Mercurial > repos > fubar > rglasso_1_9_8
changeset 2:359c66be8677 draft
Uploaded
| author | fubar |
|---|---|
| date | Tue, 21 Oct 2014 22:53:57 -0400 |
| parents | 2056139d55d6 |
| children | e0e11c2cae3f |
| files | rgToolFactory.py rglasso.xml rglasso_cox.xml test-data/cox_coxlassotest_glmnet_cvdeviance.pdf test-data/cox_coxlassotest_glmnetdev.pdf test-data/cox_test.xls test-data/coxlassotest.html test-data/coxlassotest_modelres.xls tool_dependencies.xml |
| diffstat | 9 files changed, 1880 insertions(+), 348 deletions(-) [+] |
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/rgToolFactory.py Tue Oct 21 22:53:57 2014 -0400 @@ -0,0 +1,658 @@ +# rgToolFactory.py +# see https://bitbucket.org/fubar/galaxytoolfactory/wiki/Home +# +# copyright ross lazarus (ross stop lazarus at gmail stop com) May 2012 +# +# all rights reserved +# Licensed under the LGPL +# suggestions for improvement and bug fixes welcome at https://bitbucket.org/fubar/galaxytoolfactory/wiki/Home +# +# july 2014 +# added buffered read of sterror after run +# +# august 2013 +# found a problem with GS if $TMP or $TEMP missing - now inject /tmp and warn +# +# july 2013 +# added ability to combine images and individual log files into html output +# just make sure there's a log file foo.log and it will be output +# together with all images named like "foo_*.pdf +# otherwise old format for html +# +# January 2013 +# problem pointed out by Carlos Borroto +# added escaping for <>$ - thought I did that ages ago... +# +# August 11 2012 +# changed to use shell=False and cl as a sequence + +# This is a Galaxy tool factory for simple scripts in python, R or whatever ails ye. +# It also serves as the wrapper for the new tool. +# +# you paste and run your script +# Only works for simple scripts that read one input from the history. +# Optionally can write one new history dataset, +# and optionally collect any number of outputs into links on an autogenerated HTML page. + +# DO NOT install on a public or important site - please. + +# installed generated tools are fine if the script is safe. +# They just run normally and their user cannot do anything unusually insecure +# but please, practice safe toolshed. +# Read the fucking code before you install any tool +# especially this one + +# After you get the script working on some test data, you can +# optionally generate a toolshed compatible gzip file +# containing your script safely wrapped as an ordinary Galaxy script in your local toolshed for +# safe and largely automated installation in a production Galaxy. + +# If you opt for an HTML output, you get all the script outputs arranged +# as a single Html history item - all output files are linked, thumbnails for all the pdfs. +# Ugly but really inexpensive. +# +# Patches appreciated please. +# +# +# long route to June 2012 product +# Behold the awesome power of Galaxy and the toolshed with the tool factory to bind them +# derived from an integrated script model +# called rgBaseScriptWrapper.py +# Note to the unwary: +# This tool allows arbitrary scripting on your Galaxy as the Galaxy user +# There is nothing stopping a malicious user doing whatever they choose +# Extremely dangerous!! +# Totally insecure. So, trusted users only +# +# preferred model is a developer using their throw away workstation instance - ie a private site. +# no real risk. The universe_wsgi.ini admin_users string is checked - only admin users are permitted to run this tool. +# + +import sys +import shutil +import subprocess +import os +import time +import tempfile +import optparse +import tarfile +import re +import shutil +import math + +progname = os.path.split(sys.argv[0])[1] +myversion = 'V000.2 June 2012' +verbose = False +debug = False +toolFactoryURL = 'https://bitbucket.org/fubar/galaxytoolfactory' +buffsize = 1048576 + + +def timenow(): + """return current time as a string + """ + return time.strftime('%d/%m/%Y %H:%M:%S', time.localtime(time.time())) + +html_escape_table = { + "&": "&", + ">": ">", + "<": "<", + "$": "\$" + } + +def html_escape(text): + """Produce entities within text.""" + return "".join(html_escape_table.get(c,c) for c in text) + +def cmd_exists(cmd): + return subprocess.call("type " + cmd, shell=True, + stdout=subprocess.PIPE, stderr=subprocess.PIPE) == 0 + + +class ScriptRunner: + """class is a wrapper for an arbitrary script + """ + + def __init__(self,opts=None,treatbashSpecial=True): + """ + cleanup inputs, setup some outputs + + """ + self.useGM = cmd_exists('gm') + self.useIM = cmd_exists('convert') + self.useGS = cmd_exists('gs') + self.temp_warned = False # we want only one warning if $TMP not set + self.treatbashSpecial = treatbashSpecial + if opts.output_dir: # simplify for the tool tarball + os.chdir(opts.output_dir) + self.thumbformat = 'png' + self.opts = opts + self.toolname = re.sub('[^a-zA-Z0-9_]+', '', opts.tool_name) # a sanitizer now does this but.. + self.toolid = self.toolname + self.myname = sys.argv[0] # get our name because we write ourselves out as a tool later + self.pyfile = self.myname # crude but efficient - the cruft won't hurt much + self.xmlfile = '%s.xml' % self.toolname + s = open(self.opts.script_path,'r').readlines() + s = [x.rstrip() for x in s] # remove pesky dos line endings if needed + self.script = '\n'.join(s) + fhandle,self.sfile = tempfile.mkstemp(prefix=self.toolname,suffix=".%s" % (opts.interpreter)) + tscript = open(self.sfile,'w') # use self.sfile as script source for Popen + tscript.write(self.script) + tscript.close() + self.indentedScript = '\n'.join([' %s' % x for x in s]) # for restructured text in help + self.escapedScript = '\n'.join([html_escape(x) for x in s]) + self.elog = os.path.join(self.opts.output_dir,"%s_error.log" % self.toolname) + self.tlog = os.path.join(self.opts.output_dir,"%s_runner.log" % self.toolname) + if opts.output_dir: # may not want these complexities + art = '%s.%s' % (self.toolname,opts.interpreter) + artpath = os.path.join(self.opts.output_dir,art) # need full path + artifact = open(artpath,'w') # use self.sfile as script source for Popen + artifact.write(self.script) + artifact.close() + self.cl = [] + self.html = [] + a = self.cl.append + a(opts.interpreter) + if self.treatbashSpecial and opts.interpreter in ['bash','sh']: + a(self.sfile) + else: + a('-') # stdin + a(opts.input_tab) + a(opts.output_tab) + self.outFormats = 'tabular' # TODO make this an option at tool generation time + self.inputFormats = 'tabular' # TODO make this an option at tool generation time + self.test1Input = '%s_test1_input.xls' % self.toolname + self.test1Output = '%s_test1_output.xls' % self.toolname + self.test1HTML = '%s_test1_output.html' % self.toolname + + def makeXML(self): + """ + Create a Galaxy xml tool wrapper for the new script as a string to write out + fixme - use templating or something less fugly than this example of what we produce + + <tool id="reverse" name="reverse" version="0.01"> + <description>a tabular file</description> + <command interpreter="python"> + reverse.py --script_path "$runMe" --interpreter "python" + --tool_name "reverse" --input_tab "$input1" --output_tab "$tab_file" + </command> + <inputs> + <param name="input1" type="data" format="tabular" label="Select a suitable input file from your history"/><param name="job_name" type="text" label="Supply a name for the outputs to remind you what they contain" value="reverse"/> + + </inputs> + <outputs> + <data format="tabular" name="tab_file" label="${job_name}"/> + + </outputs> + <help> + +**What it Does** + +Reverse the columns in a tabular file + + </help> + <configfiles> + <configfile name="runMe"> + +# reverse order of columns in a tabular file +import sys +inp = sys.argv[1] +outp = sys.argv[2] +i = open(inp,'r') +o = open(outp,'w') +for row in i: + rs = row.rstrip().split('\t') + rs.reverse() + o.write('\t'.join(rs)) + o.write('\n') +i.close() +o.close() + + + </configfile> + </configfiles> + </tool> + + """ + newXML="""<tool id="%(toolid)s" name="%(toolname)s" version="%(tool_version)s"> + %(tooldesc)s + %(command)s + <inputs> + %(inputs)s + </inputs> + <outputs> + %(outputs)s + </outputs> + <configfiles> + <configfile name="runMe"> + %(script)s + </configfile> + </configfiles> + %(tooltests)s + <help> + %(help)s + </help> + </tool>""" # needs a dict with toolname, toolid, interpreter, scriptname, command, inputs as a multi line string ready to write, outputs ditto, help ditto + + newCommand="""<command interpreter="python"> + %(toolname)s.py --script_path "$runMe" --interpreter "%(interpreter)s" + --tool_name "%(toolname)s" %(command_inputs)s %(command_outputs)s + </command>""" # may NOT be an input or htmlout + tooltestsTabOnly = """<tests><test> + <param name="input1" value="%(test1Input)s" ftype="tabular"/> + <param name="job_name" value="test1"/> + <param name="runMe" value="$runMe"/> + <output name="tab_file" file="%(test1Output)s" ftype="tabular"/> + </test></tests>""" + tooltestsHTMLOnly = """<tests><test> + <param name="input1" value="%(test1Input)s" ftype="tabular"/> + <param name="job_name" value="test1"/> + <param name="runMe" value="$runMe"/> + <output name="html_file" file="%(test1HTML)s" ftype="html" lines_diff="5"/> + </test></tests>""" + tooltestsBoth = """<tests><test> + <param name="input1" value="%(test1Input)s" ftype="tabular"/> + <param name="job_name" value="test1"/> + <param name="runMe" value="$runMe"/> + <output name="tab_file" file="%(test1Output)s" ftype="tabular" /> + <output name="html_file" file="%(test1HTML)s" ftype="html" lines_diff="10"/> + </test></tests>""" + xdict = {} + xdict['tool_version'] = self.opts.tool_version + xdict['test1Input'] = self.test1Input + xdict['test1HTML'] = self.test1HTML + xdict['test1Output'] = self.test1Output + if self.opts.make_HTML and self.opts.output_tab <> 'None': + xdict['tooltests'] = tooltestsBoth % xdict + elif self.opts.make_HTML: + xdict['tooltests'] = tooltestsHTMLOnly % xdict + else: + xdict['tooltests'] = tooltestsTabOnly % xdict + xdict['script'] = self.escapedScript + # configfile is least painful way to embed script to avoid external dependencies + # but requires escaping of <, > and $ to avoid Mako parsing + if self.opts.help_text: + xdict['help'] = open(self.opts.help_text,'r').read() + else: + xdict['help'] = 'Please ask the tool author for help as none was supplied at tool generation' + coda = ['**Script**','Pressing execute will run the following code over your input file and generate some outputs in your history::'] + coda.append(self.indentedScript) + coda.append('**Attribution** This Galaxy tool was created by %s at %s\nusing the Galaxy Tool Factory.' % (self.opts.user_email,timenow())) + coda.append('See %s for details of that project' % (toolFactoryURL)) + coda.append('Please cite: Creating re-usable tools from scripts: The Galaxy Tool Factory. Ross Lazarus; Antony Kaspi; Mark Ziemann; The Galaxy Team. ') + coda.append('Bioinformatics 2012; doi: 10.1093/bioinformatics/bts573') + xdict['help'] = '%s\n%s' % (xdict['help'],'\n'.join(coda)) + if self.opts.tool_desc: + xdict['tooldesc'] = '<description>%s</description>' % self.opts.tool_desc + else: + xdict['tooldesc'] = '' + xdict['command_outputs'] = '' + xdict['outputs'] = '' + if self.opts.input_tab <> 'None': + xdict['command_inputs'] = '--input_tab "$input1" ' # the space may matter a lot if we append something + xdict['inputs'] = '<param name="input1" type="data" format="%s" label="Select a suitable input file from your history"/> \n' % self.inputFormats + else: + xdict['command_inputs'] = '' # assume no input - eg a random data generator + xdict['inputs'] = '' + xdict['inputs'] += '<param name="job_name" type="text" label="Supply a name for the outputs to remind you what they contain" value="%s"/> \n' % self.toolname + xdict['toolname'] = self.toolname + xdict['toolid'] = self.toolid + xdict['interpreter'] = self.opts.interpreter + xdict['scriptname'] = self.sfile + if self.opts.make_HTML: + xdict['command_outputs'] += ' --output_dir "$html_file.files_path" --output_html "$html_file" --make_HTML "yes" ' + xdict['outputs'] += ' <data format="html" name="html_file" label="${job_name}.html"/>\n' + if self.opts.output_tab <> 'None': + xdict['command_outputs'] += ' --output_tab "$tab_file"' + xdict['outputs'] += ' <data format="%s" name="tab_file" label="${job_name}"/>\n' % self.outFormats + xdict['command'] = newCommand % xdict + xmls = newXML % xdict + xf = open(self.xmlfile,'w') + xf.write(xmls) + xf.write('\n') + xf.close() + # ready for the tarball + + + def makeTooltar(self): + """ + a tool is a gz tarball with eg + /toolname/tool.xml /toolname/tool.py /toolname/test-data/test1_in.foo ... + """ + retval = self.run() + if retval: + print >> sys.stderr,'## Run failed. Cannot build yet. Please fix and retry' + sys.exit(1) + self.makeXML() + tdir = self.toolname + os.mkdir(tdir) + if self.opts.input_tab <> 'None': # no reproducible test otherwise? TODO: maybe.. + testdir = os.path.join(tdir,'test-data') + os.mkdir(testdir) # make tests directory + shutil.copyfile(self.opts.input_tab,os.path.join(testdir,self.test1Input)) + if self.opts.output_tab <> 'None': + shutil.copyfile(self.opts.output_tab,os.path.join(testdir,self.test1Output)) + if self.opts.make_HTML: + shutil.copyfile(self.opts.output_html,os.path.join(testdir,self.test1HTML)) + if self.opts.output_dir: + shutil.copyfile(self.tlog,os.path.join(testdir,'test1_out.log')) + op = '%s.py' % self.toolname # new name + outpiname = os.path.join(tdir,op) # path for the tool tarball + pyin = os.path.basename(self.pyfile) # our name - we rewrite ourselves (TM) + notes = ['# %s - a self annotated version of %s generated by running %s\n' % (op,pyin,pyin),] + notes.append('# to make a new Galaxy tool called %s\n' % self.toolname) + notes.append('# User %s at %s\n' % (self.opts.user_email,timenow())) + pi = open(self.pyfile,'r').readlines() # our code becomes new tool wrapper (!) - first Galaxy worm + notes += pi + outpi = open(outpiname,'w') + outpi.write(''.join(notes)) + outpi.write('\n') + outpi.close() + stname = os.path.join(tdir,self.sfile) + if not os.path.exists(stname): + shutil.copyfile(self.sfile, stname) + xtname = os.path.join(tdir,self.xmlfile) + if not os.path.exists(xtname): + shutil.copyfile(self.xmlfile,xtname) + tarpath = "%s.gz" % self.toolname + tar = tarfile.open(tarpath, "w:gz") + tar.add(tdir,arcname=self.toolname) + tar.close() + shutil.copyfile(tarpath,self.opts.new_tool) + shutil.rmtree(tdir) + ## TODO: replace with optional direct upload to local toolshed? + return retval + + + def compressPDF(self,inpdf=None,thumbformat='png'): + """need absolute path to pdf + note that GS gets confoozled if no $TMP or $TEMP + so we set it + """ + assert os.path.isfile(inpdf), "## Input %s supplied to %s compressPDF not found" % (inpdf,self.myName) + our_env = os.environ.copy() + if not (our_env.get('TMP',None) or our_env.get('TEMP',None)): + our_env['TMP'] = '/tmp' + if not self.temp_warned: + print >> sys.stdout,'## WARNING - no $TMP or $TEMP!!! Please fix - using /tmp temporarily' + self.temp_warned = True + hlog = os.path.join(self.opts.output_dir,"compress_%s.txt" % os.path.basename(inpdf)) + sto = open(hlog,'w') + outpdf = '%s_compressed' % inpdf + cl = ["gs", "-sDEVICE=pdfwrite", "-dNOPAUSE", "-dUseCIEColor", "-dBATCH","-dPDFSETTINGS=/printer", "-sOutputFile=%s" % outpdf,inpdf] + x = subprocess.Popen(cl,stdout=sto,stderr=sto,cwd=self.opts.output_dir,env=our_env) + retval1 = x.wait() + sto.close() + if retval1 == 0: + os.unlink(inpdf) + shutil.move(outpdf,inpdf) + os.unlink(hlog) + else: + x = open(hlog,'r').readlines() + print >> sys.stdout,x + hlog = os.path.join(self.opts.output_dir,"thumbnail_%s.txt" % os.path.basename(inpdf)) + sto = open(hlog,'w') + outpng = '%s.%s' % (os.path.splitext(inpdf)[0],thumbformat) + if self.useGM: + cl2 = ['gm', 'convert', inpdf, outpng] + else: # assume imagemagick + cl2 = ['convert', inpdf, outpng] + x = subprocess.Popen(cl2,stdout=sto,stderr=sto,cwd=self.opts.output_dir,env=our_env) + retval2 = x.wait() + sto.close() + if retval2 <> 0: + x = open(hlog,'r').readlines() + print >> sys.stdout,x + else: + os.unlink(hlog) + retval = retval1 or retval2 + return retval + + + def getfSize(self,fpath,outpath): + """ + format a nice file size string + """ + size = '' + fp = os.path.join(outpath,fpath) + if os.path.isfile(fp): + size = '0 B' + n = float(os.path.getsize(fp)) + if n > 2**20: + size = '%1.1f MB' % (n/2**20) + elif n > 2**10: + size = '%1.1f KB' % (n/2**10) + elif n > 0: + size = '%d B' % (int(n)) + return size + + def makeHtml(self): + """ Create an HTML file content to list all the artifacts found in the output_dir + """ + + galhtmlprefix = """<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd"> + <html xmlns="http://www.w3.org/1999/xhtml" xml:lang="en" lang="en"> + <head> <meta http-equiv="Content-Type" content="text/html; charset=utf-8" /> + <meta name="generator" content="Galaxy %s tool output - see http://g2.trac.bx.psu.edu/" /> + <title></title> + <link rel="stylesheet" href="/static/style/base.css" type="text/css" /> + </head> + <body> + <div class="toolFormBody"> + """ + galhtmlattr = """<hr/><div class="infomessage">This tool (%s) was generated by the <a href="https://bitbucket.org/fubar/galaxytoolfactory/overview">Galaxy Tool Factory</a></div><br/>""" + galhtmlpostfix = """</div></body></html>\n""" + + flist = os.listdir(self.opts.output_dir) + flist = [x for x in flist if x <> 'Rplots.pdf'] + flist.sort() + html = [] + html.append(galhtmlprefix % progname) + html.append('<div class="infomessage">Galaxy Tool "%s" run at %s</div><br/>' % (self.toolname,timenow())) + fhtml = [] + if len(flist) > 0: + logfiles = [x for x in flist if x.lower().endswith('.log')] # log file names determine sections + logfiles.sort() + logfiles = [x for x in logfiles if os.path.abspath(x) <> os.path.abspath(self.tlog)] + logfiles.append(os.path.abspath(self.tlog)) # make it the last one + pdflist = [] + npdf = len([x for x in flist if os.path.splitext(x)[-1].lower() == '.pdf']) + for rownum,fname in enumerate(flist): + dname,e = os.path.splitext(fname) + sfsize = self.getfSize(fname,self.opts.output_dir) + if e.lower() == '.pdf' : # compress and make a thumbnail + thumb = '%s.%s' % (dname,self.thumbformat) + pdff = os.path.join(self.opts.output_dir,fname) + retval = self.compressPDF(inpdf=pdff,thumbformat=self.thumbformat) + if retval == 0: + pdflist.append((fname,thumb)) + else: + pdflist.append((fname,fname)) + if (rownum+1) % 2 == 0: + fhtml.append('<tr class="odd_row"><td><a href="%s">%s</a></td><td>%s</td></tr>' % (fname,fname,sfsize)) + else: + fhtml.append('<tr><td><a href="%s">%s</a></td><td>%s</td></tr>' % (fname,fname,sfsize)) + for logfname in logfiles: # expect at least tlog - if more + if os.path.abspath(logfname) == os.path.abspath(self.tlog): # handled later + sectionname = 'All tool run' + if (len(logfiles) > 1): + sectionname = 'Other' + ourpdfs = pdflist + else: + realname = os.path.basename(logfname) + sectionname = os.path.splitext(realname)[0].split('_')[0] # break in case _ added to log + ourpdfs = [x for x in pdflist if os.path.basename(x[0]).split('_')[0] == sectionname] + pdflist = [x for x in pdflist if os.path.basename(x[0]).split('_')[0] <> sectionname] # remove + nacross = 1 + npdf = len(ourpdfs) + + if npdf > 0: + nacross = math.sqrt(npdf) ## int(round(math.log(npdf,2))) + if int(nacross)**2 != npdf: + nacross += 1 + nacross = int(nacross) + width = min(400,int(1200/nacross)) + html.append('<div class="toolFormTitle">%s images and outputs</div>' % sectionname) + html.append('(Click on a thumbnail image to download the corresponding original PDF image)<br/>') + ntogo = nacross # counter for table row padding with empty cells + html.append('<div><table class="simple" cellpadding="2" cellspacing="2">\n<tr>') + for i,paths in enumerate(ourpdfs): + fname,thumb = paths + s= """<td><a href="%s"><img src="%s" title="Click to download a PDF of %s" hspace="5" width="%d" + alt="Image called %s"/></a></td>\n""" % (fname,thumb,fname,width,fname) + if ((i+1) % nacross == 0): + s += '</tr>\n' + ntogo = 0 + if i < (npdf - 1): # more to come + s += '<tr>' + ntogo = nacross + else: + ntogo -= 1 + html.append(s) + if html[-1].strip().endswith('</tr>'): + html.append('</table></div>\n') + else: + if ntogo > 0: # pad + html.append('<td> </td>'*ntogo) + html.append('</tr></table></div>\n') + logt = open(logfname,'r').readlines() + logtext = [x for x in logt if x.strip() > ''] + html.append('<div class="toolFormTitle">%s log output</div>' % sectionname) + if len(logtext) > 1: + html.append('\n<pre>\n') + html += logtext + html.append('\n</pre>\n') + else: + html.append('%s is empty<br/>' % logfname) + if len(fhtml) > 0: + fhtml.insert(0,'<div><table class="colored" cellpadding="3" cellspacing="3"><tr><th>Output File Name (click to view)</th><th>Size</th></tr>\n') + fhtml.append('</table></div><br/>') + html.append('<div class="toolFormTitle">All output files available for downloading</div>\n') + html += fhtml # add all non-pdf