# HG changeset patch
# User devteam
# Date 1400517288 14400
# Node ID 9bc0c48a027fd0cbc3f45dfb70708c8709331443
Imported from capsule None
diff -r 000000000000 -r 9bc0c48a027f cca.py
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/cca.py Mon May 19 12:34:48 2014 -0400
@@ -0,0 +1,158 @@
+#!/usr/bin/env python
+
+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()
\ No newline at end of file
diff -r 000000000000 -r 9bc0c48a027f cca.xml
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/cca.xml Mon May 19 12:34:48 2014 -0400
@@ -0,0 +1,98 @@
+
+
+
+ R
+ numpy
+ yacca
+ rpy
+
+
+ cca.py
+ $input1
+ $x_cols
+ $y_cols
+ $x_scale
+ $y_scale
+ $std_scores
+ $out_file1
+ $out_file2
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+.. class:: infomark
+
+**TIP:** If your data is not TAB delimited, use *Edit Datasets->Convert characters*
+
+-----
+
+.. class:: infomark
+
+**What it does**
+
+This tool uses functions from 'yacca' library from R statistical package to perform Canonical Correlation Analysis (CCA) on the input data. It outputs two files, one containing the summary statistics of the performed CCA, and the other containing helioplots, which display structural loadings of X and Y variables on different canonical components.
+
+*Carter T. Butts (2009). yacca: Yet Another Canonical Correlation Analysis Package. R package version 1.1.*
+
+-----
+
+.. class:: warningmark
+
+**Note**
+
+- This tool currently treats all predictor and response variables as continuous numeric variables. Running the tool on categorical variables might result in incorrect results.
+
+- Rows containing non-numeric (or missing) data in any of the chosen columns will be skipped from the analysis.
+
+- The summary statistics in the output are described below:
+
+ - correlation: Canonical correlation between the canonical variates (i.e. transformed variables)
+ - F-statistic: F-value obtained from F Test for Canonical Correlations Using Rao's Approximation
+ - p-value: denotes significance of canonical correlations
+ - Coefficients: represent the coefficients of X and Y variables on each canonical variate
+ - Loadings: represent the correlations between the original variables in each set and their respective canonical variates
+ - CrossLoadings: represent the correlations between the original variables in each set and the opposite canonical variates
+
+
+
diff -r 000000000000 -r 9bc0c48a027f test-data/1.tabular
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/1.tabular Mon May 19 12:34:48 2014 -0400
@@ -0,0 +1,6 @@
+chr22 1000 NM_17
+chr22 2000 NM_18
+chr10 2200 NM_10
+chr10 hap test
+chr10 1200 NM_11
+chr22 1600 NM_19
\ No newline at end of file
diff -r 000000000000 -r 9bc0c48a027f test-data/cca_out1.tabular
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/cca_out1.tabular Mon May 19 12:34:48 2014 -0400
@@ -0,0 +1,22 @@
+#Component 1 2
+#Correlation 0.9409 0.1311
+#F-statistic 144.4 2.57
+#p-value 6.213e-68 0.1111
+#X-Coefficients 1 2
+c3 1.507 -3.378
+c4 -0.5372 3.659
+#Y-Coefficients 1 2
+c1 6.35 3.379
+c2 -2.66 6.67
+#X-Loadings 1 2
+c3 0.9894 0.1452
+c4 0.9133 0.4073
+#Y-Loadings 1 2
+c1 0.9289 0.3704
+c2 -0.4698 0.8828
+#X-CrossLoadings 1 2
+c3 0.9309 0.01904
+c4 0.8593 0.05339
+#Y-CrossLoadings 1 2
+c1 0.874 0.04855
+c2 -0.442 0.1157
diff -r 000000000000 -r 9bc0c48a027f test-data/cca_out2.pdf
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/cca_out2.pdf Mon May 19 12:34:48 2014 -0400
@@ -0,0 +1,858 @@
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/iris.tabular Mon May 19 12:34:48 2014 -0400
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000
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