annotate tools/multivariate_stats/cca.py @ 2:c2a356708570

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#!/usr/bin/env python

from galaxy import eggs
import sys, string
from rpy import *
import numpy

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

infile = sys.argv[1]
x_cols = sys.argv[2].split(',')
y_cols = sys.argv[3].split(',')

x_scale = x_center = "FALSE"
if sys.argv[4] == 'both':
    x_scale = x_center = "TRUE"
elif sys.argv[4] == 'center':
    x_center = "TRUE"
elif sys.argv[4] == 'scale':
    x_scale = "TRUE"
    
y_scale = y_center = "FALSE"
if sys.argv[5] == 'both':
    y_scale = y_center = "TRUE"
elif sys.argv[5] == 'center':
    y_center = "TRUE"
elif sys.argv[5] == 'scale':
    y_scale = "TRUE"

std_scores = "FALSE"   
if sys.argv[6] == "yes":
    std_scores = "TRUE"
    
outfile = sys.argv[7]
outfile2 = sys.argv[8]

fout = open(outfile,'w')
elems = []
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." )

x_vals = []

for k,col in enumerate(x_cols):
    x_cols[k] = int(col)-1
    x_vals.append([])

y_vals = []

for k,col in enumerate(y_cols):
    y_cols[k] = int(col)-1
    y_vals.append([])

skipped = 0
for ind,line in enumerate( file( infile )):
    if line and not line.startswith( '#' ):
        try:
            fields = line.strip().split("\t")
            valid_line = True
            for col in x_cols+y_cols:
                try:
                    assert float(fields[col])
                except:
                    skipped += 1
                    valid_line = False
                    break
            if valid_line:
                for k,col in enumerate(x_cols):
                    try:
                        xval = float(fields[col])
                    except:
                        xval = NaN#
                    x_vals[k].append(xval)
                for k,col in enumerate(y_cols):
                    try:
                        yval = float(fields[col])
                    except:
                        yval = NaN#
                    y_vals[k].append(yval)
        except:
            skipped += 1

x_vals1 = numpy.asarray(x_vals).transpose()
y_vals1 = numpy.asarray(y_vals).transpose()

x_dat= r.list(array(x_vals1))
y_dat= r.list(array(y_vals1))

try:
    r.suppressWarnings(r.library("yacca"))
except:
    stop_err("Missing R library yacca.")
    
set_default_mode(NO_CONVERSION)
try:
    xcolnames = ["c%d" %(el+1) for el in x_cols]
    ycolnames = ["c%d" %(el+1) for el in y_cols]
    cc = r.cca(x=x_dat, y=y_dat, xlab=xcolnames, ylab=ycolnames, xcenter=r(x_center), ycenter=r(y_center), xscale=r(x_scale), yscale=r(y_scale), standardize_scores=r(std_scores))
    ftest = r.F_test_cca(cc)
except RException, rex:
    stop_err("Encountered error while performing CCA on the input data: %s" %(rex))

set_default_mode(BASIC_CONVERSION)
summary = r.summary(cc)

ncomps = len(summary['corr'])
comps = summary['corr'].keys()
corr = summary['corr'].values()
xlab = summary['xlab']
ylab = summary['ylab']

for i in range(ncomps):
    corr[comps.index('CV %s' %(i+1))] = summary['corr'].values()[i]

ftest=ftest.as_py()
print >>fout, "#Component\t%s" %("\t".join(["%s" % el for el in range(1,ncomps+1)]))
print >>fout, "#Correlation\t%s" %("\t".join(["%.4g" % el for el in corr]))
print >>fout, "#F-statistic\t%s" %("\t".join(["%.4g" % el for el in ftest['statistic']]))
print >>fout, "#p-value\t%s" %("\t".join(["%.4g" % el for el in ftest['p.value']]))

print >>fout, "#X-Coefficients\t%s" %("\t".join(["%s" % el for el in range(1,ncomps+1)]))
for i,val in enumerate(summary['xcoef']):
    print >>fout, "%s\t%s" %(xlab[i], "\t".join(["%.4g" % el for el in val]))

print >>fout, "#Y-Coefficients\t%s" %("\t".join(["%s" % el for el in range(1,ncomps+1)]))
for i,val in enumerate(summary['ycoef']):
    print >>fout, "%s\t%s" %(ylab[i], "\t".join(["%.4g" % el for el in val]))
       
print >>fout, "#X-Loadings\t%s" %("\t".join(["%s" % el for el in range(1,ncomps+1)]))
for i,val in enumerate(summary['xstructcorr']):
    print >>fout, "%s\t%s" %(xlab[i], "\t".join(["%.4g" % el for el in val]))

print >>fout, "#Y-Loadings\t%s" %("\t".join(["%s" % el for el in range(1,ncomps+1)]))
for i,val in enumerate(summary['ystructcorr']):
    print >>fout, "%s\t%s" %(ylab[i], "\t".join(["%.4g" % el for el in val]))

print >>fout, "#X-CrossLoadings\t%s" %("\t".join(["%s" % el for el in range(1,ncomps+1)]))
for i,val in enumerate(summary['xcrosscorr']):
    print >>fout, "%s\t%s" %(xlab[i], "\t".join(["%.4g" % el for el in val]))

print >>fout, "#Y-CrossLoadings\t%s" %("\t".join(["%s" % el for el in range(1,ncomps+1)]))
for i,val in enumerate(summary['ycrosscorr']):
    print >>fout, "%s\t%s" %(ylab[i], "\t".join(["%.4g" % el for el in val]))

r.pdf( outfile2, 8, 8 )
#r.plot(cc)
for i in range(ncomps):
    r.helio_plot(cc, cv = i+1, main = r.paste("Explained Variance for CV",i+1), type = "variance")
r.dev_off()