files to the end of the display + else: + html.append('<div class="warningmessagelarge">### Error - %s returned no files - please confirm that parameters are sane</div>' % self.opts.interpreter) + html.append(galhtmlpostfix) + htmlf = file(self.opts.output_html,'w') + htmlf.write('\n'.join(html)) + htmlf.write('\n') + htmlf.close() + self.html = html + + + def run(self): + """ + scripts must be small enough not to fill the pipe! + """ + my_env = os.environ.copy() + if self.treatbashSpecial and self.opts.interpreter in ['bash','sh']: + retval = self.runBash(pth) + else: + if self.opts.output_dir: + ste = open(self.elog,'w') + sto = open(self.tlog,'w') + sto.write('## Toolfactory running %s as %s script\n' % (self.toolname,self.opts.interpreter)) + sto.flush() + p = subprocess.Popen(self.cl,shell=False,stdout=sto,stderr=ste,stdin=subprocess.PIPE,cwd=self.opts.output_dir,env=my_env) + else: + p = subprocess.Popen(self.cl,shell=False,stdin=subprocess.PIPE,env=my_env) + p.stdin.write(self.script) + p.stdin.close() + retval = p.wait() + if self.opts.output_dir: + sto.close() + ste.close() + # get stderr, allowing for case where it's very large + tmp_stderr = open( self.elog, 'rb' ) + stderr = '' + try: + while True: + stderr += tmp_stderr.read( buffsize ) + if not stderr or len( stderr ) % buffsize != 0: + break + except OverflowError: + pass + tmp_stderr.close() + if self.opts.make_HTML: + self.makeHtml() + return retval + + def runBash(self): + """ + cannot use - for bash so use self.sfile + """ + if self.opts.output_dir: + s = '## Toolfactory generated command line = %s\n' % ' '.join(self.cl) + ste = open(self.elog,'w') + sto = open(self.tlog,'w') + sto.write(s) + sto.flush() + p = subprocess.Popen(self.cl,shell=False,stdout=sto,stderr=ste,cwd=self.opts.output_dir) + else: + p = subprocess.Popen(self.cl,shell=False) + retval = p.wait() + if self.opts.output_dir: + sto.close() + ste.close() + # get stderr, allowing for case where it's very large + tmp_stderr = open(self.elog, 'rb' ) + stderr = '' + try: + while True: + stderr += tmp_stderr.read( buffsize ) + if not stderr or len( stderr ) % buffsize != 0: + break + except OverflowError: + pass + tmp_stderr.close() + if self.opts.make_HTML: + self.makeHtml() + return retval + + +def main(): + u = """ + This is a Galaxy wrapper. It expects to be called by a special purpose tool.xml as: + <command interpreter="python">rgBaseScriptWrapper.py --script_path "$scriptPath" --tool_name "foo" --interpreter "Rscript" + </command> + """ + op = optparse.OptionParser() + a = op.add_option + a('--script_path',default=None) + a('--tool_name',default=None) + a('--interpreter',default=None) + a('--output_dir',default=None) + a('--output_html',default=None) + a('--input_tab',default="None") + a('--output_tab',default="None") + a('--user_email',default='Unknown') + a('--bad_user',default=None) + a('--make_Tool',default=None) + a('--make_HTML',default=None) + a('--help_text',default=None) + a('--tool_desc',default=None) + a('--new_tool',default=None) + a('--tool_version',default=None) + opts, args = op.parse_args() + assert not opts.bad_user,'UNAUTHORISED: %s is NOT authorized to use this tool until Galaxy admin adds %s to admin_users in universe_wsgi.ini' % (opts.bad_user,opts.bad_user) + assert opts.tool_name,'## Tool Factory expects a tool name - eg --tool_name=DESeq' + assert opts.interpreter,'## Tool Factory wrapper expects an interpreter - eg --interpreter=Rscript' + assert os.path.isfile(opts.script_path),'## Tool Factory wrapper expects a script path - eg --script_path=foo.R' + if opts.output_dir: + try: + os.makedirs(opts.output_dir) + except: + pass + r = ScriptRunner(opts) + if opts.make_Tool: + retcode = r.makeTooltar() + else: + retcode = r.run() + os.unlink(r.sfile) + if retcode: + sys.exit(retcode) # indicate failure to job runner + + +if __name__ == "__main__": + main() + +
--- a/rglasso.xml Tue Oct 21 22:53:49 2014 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,348 +0,0 @@ -<tool id="rglasso" name="Lasso" version="0.01"> - <description>and ridge regression using glmnet</description> - <requirements> - <requirement type="package" version="3.1.1">R_3_1_1</requirement> - <requirement type="package" version="1.3.18">graphicsmagick</requirement> - <requirement type="package" version="9.10">ghostscript</requirement> - <requirement type="package" version="2.14">glmnet_lars_2_14</requirement> - </requirements> - <stdio> - <exit_code range="4" level="fatal" description="Number of subject ids must match total number of samples in the input matrix" /> - </stdio> - <command interpreter="python"> - rgToolFactory.py --script_path "$runme" --interpreter "Rscript" --tool_name "rglasso" - --output_dir "$html_file.files_path" --output_html "$html_file" --make_HTML "yes" - </command> - <inputs> - <param name="title" type="text" value="lasso test" size="80" label="Title for job outputs" help="Typing a short, meaningful text here will help remind you (and explain to others) what the outputs represent"> - <sanitizer invalid_char=""> - <valid initial="string.letters,string.digits"><add value="_" /> </valid> - </sanitizer> - </param> - <param name="input1" type="data" format="tabular" label="Select an input tabular file. Each row should contain a different measurement (phenotype) to be analysed." - multiple='False' help="Tabular data with samples as rows, phenotypes as columns with a header row of names and unique sample ID in first column" /> - <param name="yvar_cols" label="Select columns containing variables to use as dependent (y) in lasso regressions" type="data_column" data_ref="input1" numerical="True" - multiple="True" use_header_names="True" help = "Each selected variable will be run and reported separately."/> - <param name="xvar_cols" label="Select columns containing variables to use as predictor (x) vars in each y lasso" type="data_column" data_ref="input1" numerical="True" - multiple="True" use_header_names="True" /> - <param name="logtrans" type="select" label="Perform a log transformation on all predictors" > - <option value="False" selected="true">No log transformation of predictors</option> - <option value="True">Log 2 transform predictors before model</option> - </param> - <param name="do_standard" type="select" label="Standardise x vars" - help="If all measurements on same scale, may not be needed. Coefficients are always returned on the original scale."> - <option value="False" selected="true">No standardisation of predictors</option> - <option value="True">Standardise predictors before model</option> - </param> - <param name="alpha" type="float" value="0.95" size="5" min="0.01" max="1.0" label="Alpha - see glmnet docs. 1-> lasso. 