[2] | 1 | #!/usr/bin/env python |
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| 2 | |
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| 3 | from galaxy import eggs |
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| 4 | import sys, string |
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| 5 | from rpy import * |
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| 6 | import numpy |
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| 7 | |
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| 8 | def stop_err(msg): |
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| 9 | sys.stderr.write(msg) |
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| 10 | sys.exit() |
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| 11 | |
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| 12 | infile = sys.argv[1] |
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| 13 | y_col = int(sys.argv[2])-1 |
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| 14 | x_cols = sys.argv[3].split(',') |
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| 15 | outfile = sys.argv[4] |
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| 16 | outfile2 = sys.argv[5] |
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| 17 | |
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| 18 | print "Predictor columns: %s; Response column: %d" %(x_cols,y_col+1) |
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| 19 | fout = open(outfile,'w') |
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| 20 | elems = [] |
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| 21 | for i, line in enumerate( file ( infile )): |
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| 22 | line = line.rstrip('\r\n') |
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| 23 | if len( line )>0 and not line.startswith( '#' ): |
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| 24 | elems = line.split( '\t' ) |
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| 25 | break |
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| 26 | if i == 30: |
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| 27 | break # Hopefully we'll never get here... |
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| 28 | |
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| 29 | if len( elems )<1: |
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| 30 | stop_err( "The data in your input dataset is either missing or not formatted properly." ) |
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| 31 | |
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| 32 | y_vals = [] |
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| 33 | x_vals = [] |
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| 34 | |
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| 35 | for k,col in enumerate(x_cols): |
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| 36 | x_cols[k] = int(col)-1 |
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| 37 | x_vals.append([]) |
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| 38 | |
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| 39 | NA = 'NA' |
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| 40 | for ind,line in enumerate( file( infile )): |
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| 41 | if line and not line.startswith( '#' ): |
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| 42 | try: |
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| 43 | fields = line.split("\t") |
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| 44 | try: |
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| 45 | yval = float(fields[y_col]) |
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| 46 | except: |
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| 47 | yval = r('NA') |
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| 48 | y_vals.append(yval) |
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| 49 | for k,col in enumerate(x_cols): |
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| 50 | try: |
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| 51 | xval = float(fields[col]) |
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| 52 | except: |
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| 53 | xval = r('NA') |
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| 54 | x_vals[k].append(xval) |
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| 55 | except: |
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| 56 | pass |
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| 57 | |
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| 58 | x_vals1 = numpy.asarray(x_vals).transpose() |
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| 59 | |
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| 60 | dat= r.list(x=array(x_vals1), y=y_vals) |
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| 61 | |
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| 62 | set_default_mode(NO_CONVERSION) |
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| 63 | try: |
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| 64 | linear_model = r.lm(r("y ~ x"), data = r.na_exclude(dat)) |
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| 65 | except RException, rex: |
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| 66 | 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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| 67 | set_default_mode(BASIC_CONVERSION) |
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| 68 | |
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| 69 | coeffs=linear_model.as_py()['coefficients'] |
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| 70 | yintercept= coeffs['(Intercept)'] |
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| 71 | print >>fout, "Y-intercept\t%s" %(yintercept) |
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| 72 | summary = r.summary(linear_model) |
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| 73 | |
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| 74 | co = summary.get('coefficients', 'NA') |
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| 75 | """ |
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| 76 | if len(co) != len(x_vals)+1: |
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| 77 | stop_err("Stopped performing linear regression on the input data, since one of the predictor columns contains only non-numeric or invalid values.") |
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| 78 | """ |
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| 79 | print >>fout, "p-value (Y-intercept)\t%s" %(co[0][3]) |
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| 80 | |
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| 81 | if len(x_vals) == 1: #Simple linear regression case with 1 predictor variable |
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| 82 | try: |
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| 83 | slope = coeffs['x'] |
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| 84 | except: |
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| 85 | slope = 'NA' |
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| 86 | try: |
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| 87 | pval = co[1][3] |
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| 88 | except: |
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| 89 | pval = 'NA' |
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| 90 | print >>fout, "Slope (c%d)\t%s" %(x_cols[0]+1,slope) |
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| 91 | print >>fout, "p-value (c%d)\t%s" %(x_cols[0]+1,pval) |
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| 92 | else: #Multiple regression case with >1 predictors |
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| 93 | ind=1 |
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| 94 | while ind < len(coeffs.keys()): |
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| 95 | print >>fout, "Slope (c%d)\t%s" %(x_cols[ind-1]+1,coeffs['x'+str(ind)]) |
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| 96 | try: |
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| 97 | pval = co[ind][3] |
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| 98 | except: |
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| 99 | pval = 'NA' |
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| 100 | print >>fout, "p-value (c%d)\t%s" %(x_cols[ind-1]+1,pval) |
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| 101 | ind+=1 |
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| 102 | |
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| 103 | print >>fout, "R-squared\t%s" %(summary.get('r.squared','NA')) |
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| 104 | print >>fout, "Adjusted R-squared\t%s" %(summary.get('adj.r.squared','NA')) |
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| 105 | print >>fout, "F-statistic\t%s" %(summary.get('fstatistic','NA')) |
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| 106 | print >>fout, "Sigma\t%s" %(summary.get('sigma','NA')) |
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| 107 | |
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| 108 | r.pdf( outfile2, 8, 8 ) |
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| 109 | if len(x_vals) == 1: #Simple linear regression case with 1 predictor variable |
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| 110 | sub_title = "Slope = %s; Y-int = %s" %(slope,yintercept) |
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| 111 | try: |
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| 112 | r.plot(x=x_vals[0], y=y_vals, xlab="X", ylab="Y", sub=sub_title, main="Scatterplot with regression") |
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| 113 | r.abline(a=yintercept, b=slope, col="red") |
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| 114 | except: |
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| 115 | pass |
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| 116 | else: |
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| 117 | r.pairs(dat, main="Scatterplot Matrix", col="blue") |
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| 118 | try: |
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| 119 | r.plot(linear_model) |
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| 120 | except: |
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| 121 | pass |
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| 122 | r.dev_off() |
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