# HG changeset patch
# User devteam
# Date 1400517271 14400
# Node ID e9ebd4bfbdfcba7df079158804d12db27857c631
Imported from capsule None
diff -r 000000000000 -r e9ebd4bfbdfc kpca.py
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/kpca.py Mon May 19 12:34:31 2014 -0400
@@ -0,0 +1,134 @@
+#!/usr/bin/env python
+
+"""
+Run kernel PCA using kpca() from R 'kernlab' package
+
+usage: %prog [options]
+ -i, --input=i: Input file
+ -o, --output1=o: Summary output
+ -p, --output2=p: Figures output
+ -c, --var_cols=c: Variable columns
+ -k, --kernel=k: Kernel function
+ -f, --features=f: Number of principal components to return
+ -s, --sigma=s: sigma
+ -d, --degree=d: degree
+ -l, --scale=l: scale
+ -t, --offset=t: offset
+ -r, --order=r: order
+
+usage: %prog input output1 output2 var_cols kernel features sigma(or_None) degree(or_None) scale(or_None) offset(or_None) order(or_None)
+"""
+
+import sys, string
+from rpy import *
+import numpy
+from bx.cookbook import doc_optparse
+
+
+def stop_err(msg):
+ sys.stderr.write(msg)
+ sys.exit()
+
+#Parse Command Line
+options, args = doc_optparse.parse( __doc__ )
+#{'options= kernel': 'rbfdot', 'var_cols': '1,2,3,4', 'degree': 'None', 'output2': '/afs/bx.psu.edu/home/gua110/workspace/galaxy_bitbucket/database/files/000/dataset_260.dat', 'output1': '/afs/bx.psu.edu/home/gua110/workspace/galaxy_bitbucket/database/files/000/dataset_259.dat', 'scale': 'None', 'offset': 'None', 'input': '/afs/bx.psu.edu/home/gua110/workspace/galaxy_bitbucket/database/files/000/dataset_256.dat', 'sigma': '1.0', 'order': 'None'}
+
+infile = options.input
+x_cols = options.var_cols.split(',')
+kernel = options.kernel
+outfile = options.output1
+outfile2 = options.output2
+ncomps = int(options.features)
+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([])
+
+NA = 'NA'
+skipped = 0
+for ind,line in enumerate( file( infile )):
+ if line and not line.startswith( '#' ):
+ try:
+ fields = line.strip().split("\t")
+ for k,col in enumerate(x_cols):
+ try:
+ xval = float(fields[col])
+ except:
+ #xval = r('NA')
+ xval = NaN#
+ x_vals[k].append(xval)
+ except:
+ skipped += 1
+
+x_vals1 = numpy.asarray(x_vals).transpose()
+dat= r.list(array(x_vals1))
+
+print r('library("kernlab")')
+
+try:
+ r.suppressWarnings(r.library('kernlab'))
+except:
+ stop_err('Missing R library kernlab')
+
+set_default_mode(NO_CONVERSION)
+if kernel=="rbfdot" or kernel=="anovadot":
+ pars = r.list(sigma=float(options.sigma))
+elif kernel=="polydot":
+ pars = r.list(degree=float(options.degree),scale=float(options.scale),offset=float(options.offset))
+elif kernel=="tanhdot":
+ pars = r.list(scale=float(options.scale),offset=float(options.offset))
+elif kernel=="besseldot":
+ pars = r.list(degree=float(options.degree),sigma=float(options.sigma),order=float(options.order))
+elif kernel=="anovadot":
+ pars = r.list(degree=float(options.degree),sigma=float(options.sigma))
+else:
+ pars = r.list()
+
+try:
+ kpc = r.kpca(x=r.na_exclude(dat), kernel=kernel, kpar=pars, features=ncomps)
+except RException, rex:
+ stop_err("Encountered error while performing kPCA on the input data: %s" %(rex))
+set_default_mode(BASIC_CONVERSION)
+
+eig = r.eig(kpc)
+pcv = r.pcv(kpc)
+rotated = r.rotated(kpc)
+
+comps = eig.keys()
+eigv = eig.values()
+for i in range(ncomps):
+ eigv[comps.index('Comp.%s' %(i+1))] = eig.values()[i]
+
+print >>fout, "#Component\t%s" %("\t".join(["%s" % el for el in range(1,ncomps+1)]))
+
+print >>fout, "#Eigenvalue\t%s" %("\t".join(["%.4g" % el for el in eig.values()]))
+