0.025-> ridge regression" - help="Default 0.95 allows lasso to cope better with expected predictor collinearity. Use (eg) 0.5 for hybrid regularised regression or (eg) 0.025 for ridge regression"/> - <param name="nfold" type="integer" value="10" size="5" label="Number of folds for internal cross validation" - help="Default of 10 is usually ok"/> - <param name="fdrthresh" type="float" value="0.05" size="5" label="P value threshold for FDR filtering for amily wise error rate control" - help="Conventional default value of 0.05 recommended"/> - <param name="fdrtype" type="select" label="FDR (Type II error) control method" - help="Use fdr or bh typically to control for the number of tests in a reliable way"> - <option value="fdr" selected="true">fdr</option> - <option value="BH">Benjamini Hochberg</option> - <option value="BY">Benjamini Yukateli</option> - <option value="bonferroni">Bonferroni</option> - <option value="hochberg">Hochberg</option> - <option value="holm">Holm</option> - <option value="hommel">Hommel</option> - <option value="none">no control for multiple tests</option> - </param> - </inputs> - <outputs> - <data format="html" name="html_file" label="${title}.html"/> - <data format="tabular" name="model_file" label="${title}_modelres.xls"/> - </outputs> - <tests> -<test> -<param name='finput' value='test_bams2mx.xls' ftype='tabular' /> - <param name='treatment_name' value='case' /> - <param name='title' value='edgeRtest' /> - <param name='useNDF' value='' /> - <param name='fdrtype' value='fdr' /> - <param name='priordf' value="0" /> - <param name='fdrthresh' value="0.05" /> - <param name='control_name' value='control' /> - <param name='subjectids' value='' /> - <param name='Treat_cols' value='3,4,5,9' /> - <param name='Control_cols' value='2,6,7,8' /> - <output name='outtab' file='edgeRtest1out.xls' compare='diff' /> - <output name='html_file' file='edgeRtest1out.html' compare='diff' lines_diff='20' /> -</test> -</tests> - -<configfiles> -<configfile name="runme"> -<![CDATA[ -library('glmnet') -library('lars') - -do_lasso = function(x=NA,y=NA,do_standard=T,p.cutoff=0.05,debugOn=T,defaultFam="gaussian",logtransform=T,descr='description', - indx=1,target='target',sane=F,alpha=1.0,nfold=10) -{ - logf = file(paste(target,"rglasso.log",sep='_'), open = "wt") - sink(logf,type = c("output", "message")) - res = NULL - phe_is_bin = (length(unique(y)) == 2) - cat('i=',indx,'target=',target,'logtrans=',logtransform,'is binary=',phe_is_bin,'dim(x)=',dim(x),'length(y)=',length(y),'\n') - fam = "gaussian" - if (defaultFam %in% c("poisson","binomial","gaussian","multinomial")) fam=defaultFam - if (phe_is_bin == T) { - fam = "binomial" - } - do_standard = do_standard - standardize = do_standard - normalize = do_standard - if (fam == "binomial") - { - larsres = try(glmnet(x,y,family=fam,standardize=standardize,maxit=10000,alpha=alpha,type.logistic = "modified.Newton" ),T) - } else { - larsres = try(glmnet(x,y,family=fam,standardize=standardize,maxit=10000,alpha=alpha,type.gaussian="covariance"),T) - } - if (class(larsres) == "try-error") - { - print.noquote(paste('Unable to run glmnet on your data for',target)) - return(NA) - } - mt = paste(descr,'glmnet on',target) - outpdf = paste(target,descr,'glmnetPath.pdf',sep='_') - pdf(outpdf) - try( - { - plot(larsres,main=mtitle,label=T) - grid() - dev.off() - },T) - outpdf = paste(target,descr,'glmnetDeviance.pdf',sep='_') - pdf(outpdf) - mt2 = paste(descr,'Deviance for',target) - try( { - plot(larsres,xvar="dev",main=mt2,label=T) - grid() - dev.off() - },T) - larscv = NA - if (fam=="binomial") { - tmain = paste(target,'AUC') - outpdf = paste(target,descr,'glmnetCV_AUC.pdf',sep='_') - larscv = try(cv.glmnet(x=x,y=y,family=fam,type.measure='auc'),T) - } else { - tmain = paste(target,'CV MSE') - outpdf = paste(target,descr,'glmnetCV_MSE.pdf',sep='_') - larscv = try(cv.glmnet(x,y,family=fam,type.measure='mse'),T) - } - if (class(larscv) == "try-error") { return(NA) } - pdf(outpdf) - try( - { - plot(larscv,main=tmain) - grid() - dev.off() - },T) - best_lambda = larscv\$lambda.min - bestpred = as.matrix(coef(larscv, s = "lambda.min")) - inmodel = which(bestpred != 0) - coefs = bestpred[inmodel,1] - preds = rownames(bestpred)[inmodel] - names(coefs) = preds - if (debugOn) {cat(indx,'best_lambda=',best_lambda,'saving',fam,'respreds=',names(coefs),'as predictors of',target,'coefs=',coefs,'\n')} - res = try(data.frame(i=indx,pred=target,regulator=names(coefs),coef=coefs,glmnet_model=fam),T) - if (class(res) == "try-error") { return(NA) } - print.noquote(res) - return(res) - sink() -} - -dolasso_generic = function(predvars=NA,depvars=NA,debugOn=T,p.cutoff = 0.05,maxsteps=100, alpha=alpha,nfold=10,xcolnames=c(),ycolnames=c(), - descr="describe me",logtransform=F,do_standard=F,defaultFam="gaussian") -{ - xdat = predvars - if (logtransform) { - small = 1e-10 - predvars = try(log(predvars+small),T) - descr = paste('logxformed',descr,sep='_') - } - if (class(predvars) == "try-error") - { - print.noquote('Unable to log transform your predictors') - return(NA) - } - xm = as.matrix(xdat) - res = NULL - depnames = ycolnames - ndep = length(depnames) - for (i in c(1:max(1,ndep))) { - target = depnames[i] - if (length(target) < 1) { target='y' } - if (i %% 100 == 0) { cat(i,target,'\n') } - if (ndep <= 1) { - y=depvars - } else { - y = depvars[,i] - } - x = xm - ok = complete.cases(x,y) - if (sum(! ok) > 0) { - y = y[(ok)] - x = xm[(ok),] - } - if (sum(ok) == 0 ) { - print(paste("No complete cases found for",target,"in input x dim =",dim(xm),"length y=",length(y))) - } else { - regres = do_lasso(x=x,y=y,do_standard=do_standard,p.cutoff=p.cutoff,debugOn=debugOn,defaultFam=defaultFam, - logtransform=logtransform,descr=descr,indx=i,target=target,alpha=alpha,nfold=nfold) - if (! is.na(regres)) { res = rbind(res,regres) } - } - - } - if (! is.na(res)) { - rownames(res) = c(1:length(res\$i)) - } - return(res) -} - - - - -corPlot=function(xdat=c(),main='main title',is_raw=T) -{ - library(pheatmap) - library(gplots) - if (is_raw) { - cxdat = cor(xdat,method="spearman",use="pairwise.complete.obs") - } else { - cxdat=xdat - } - xro = nrow(cxdat) - if (xro > 1000) stop("Too many rows for heatmap, who can read?!") - fontsize_col = 5.0 - pheatmap(cxdat, main=main, show_colnames = F, width=30, height=30, - fontsize_row=fontsize_col, border_color=NA) -} - - - - -mdsPlot = function(dm,myTitle,groups) -{ - - samples = colnames(dm) - mt = paste(unlist(strsplit(myTitle,' ')),collapse=" ") - outpdfname=paste(mt,"MDS.pdf",sep='_') - gu = unique(groups) - colours = rainbow(length(gu),start=0.1,end=0.9) - pcols = colours[match(groups,gu)] - - mydata = t(dm) - d = dist(mydata) - fit = cmdscale(d,eig=TRUE, k=2) - - x = fit\$points[,1] - y = fit\$points[,2] - pdf(outpdfname) - plot(x, y, xlab="Dimension 1", ylab="Dimension 2", - main=paste(mt,"MDS Plot"),type="n", col=pcols) - text(x, y, labels = row.names(mydata), cex=0.3, col=pcols) - grid(col="lightgray",lty="dotted") - dev.off() -} - -runTest = function(n=10) -{ - set.seed (NULL) - Y = data.frame(y1=runif (n),y2=runif(n)) - Xv <- runif(n*n) - X <- matrix(Xv, nrow = n, ncol = n) - - mydf <- data.frame(Y, X) - - regres_out = dolasso_generic(predvars=X,depvars=Y,debugOn=T,p.cutoff = 0.05,maxsteps=10000,nfold=10, - descr='randomdata',logtransform=F,do_standard=do_standard,defaultFam="gaussian",alpha=0.05) - return(regres_out) -} -]]> -alpha = $alpha -nfold = $nfold -Out_Dir = "$html_file.files_path" -Input = "$input1" -yvar_cols = "$yvar_cols" -xvar_cols= "$xvar_cols" -fdrtype = "$fdrtype" -fdrthresh = $fdrthresh -myTitle = "$title" -outtab = "$model_file" -logtrans = as.logical("$logtrans") -do_standard = as.logical("$do_standard") -indat = read.table(Input,head=T,sep='\t') -datcols = colnames(indat) -if (length(yvar_cols) == 1) {yvar_cols = c(yvar_cols)} -if (length(xvar_cols) == 1) {xvar_cols = c(xvar_cols)} -yvar_cols = as.numeric(strsplit(yvar_cols,",")[[1]]) -xvar_cols = as.numeric(strsplit(xvar_cols,",")[[1]]) -y = indat[,yvar_cols] -x = indat[,xvar_cols] -xcolnames = colnames(indat)[xvar_cols] -ycolnames = colnames(indat)[yvar_cols] -cat('Got yvar=',paste(ycolnames,collapse=','),'cols',yvar_cols,'n preds=',length(xcolnames),'\n') -if (file.exists(Out_Dir) == F) dir.create(Out_Dir) -regres_out = dolasso_generic(predvars=x,depvars=y,debugOn=T,p.cutoff = fdrthresh,maxsteps=10000,nfold=nfold,xcolnames=xcolnames,ycolnames=ycolnames, - descr=myTitle,logtransform=logtrans,do_standard=do_standard,defaultFam="gaussian",alpha=alpha) -print.noquote(regres_out) -write.table(regres_out,outtab,quote=FALSE, sep="\t",row.names=F) -sessionInfo() - -</configfile> -</configfiles> -<help> - -**What it does** - -Runs Bioconductor glmnet code on your data -glmnet allows you to use extremely efficient code providing blazing fast coordinate descent solution to lasso (or by altering alpha from 1.0, hybrid ridge/elastic-net) -regularised regression models. These are highly optimised forward stepwise regressions which (in very broad terms) use penalties on -the number of coefficients in a clever way and usually find a pretty good, sane, sparse set of predictors for the dependent variable. - -These provide all optimal solutions starting with all zero regression parameters, allowing one to become non-zero at a time, stepwise. - -Internal cross validation is used to optimise the choice of lambda based on CV AUC for logistic (binomial outcome) models, or CV mse for gaussian. - -Sufficiently big jobs will take a while (eg each lasso regression with 20k features on 1k samples takes about 2 minutes on our aged cluster) - -**Input** - -Assuming you have more measurements than samples, you supply data as a tabular text file where each row is a sample and columns -are variables. You specify which columns are dependent (predictors) and which are observations for each sample. Each of multiple -dependent variable columns will be run and reported independently. -**Output** - -For each selected dependent regression variable, a brief report of the model coefficients predicted at the -'optimal' nfold CV value of lambda. - -**Attributions** - -glmnet_ is a tutorial introduction worth reading - based on the vignette but easier to follow. - - - -See above for Bioconductor package documentation for packages exposed in Galaxy by this tool and app store package. - -Galaxy_ (that's what you are using right now!) for gluing everything together - -Otherwise, all code and documentation comprising this tool was written by Ross Lazarus and is -licensed to you under the LGPL_ like other rgenetics artefacts - -.. _LGPL: http://www.gnu.org/copyleft/lesser.html -.. _glmnet: http://web.stanford.edu/~hastie/glmnet/glmnet_alpha.html -.. _Galaxy: http://getgalaxy.org - -http://web.stanford.edu/~hastie/glmnet/glmnet_alpha.html - -</help> - -</tool> - - -
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/rglasso_cox.xml Tue Oct 21 22:53:57 2014 -0400 @@ -0,0 +1,547 @@ +<tool id="rglasso_cox" name="Lasso" version="0.03"> + <description>and cox regression using elastic net</description> + <requirements> + <requirement type="package" version="3.1.1">R_3_1_1</requirement> + <requirement type="package" version="1.3.18">graphicsmagick</requirement> + <requirement type="package" version="9.10">ghostscript</requirement> + <requirement type="package" version="2.14">glmnet_lars_2_14</requirement> + </requirements> + <command interpreter="python"> + rgToolFactory.py --script_path "$runme" --interpreter "Rscript" --tool_name "rglasso" + --output_dir "$html_file.files_path" --output_html "$html_file" --make_HTML "yes" + </command> + <inputs> + <param name="title" type="text" value="lasso test" size="80" label="Title for job outputs" help="Typing a short, meaningful text here will help remind you (and explain to others) what the outputs represent"> + <sanitizer invalid_char=""> + <valid initial="string.letters,string.digits"><add value="_" /> </valid> + </sanitizer> + </param> + <param name="input1" type="data" format="tabular" label="Select an input tabular text file from your history. Rows represent samples; Columns are measured phenotypes" + multiple='False' help="Tabular text data with samples as rows, phenotypes as columns with a header row of column identifiers" /> + <param name="xvar_cols" label="Select columns containing numeric variables to use as predictor (x) variables" type="data_column" data_ref="input1" numerical="True" + multiple="True" use_header_names="True" /> + <param name="force_xvar_cols" label="Select numeric columns containing variables ALWAYS included as predictors in cross validation" type="data_column" data_ref="input1" numerical="True" + multiple="True" use_header_names="True" optional="True"/> + <conditional name="model"> + <param name="fam" type="select" label="GLM Link function for models" + help="Binary dependant variables will automatically be set to Binomial no matter what this is set to"> + "gaussian","binomial","poisson","multinomial","cox","mgaussian" + <option value="gaussian" selected="true">Gaussian - continuous dependent (y)</option> + <option value="binomial">Binomial dependent variables</option> + <option value="poisson">Poisson (eg counts)</option> + <option value="cox">Cox models - require special setup for y variables - see below</option> + </param> + <when value="gaussian"> + <param name="yvar_cols" label="Select numeric columns containing variables to use as the dependent (y) in elasticnet" type="data_column" data_ref="input1" numerical="True" + multiple="True" use_header_names="True" help = "If multiple, each will be modelled against all the x variables and reported separately."/> + </when> + <when value="binomial"> + <param name="yvar_cols" label="Select numeric columns containing variables to use as the dependent (y) in elasticnet" type="data_column" data_ref="input1" numerical="True" + multiple="True" use_header_names="True" help = "If multiple, each will be modelled against all the x variables and reported separately."/> + </when> + <when value="poisson"> + <param name="yvar_cols" label="Select columns containing variables to use as the dependent (y) in elasticnet" type="data_column" data_ref="input1" numerical="True" + multiple="True" use_header_names="True" help = "If multiple, each will be modelled against all the x variables and reported separately."