comparison partialR_square.py @ 0:ffcdde989859 draft

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0:ffcdde989859

#!/usr/bin/env python

#from galaxy import eggs

import sys, string
#from rpy import *

import rpy2.robjects as robjects
import rpy2.rlike.container as rlc
r = robjects.r
import numpy

#export PYTHONPATH=~/galaxy/lib/
#running command python partialR_square.py reg_inp.tab 4 1,2,3 partialR_result.tabular

def stop_err(msg):
    sys.stderr.write(msg)
    sys.exit()

def sscombs(s):
    if len(s) == 1:
        return [s]
    else:
        ssc = sscombs(s[1:])
        return [s[0]] + [s[0]+comb for comb in ssc] + ssc


infile = sys.argv[1]
y_col = int(sys.argv[2])-1
x_cols = sys.argv[3].split(',')
outfile = sys.argv[4]

print "Predictor columns: %s; Response column: %d" %(x_cols,y_col+1)
fout = open(outfile,'w')

for i, line in enumerate( file ( infile )):
    line = line.rstrip('\r\n')
    if len( line )>0 and not line.startswith( '#' ):
        elems = line.split( '\t' )
        break 
    if i == 30:
        break # Hopefully we'll never get here...

if len( elems )<1:
    stop_err( "The data in your input dataset is either missing or not formatted properly." )

y_vals = []
x_vals = []
x_vector = []
for k,col in enumerate(x_cols):
    x_cols[k] = int(col)-1
    x_vals.append([])    
    """
    try:
        float( elems[x_cols[k]] )
    except:
        try:
            msg = "This operation cannot be performed on non-numeric column %d containing value '%s'." %( col, elems[x_cols[k]] )
        except:
            msg = "This operation cannot be performed on non-numeric data."
        stop_err( msg )
    """
NA = 'NA'
for ind,line in enumerate( file( infile )):
    if line and not line.startswith( '#' ):
        try:
            fields = line.split("\t")
            try:
                yval = float(fields[y_col])
            except Exception, ey:
                yval = r('NA')
                #print >>sys.stderr, "ey = %s" %ey
            y_vals.append(yval)
            for k,col in enumerate(x_cols):
                try:
                    xval = float(fields[col])
                except Exception, ex:
                    xval = r('NA')
                    #print >>sys.stderr, "ex = %s" %ex
                x_vals[k].append(xval)
                x_vector.append(xval)
        except:
            pass

#x_vals1 = numpy.asarray(x_vals).transpose()
#dat= r.list(x=array(x_vals1), y=y_vals)

#set_default_mode(NO_CONVERSION)
#try:
#    full = r.lm(r("y ~ x"), data= r.na_exclude(dat))    #full model includes all the predictor variables specified by the user
#except RException, rex:
#    stop_err("Error performing linear regression on the input data.\nEither the response column or one of the predictor columns contain no numeric values.")
#set_default_mode(BASIC_CONVERSION)

fv = robjects.FloatVector(x_vector)
m = r['matrix'](fv, ncol=len(x_cols),byrow=True)
# ensure order for generating formula
od = rlc.OrdDict([('y',robjects.FloatVector(y_vals)),('x',m)])
dat = robjects.DataFrame(od)
# convert dat.names: ["y","x.1","x.2"] to formula string: 'y ~ x.1 + x.2'
formula = ' + '.join(dat.names).replace('+','~',1)
try:
    full = r.lm(formula,  data =  r['na.exclude'](dat))
except RException, rex:
    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.")



summary = r.summary(full)
#fullr2 = summary.get('r.squared','NA')
fullr2 = summary.rx2('r.squared')[0]

if fullr2 == 'NA':
    stop_error("Error in linear regression")

if len(x_vals) < 10:
    s = ""
    for ch in range(len(x_vals)):
        s += str(ch)
else:
    stop_err("This tool only works with less than 10 predictors.")

print >>fout, "#Model\tR-sq\tpartial_R_Terms\tpartial_R_Value"
all_combos = sorted(sscombs(s), key=len)
all_combos.reverse()
for j,cols in enumerate(all_combos):
    #if len(cols) == len(s):    #Same as the full model above
    #    continue
    if len(cols) == 1:
        #x_vals1 = x_vals[int(cols)]
        x_v = x_vals[int(cols)]        
    else:
        x_v = []
        for col in cols:
            #x_v.append(x_vals[int(col)])
            x_v.extend(x_vals[int(col)])
        #x_vals1 = numpy.asarray(x_v).transpose()
    #dat= r.list(x=array(x_vals1), y=y_vals)
    #set_default_mode(NO_CONVERSION)
    #red = r.lm(r("y ~ x"), data= dat)    #Reduced model
    #set_default_mode(BASIC_CONVERSION)
    fv = robjects.FloatVector(x_v)
    m = r['matrix'](fv, ncol=len(cols),byrow=False)
    # ensure order for generating formula
    od = rlc.OrdDict([('y',robjects.FloatVector(y_vals)),('x',m)])
    dat = robjects.DataFrame(od)
    # convert dat.names: ["y","x.1","x.2"] to formula string: 'y ~ x.1 + x.2'
    formula = ' + '.join(dat.names).replace('+','~',1)
    try:
        red = r.lm(formula,  data =  r['na.exclude'](dat))
    except RException, rex:
        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.")
    

    summary = r.summary(red)
    #redr2 = summary.get('r.squared','NA')
    redr2 = summary.rx2('r.squared')[0]

    try:
        partial_R = (float(fullr2)-float(redr2))/(1-float(redr2))
    except:
        partial_R = 'NA'
    col_str = ""
    for col in cols:
        col_str = col_str + str(int(x_cols[int(col)]) + 1) + " "
    col_str.strip()
    partial_R_col_str = ""
    for col in s:
        if col not in cols:
            partial_R_col_str = partial_R_col_str + str(int(x_cols[int(col)]) + 1) + " "
    partial_R_col_str.strip()
    if len(cols) == len(s):    #full model
        partial_R_col_str = "-"
        partial_R = "-"
    try:
        redr2 = "%.4f" %(float(redr2))
    except:
        pass
    try:
        partial_R = "%.4f" %(float(partial_R))
    except:
        pass
    print >>fout, "%s\t%s\t%s\t%s" %(col_str,redr2,partial_R_col_str,partial_R)