects.r
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11 import numpy
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12
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13 #export PYTHONPATH=~/galaxy/lib/
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14 #running command python partialR_square.py reg_inp.tab 4 1,2,3 partialR_result.tabular
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15
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16 def stop_err(msg):
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17 sys.stderr.write(msg)
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18 sys.exit()
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19
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20 def sscombs(s):
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21 if len(s) == 1:
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22 return [s]
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23 else:
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24 ssc = sscombs(s[1:])
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25 return [s[0]] + [s[0]+comb for comb in ssc] + ssc
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26
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27
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28 infile = sys.argv[1]
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29 y_col = int(sys.argv[2])-1
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30 x_cols = sys.argv[3].split(',')
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31 outfile = sys.argv[4]
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32
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33 print "Predictor columns: %s; Response column: %d" %(x_cols,y_col+1)
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34 fout = open(outfile,'w')
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35
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36 for i, line in enumerate( file ( infile )):
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37 line = line.rstrip('\r\n')
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38 if len( line )>0 and not line.startswith( '#' ):
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39 elems = line.split( '\t' )
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40 break
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41 if i == 30:
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42 break # Hopefully we'll never get here...
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43
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44 if len( elems )<1:
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45 stop_err( "The data in your input dataset is either missing or not formatted properly." )
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46
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47 y_vals = []
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48 x_vals = []
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49 x_vector = []
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50 for k,col in enumerate(x_cols):
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51 x_cols[k] = int(col)-1
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52 x_vals.append([])
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53 """
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54 try:
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55 float( elems[x_cols[k]] )
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56 except:
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57 try:
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58 msg = "This operation cannot be performed on non-numeric column %d containing value '%s'." %( col, elems[x_cols[k]] )
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59 except:
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60 msg = "This operation cannot be performed on non-numeric data."
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61 stop_err( msg )
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62 """
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63 NA = 'NA'
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64 for ind,line in enumerate( file( infile )):
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65 if line and not line.startswith( '#' ):
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66 try:
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67 fields = line.split("\t")
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68 try:
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69 yval = float(fields[y_col])
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70 except Exception, ey:
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71 yval = r('NA')
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72 #print >>sys.stderr, "ey = %s" %ey
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73 y_vals.append(yval)
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74 for k,col in enumerate(x_cols):
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75 try:
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76 xval = float(fields[col])
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77 except Exception, ex:
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78 xval = r('NA')
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79 #print >>sys.stderr, "ex = %s" %ex
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80 x_vals[k].append(xval)
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81 x_vector.append(xval)
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82 except:
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83 pass
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84
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85 #x_vals1 = numpy.asarray(x_vals).transpose()
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86 #dat= r.list(x=array(x_vals1), y=y_vals)
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87
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88 #set_default_mode(NO_CONVERSION)
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89 #try:
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90 # 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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91 #except RException, rex:
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92 # 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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93 #set_default_mode(BASIC_CONVERSION)
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94
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95 fv = robjects.FloatVector(x_vector)
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96 m = r['matrix'](fv, ncol=len(x_cols),byrow=True)
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97 # ensure order for generating formula
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98 od = rlc.OrdDict([('y',robjects.FloatVector(y_vals)),('x',m)])
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99 dat = robjects.DataFrame(od)
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100 # convert dat.names: ["y","x.1","x.2"] to formula string: 'y ~ x.1 + x.2'
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101 formula = ' + '.join(dat.names).replace('+','~',1)
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102 try:
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103 full = r.lm(formula, data = r['na.exclude'](dat))
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104 except RException, rex:
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105 stop_err("Error performing linear 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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106
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107
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108
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109 summary = r.summary(full)
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110 #fullr2 = summary.get('r.squared','NA')
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111 fullr2 = summary.rx2('r.squared')[0]
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112
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113 if fullr2 == 'NA':
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114 stop_error("Error in linear regression")
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115
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116 if len(x_vals) < 10:
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117 s = ""
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118 for ch in range(len(x_vals)):
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119 s += str(ch)
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120 else:
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121 stop_err("This tool only works with less than 10 predictors.")
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122
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123 print >>fout, "#Model\tR-sq\tpartial_R_Terms\tpartial_R_Value"
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124 all_combos = sorted(sscombs(s), key=len)
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125 all_combos.reverse()
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126 for j,cols in enumerate(all_combos):
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127 #if len(cols) == len(s): #Same as the full model above
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128 # continue
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129 if len(cols) == 1:
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130 #x_vals1 = x_vals[int(cols)]
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131 x_v = x_vals[int(cols)]
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132 else:
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133 x_v = []
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134 for col in cols:
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135 #x_v.append(x_vals[int(col)])
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136 x_v.extend(x_vals[int(col)])
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137 #x_vals1 = numpy.asarray(x_v).transpose()
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138 #dat= r.list(x=array(x_vals1), y=y_vals)
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139 #set_default_mode(NO_CONVERSION)
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140 #red = r.lm(r("y ~ x"), data= dat) #Reduced model
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141 #set_default_mode(BASIC_CONVERSION)
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142 fv = robjects.FloatVector(x_v)
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143 m = r['matrix'](fv, ncol=len(cols),byrow=False)
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144 # ensure order for generating formula
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145 od = rlc.OrdDict([('y',robjects.FloatVector(y_vals)),('x',m)])
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146 dat = robjects.DataFrame(od)
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147 # convert dat.names: ["y","x.1","x.2"] to formula string: 'y ~ x.1 + x.2'
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148 formula = ' + '.join(dat.names).replace('+','~',1)
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149 try:
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150 red = r.lm(formula, data = r['na.exclude'](dat))
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151 except RException, rex:
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152 stop_err("Error performing linear 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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153
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154
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155 summary = r.summary(red)
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156 #redr2 = summary.get('r.squared','NA')
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157 redr2 = summary.rx2('r.squared')[0]
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158
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159 try:
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160 partial_R = (float(fullr2)-float(redr2))/(1-float(redr2))
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161 except:
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162 partial_R = 'NA'
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163 col_str = ""
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164 for col in cols:
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165 col_str = col_str + str(int(x_cols[int(col)]) + 1) + " "
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166 col_str.strip()
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167 partial_R_col_str = ""
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168 for col in s:
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169 if col not in cols:
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170 partial_R_col_str = partial_R_col_str + str(int(x_cols[int(col)]) + 1) + " "
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171 partial_R_col_str.strip()
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172 if len(cols) == len(s): #full model
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173 partial_R_col_str = "-"
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174 partial_R = "-"
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175 try:
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176 redr2 = "%.4f" %(float(redr2))
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177 except:
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178 pass
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179 try:
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180 partial_R = "%.4f" %(float(partial_R))
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181 except:
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182 pass
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183 print >>fout, "%s\t%s\t%s\t%s" %(col_str,redr2,partial_R_col_str,partial_R)
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