/> + </when> + <when value="cox"> + <param name="cox_time" label="Select column containing time under observation for Cox regression" + type="data_column" data_ref="input1" numerical="True" optional="False" + multiple="False" use_header_names="True" help = "This MUST contain a time period - eg continuous years or days to failure or right censoring"/> + <param name="cox_status" label="Select column containing status = 1 for outcome of interest at the end of the time under observation or 0 for right censoring" + type="data_column" data_ref="input1" numerical="True" optional="False" + multiple="False" use_header_names="True" help = "This MUST contain 1 for subjects who had an event at that time or 0 for a right censored observation"/> + <param name="output_full" type="select" label="Create a tabular output with coefficients for all predictors" > + <option value="F" selected="true">No full model output file</option> + <option value="T">Create a full model output file</option> + </param> + </when> + </conditional> + <param name="logtrans" type="select" label="Perform a log transformation on all predictors" help="A tiny number (1e-10) will be added to prevent taking logs of zero"> + <option value="False" selected="true">No log transformation of predictors</option> + <option value="True">Log 2 transform predictors before modelling - will fail if negative values!</option> + </param> + <param name="do_standard" type="select" label="Standardise x vars" + help="If all measurements on same scale, may not be needed. Coefficients are always returned on the original scale."> + <option value="False" selected="true">No standardisation of predictors</option>l + + <option value="True">Standardise predictors before model</option> + </param> + <param name="alpha" type="float" value="0.95" size="5" min="0.01" max="1.0" label="Alpha - see glmnet docs. 1 for pure lasso. 0.0 for pure ridge regression" + help="Default 0.95 allows lasso to cope better with expected predictor collinearity. Use (eg) 0.5 for hybrid regularised regression or (eg) 0.025 for ridge regression"/> + <param name="nfold" type="integer" value="10" size="5" label="Number of folds for internal cross validation" + help="Default of 10 is usually ok"/> + </inputs> + <outputs> + <data format="html" name="html_file" label="${title}.html"/> + <data format="tabular" name="model_file" label="${title}_modelres.xls"/> + <data format="tabular" name="full_cox" label="${title}_full_cox_model.xls"> + <filter>model['output_full'] == 'T'</filter> + </data> + </outputs> + <tests> + <test> + <param name='input1' value='cox_test.xls' ftype='tabular' /> + <param name='treatment_name' value='case' /> + <param name='title' value='Cox glmnet test' /> + <param name='nfold' value='10' /> + <param name='logtrans' value='False' /> + <param name='alpha' value='0.95' /> + <param name='do_standard' value="True" /> + <param name='cox_time' value='1' /> + <param name='cox_status' value='2' /> + <param name='fam' value='cox' /> + <param name='model.yvar_cols' value='' /> + <param name='xvar_cols' value='3,4,5' /> + <param name='force_xvar_cols' value='3' /> + <param name='output_full' value='0' /> + <output name='model_file'> + <assert_contents> + <has_text text="rhubarb" /> + <has_text text="TRUE" /> + <!-- 	 is XML escape code for tab --> + <has_line line="regulator	partial_likelihood	forced_in	glmnet_model	best_lambda" /> + <has_n_columns n="5" /> + </assert_contents> + </output> + <output name='html_file' file='coxlassotest.html' compare='diff' lines_diff='10' /> + </test> +</tests> +<help> + +**Before you start** + +Please read the glmnet documentation @ glmnet_ + +This Galaxy wrapper merely exposes that code and the glmnet_ documentation is essential reading +before getting useful results here. + +**What it does** + +From documentation at glmnet_ :: + + Glmnet is a package that fits a generalized linear model via penalized maximum likelihood. + The regularization path is computed for the lasso or elasticnet penalty at a grid of values for the regularization parameter lambda. + The algorithm is extremely fast, and can exploit sparsity in the input matrix x. + It fits linear, logistic and multinomial, poisson, and Cox regression models. + A variety of predictions can be made from the fitted models. + +All solutions starting with all zero regression parameters, allowing one to become non-zero at a time +in a forward stepwise manner, are estimated by coordinate descent, each point corresponding to a value for lambda. + +Internal cross validation is used to optimise the choice of lambda based on CV AUC for logistic (binomial outcome) models, or CV mse for gaussian. + +Sufficiently big jobs will take a while (eg each lasso regression with 20k features on 1k samples takes about 2-3 minutes on our aged cluster) + +**Input** + +Assuming you have more measurements than samples, you supply data as a tabular text file where each row is a sample and columns +are variables. You specify which columns are dependent (predictors) and which are observations for each sample. Each of multiple +dependent variable columns will be run and reported independently. Prectors can be forced in to the model. + +**Output** + +For each selected dependent regression variable, a brief report of the model coefficients predicted at the +'optimal' nfold CV value of lambda. + +**Attributions** + +glmnet_ is the R package exposed by this Galaxy tool. + +Galaxy_ (that's what you are using right now!) for gluing everything together + +Otherwise, all code and documentation comprising this tool was written by Ross Lazarus and is +licensed to you under the LGPL_ like other rgenetics artefacts + +.. _LGPL: http://www.gnu.org/copyleft/lesser.html +.. _glmnet: http://web.stanford.edu/~hastie/glmnet/glmnet_alpha.html +.. _Galaxy: http://getgalaxy.org + + +</help> + +<configfiles> +<configfile name="runme"> +<![CDATA[ +library('glmnet') +library('lars') + + +mdsPlot = function(dm,myTitle,groups,outpdfname,transpose=T) +{ + + samples = colnames(dm) + mt = paste(unlist(strsplit(myTitle,' ')),collapse=" ") + pcols=c('maroon') + if (! is.na(groups)) + { + gu = unique(groups) + colours = rainbow(length(gu),start=0.1,end=0.9) + pcols = colours[match(groups,gu)] + } + mydata = dm + if (transpose==T) + { + mydata = t(dm) + } + d = dist(mydata) + fit = cmdscale(d,eig=TRUE, k=10) + + x = fit\$points[,1] + y = fit\$points[,2] + pdf(outpdfname) + plot(x, y, xlab="Dimension 1", ylab="Dimension 2", + main=paste(mt,"MDS Plot"),type="n", col=pcols, cex=0.3) + text(x, y, labels = row.names(mydata), cex=0.3, col=pcols) + grid(col="lightgray",lty="dotted") + dev.off() +} + + +dolasso_cox = function(x,y,debugOn=F,maxsteps=10000,nfold=10,xcolnames,ycolnames,out_full=F,out_full_file=NA, + descr='Cox test',logtransform=F,do_standard=F,alpha=0.9,penalty=c()) +{ + logf = file("cox_rglasso.log", open = "wt") + sink(logf,type = c("output", "message")) + res = NULL + + ok = complete.cases(x,y) + if (sum(! ok) > 0) { + y = y[(ok),] + x = x[(ok),] + print(paste("Removed",sum(! ok),'cases