+print >>fout, "#Principal component vectors\t%s" %("\t".join(["%s" % el for el in range(1,ncomps+1)]))
+for obs,val in enumerate(pcv):
+ print >>fout, "%s\t%s" %(obs+1, "\t".join(["%.4g" % el for el in val]))
+
+print >>fout, "#Rotated values\t%s" %("\t".join(["%s" % el for el in range(1,ncomps+1)]))
+for obs,val in enumerate(rotated):
+ print >>fout, "%s\t%s" %(obs+1, "\t".join(["%.4g" % el for el in val]))
+
+r.pdf( outfile2, 8, 8 )
+if ncomps != 1:
+ r.pairs(rotated,labels=r.list(range(1,ncomps+1)),main="Scatterplot of rotated values")
+else:
+ r.plot(rotated, ylab='Comp.1', main="Scatterplot of rotated values")
+r.dev_off()
\ No newline at end of file
diff -r 000000000000 -r e9ebd4bfbdfc kpca.xml
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/kpca.xml Mon May 19 12:34:31 2014 -0400
@@ -0,0 +1,144 @@
+
+
+
+ rpy
+ R
+ kernlab
+ numpy
+ bx-python
+
+
+ kpca.py
+ --input=$input1
+ --output1=$out_file1
+ --output2=$out_file2
+ --var_cols=$var_cols
+ --kernel=$kernelChoice.kernel
+ --features=$features
+ #if $kernelChoice.kernel == "rbfdot" or $kernelChoice.kernel == "anovadot":
+ --sigma=$kernelChoice.sigma
+ --degree="None"
+ --scale="None"
+ --offset="None"
+ --order="None"
+ #elif $kernelChoice.kernel == "polydot":
+ --sigma="None"
+ --degree=$kernelChoice.degree
+ --scale=$kernelChoice.scale
+ --offset=$kernelChoice.offset
+ --order="None"
+ #elif $kernelChoice.kernel == "tanhdot":
+ --sigma="None"
+ --degree="None"
+ --scale=$kernelChoice.scale
+ --offset=$kernelChoice.offset
+ --order="None"
+ #elif $kernelChoice.kernel == "besseldot":
+ --sigma=$kernelChoice.sigma
+ --degree=$kernelChoice.degree
+ --scale="None"
+ --offset="None"
+ --order=$kernelChoice.order
+ #elif $kernelChoice.kernel == "anovadot":
+ --sigma=$kernelChoice.sigma
+ --degree=$kernelChoice.degree
+ --scale="None"
+ --offset="None"
+ --order="None"
+ #else:
+ --sigma="None"
+ --degree="None"
+ --scale="None"
+ --offset="None"
+ --order="None"
+ #end if
+
+
+
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+
+
+
+
+
+.. 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 'kernlab' library from R statistical package to perform Kernel Principal Component Analysis (kPCA) on the input data. It outputs two files, one containing the summary statistics of the performed kPCA, and the other containing a scatterplot matrix of rotated values reported by kPCA.
+
+*Alexandros Karatzoglou, Alex Smola, Kurt Hornik, Achim Zeileis (2004). kernlab - An S4 Package for Kernel Methods in R. Journal of Statistical Software 11(9), 1-20. URL http://www.jstatsoft.org/v11/i09/*
+
+-----
+
+.. class:: warningmark
+
+**Note**
+
+This tool currently treats all 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.
+
+
+
diff -r 000000000000 -r e9ebd4bfbdfc test-data/1.tabular
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/1.tabular Mon May 19 12:34:31 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 e9ebd4bfbdfc test-data/iris.tabular
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/iris.tabular Mon May 19 12:34:31 2014 -0400
@@ -0,0 +1,151 @@
+5.1 3.5 1.4 0.2 Iris-setosa
+4.9 3.0 1.4 0.2 Iris-setosa
+4.7 3.2 1.3 0.2 Iris-setosa
+4.6 3.1 1.5 0.2 Iris-setosa
+5.0 3.6 1.4 0.2 Iris-setosa
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+4.6 3.4 1.4 0.3 Iris-setosa
+5.0 3.4 1.5 0.2 Iris-setosa
+4.4 2.9 1.4 0.2 Iris-setosa
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+4.8 3.4 1.6 0.2 Iris-setosa
+4.8 3.0 1.4 0.1 Iris-setosa
+4.3 3.0 1.1 0.1 Iris-setosa
+5.8 4.0 1.2 0.2 Iris-setosa
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+5.4 3.9 1.3 0.4 Iris-setosa
+5.1 3.5 1.4 0.3 Iris-setosa
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+6.8 3.2 5.9 2.3 Iris-virginica
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+6.3 2.5 5.0 1.9 Iris-virginica
+6.5 3.0 5.2 2.0 Iris-virginica
+6.2 3.4 5.4 2.3 Iris-virginica
+5.9 3.0 5.1 1.8 Iris-virginica
+
diff -r 000000000000 -r e9ebd4bfbdfc test-data/kpca_out1.tabular
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/kpca_out1.tabular Mon May 19 12:34:31 2014 -0400
@@ -0,0 +1,304 @@
+#Component 1 2
+#Eigenvalue 748.5 31.76
+#Principal component vectors 1 2
+1 -0.003553 0.0108
+2 -0.003722 -0.003192
+3 -0.003887 -0.003062
+4 -0.0038 -0.007509
+5 -0.0036 0.01068
+6 -0.002995 0.02441
+7 -0.003814 -0.001828
+8 -0.00354 0.006305
+9 -0.004002 -0.01452
+10 -0.003663 -0.001368
+11 -0.003267 0.02134
+12 -0.003575 0.001815
+13 -0.003807 -0.004912
+14 -0.004281 -0.01352
+15 -0.003251 0.03864
+16 -0.002922 0.04394
+17 -0.003349 0.02585
+18 -0.003521 0.01053
+19 -0.002847 0.02952
+20 -0.003403 0.01643
+21 -0.003128 0.01419
+22 -0.003382 0.01406
+23 -0.004124 0.003402
+24 -0.003227 0.004863
+25 -0.003329 0.0007417
+26 -0.003506 -0.001891
+27 -0.003393 0.005424
+28 -0.003411 0.01263
+29 -0.003506 0.01091
+30 -0.003655 -0.004034
+31 -0.00361 -0.003953
+32 -0.003234 0.01438
+33 -0.003377 0.02573
+34 -0.003236 0.0352
+35 -0.003663 -0.001368
+36 -0.003801 0.003321
+37 -0.003401 0.01999
+38 -0.003663 -0.001368
+39 -0.004064 -0.01246
+40 -0.003482 0.008422
+41 -0.00366 0.008718
+42 -0.004057 -0.02284
+43 -0.004044 -0.008892
+44 -0.003315 0.006892
+45 -0.003022 0.01469
+46 -0.003747 -0.005408
+47 -0.003352 0.01636
+48 -0.003865 -0.00534
+49 -0.003328 0.01907
+50 -0.003632 0.004649
+51 0.002131 0.02353
+52 0.001268 0.01077
+53 0.00232 0.0165
+54 -0.0005446 -0.02218
+55 0.001391 0.002863
+56 0.0003522 -0.01197
+57 0.001503 0.008832
+58 -0.001879 -0.02529
+59 0.001425 0.008304
+60 -0.000784 -0.0196
+61 -0.001688 -0.03188
+62 0.000332 -0.002273
+63 -0.0002401 -0.01364
+64 0.001079 -0.003714
+65 -0.0007557 -0.005543
+66 0.001375 0.01646
+67 0.0004002 -0.01059
+68 -0.0001866 -0.007917
+69 0.0007813 -0.01567
+70 -0.0006023 -0.01492
+71 0.001281 -0.003829
+72 0.0001411 -0.0001777
+73 0.00148 -0.01053
+74 0.0009533 -0.005025
+75 0.0008315 0.006252
+76 0.001242 0.01179
+77 0.001929 0.008282
+78 0.002309 0.007654
+79 0.000773 -0.00466
+80 -0.0009729 -0.007942
+81 -0.0008252 -0.01809
+82 -0.0009752 -0.01705
+83 -0.0003282 -0.007332
+84 0.001528 -0.01434
+85 0.0002046 -0.0145
+86 0.0009586 0.006116
+87 0.001825 0.01357
+88 0.0006918 -0.009912
+89 -0.0001648 -0.006837
+90 -0.0004951 -0.01827
+91 -5.753e-05 -0.01874
+92 0.00098 -0.0006915
+93 -0.0002379 -0.01011
+94 -0.001821 -0.02537
+95 -0.0001244 -0.01378
+96 1.283e-06 -0.005116
+97 2.13e-05 -0.007654
+98 0.0006301 0.001939
+99 -0.001958 -0.01857
+100 -0.000121 -0.00904
+101 0.003803 -0.00735
+102 0.001462 -0.01964
+103 0.004318 0.006231
+104 0.002705 -0.009198
+105 0.003481 -0.006612
+106 0.006089 0.01059
+107 -0.0003162 -0.03404
+108 0.004981 0.005895
+109 0.003329 -0.01195
+110 0.005217 0.01984
+111 0.002443 0.005619
+112 0.002393 -0.009436
+113 0.003352 0.003334
+114 0.001216 -0.02527
+115 0.001742 -0.01987
+116 0.002768 0.0001267
+117 0.002831 -0.001715
+118 0.006776 0.03336
+119 0.006694 -0.002136
+120 0.0012 -0.02356
+121 0.003951 0.00754
+122 0.001074 -0.02014
+123 0.006251 0.00718
+124 0.001691 -0.007518
+125 0.003625 0.006525
+126 0.004493 0.01431
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diff -r 000000000000 -r e9ebd4bfbdfc tool_dependencies.xml
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