with any missing variables')) + } + if (sum(ok) == 0 ) { + print(paste("No complete cases found for cox models in input x dim =",paste(dim(xm),collapse=','),"length y=",length(y))) + sink + return(NA) + } + do_standard = do_standard + standardize = do_standard + normalize = do_standard + larsres = try(glmnet(x,y,family='cox',standardize=standardize,alpha=alpha ),T) + if (class(larsres) == "try-error") + { + print.noquote('Unable to run cox glmnet on your data') + print(larsres) + sink() + return(NA) + } + if (out_full == T) + { + b = as.matrix(larsres\$beta) + nb = length(colnames(b)) + bcoef = b[,nb] + lastl = larsres\$lambda[length(larsres\$lambda)] + allres = data.frame(x=rownames(b),beta=bcoef,lambda=lastl) + write.table(allres,out_full_file,quote=FALSE, sep="\t",row.names=F) + } + + outpdf = paste('cox',descr,'glmnetdev.pdf',sep='_') + try( + { + pdf(outpdf) + plot(larsres,main='cox glmnet',label=T) + grid() + dev.off() + },T) + + larscv = NA + + larscv = try(cv.glmnet(x,y,family=fam,type.measure='deviance',penalty=penalty),T) + if (class(larscv) == "try-error") { return(NA) } + outpdf = paste('cox',descr,'glmnet_cvdeviance.pdf',sep='_') + + try( + { + pdf(outpdf) + plot(larscv,main='Deviance',label=T) + grid() + dev.off() + },T) + best_lambda = larscv\$lambda.min + bestcoef = coef(larscv, s = "lambda.min") + inmodel = which(bestcoef != 0) + coefs = bestcoef[inmodel,1] + preds = rownames(bestcoef)[inmodel] + names(coefs) = preds + pen = as.logical( ! penalty[inmodel]) + if (debugOn) { + print.noquote(paste('best_lambda=',best_lambda,'saving cox respreds=',paste(names(coefs),collapse=','),'as predictors of survival. Coefs=',paste(coefs,collapse=','))) + } + res = try(data.frame(regulator=names(coefs),partial_likelihood=coefs,forced_in=pen,glmnet_model='cox',best_lambda=best_lambda),T) + if (class(res) == "try-error") { return(NA) } + if (debugOn) { + print.noquote('CV results:') + print.noquote(res) + } + sink() + return(res) + +} + +do_lasso = function(x=NA,y=NA,do_standard=T,debugOn=T,defaultFam="gaussian",logtransform=T,descr='description', + indx=1,target='target',sane=F,alpha=1.0,nfold=10,penalty=c()) +{ + logf = file(paste(target,"rglasso.log",sep='_'), open = "wt") + sink(logf,type = c("output", "message")) + res = NA + phe_is_bin = (length(unique(y)) == 2) + forcedin = paste(colnames(x)[which(penalty == 0)],collapse=',') + print.noquote(paste('i=',indx,'target=',target,'logtrans=',logtransform,'is binary=',phe_is_bin,'dim(x)=',paste(dim(x),collapse=','),'length(y)=',length(y),'force=',forcedin)) + fam = "gaussian" + if (defaultFam %in% c("poisson","binomial","gaussian","multinomial")) fam=defaultFam + if (phe_is_bin == T) { + fam = "binomial" + } + standardize = do_standard + if (fam == "binomial") + { + larsres = try(glmnet(x,y,family=fam,standardize=standardize,maxit=10000,alpha=alpha,type.logistic = "modified.Newton" ),T) + } else { + larsres = try(glmnet(x,y,family=fam,standardize=standardize,maxit=10000,alpha=alpha,type.gaussian="covariance"),T) + } + if (class(larsres) == "try-error") + { + print.noquote(paste('Unable to run glmnet on your data for',target)) + sink() + return(NA) + } + mt = paste(descr,'glmnet on',target) + outpdf = paste(target,descr,'glmnetPath.pdf',sep='_') + pdf(outpdf) + try( + { + plot(larsres,main=mt,label=T) + grid() + dev.off() + },T) + outpdf = paste(target,descr,'glmnetDeviance.pdf',sep='_') + pdf(outpdf) + mt2 = paste(descr,'Deviance for',target) + try( { + plot(larsres,xvar="dev",main=mt2,label=T) + grid() + dev.off() + },T) + larscv = NA + if (fam=="binomial") { + tmain = paste(target,'AUC') + outpdf = paste(target,descr,'glmnetCV_AUC.pdf',sep='_') + larscv = try(cv.glmnet(x=x,y=y,family=fam,type.measure='auc',penalty=penalty),T) + } else { + tmain = paste(target,'CV MSE') + outpdf = paste(target,descr,'glmnetCV_MSE.pdf',sep='_') + larscv = try(cv.glmnet(x,y,family=fam,type.measure='mse',penalty=penalty),T) + } + if (class(larscv) == "try-error") + { + print(paste('ERROR: unable to run cross validation for target',target,'error=',larscv)) + sink() + return(NA) + } + pdf(outpdf) + try( + { + plot(larscv,main=tmain) + grid() + dev.off() + },T) + ipenalty = c(0,penalty) ### must include intercept which is always forcedq + best_lambda = larscv\$lambda.min + bestpred = as.matrix(coef(larscv, s = "lambda.min")) + inmodel = which(bestpred != 0) + coefs = bestpred[inmodel,1] + preds = rownames(bestpred)[inmodel] + iforced = ipenalty[inmodel] + forced = ! as.logical(iforced) + names(coefs) = preds + if (debugOn) {cat(indx,'best_lambda=',best_lambda,'saving',fam,'respreds=',names(coefs),'as predictors of',target,'coefs=',coefs,'\n')} + res = try(data.frame(i=indx,pred=target,regulator=names(coefs),coef=coefs,forced_in=forced,glmnet_model=fam),T) + if (class(res) == "try-error") { + sink() + return(NA) } + print.noquote(res) + sink() + return(res) +} + +dolasso_generic = function(predvars=NA,depvars=NA,debugOn=T,maxsteps=100, alpha=alpha,nfold=10,xcolnames=c(),ycolnames=c(), + descr="describe me",logtransform=F,do_standard=F,defaultFam="gaussian",penalty=penalty) +{ + logf = file("rglasso.log", open = "a") + sink(logf,type = c("output", "message")) + xdat = predvars + if (logtransform) { + small = 1e-10 + try( { lpred = log(predvars) },T) + if (class(lpred) == "try-error") + try( { lpred = log(predvars+small) }, T) + if (class(lpred) == "try-error") + { + print.noquote('Unable to logtransform your data') + return(NA) + } + descr = paste('logx',descr,sep='_') + } + + xm = as.matrix(xdat) + res = NA + depnames = ycolnames + ndep = length(depnames) + dm = xm + outpdfname = 'rglasso_x_in_sample_space_MDS.pdf' + mdsPlot(dm,'measurements in sample space',groups=NA,outpdfname=outpdfname,transpose=T) + outpdfname = 'rglasso_samples_in_x_space_MDS.pdf' + mdsPlot(dm,'samples in measurement space',groups=NA,outpdfname=outpdfname,transpose=F) + for (i in c(1:max(1,ndep))) { + target = depnames[i] + if (length(target) < 1) { target='y' } + if (i %% 100 == 0) { cat(i,target,'\n') } + if (ndep <= 1) { + y=depvars + } else { + y = depvars[,i] + } + x = xm + ok = complete.cases(x,y) + if (sum(! ok) > 0) { + y = y[(ok)] + x = xm[(ok),] + } + if (sum(ok) == 0 ) { + print(paste("No complete cases found for",target,"in input x dim =",paste(dim(xm),collapse=','),"length y=",length(y))) + } else { + regres = do_lasso(x=x,y=y,do_standard=do_standard,debugOn=debugOn,defaultFam=defaultFam, + logtransform=logtransform,descr=descr,indx=i,target=target,alpha=alpha,nfold=nfold,penalty=penalty) + if (! is.na(regres)) { res = rbind(res,regres) } + } + + } + if (! is.na(res)) { + rownames(res) = c(1:length(res\$i)) + } + sink() + return(res) +} + + + + +corPlot=function(xdat=c(),main='main title',is_raw=T) +{ + library(pheatmap) + library(gplots) + if (is_raw) { + cxdat = cor(xdat,method="spearman",use="pairwise.complete.obs") + } else { + cxdat=xdat + } + xro = nrow(cxdat) + if (xro > 1000) stop("Too many rows for heatmap, who can read?!") + fontsize_col = 5.0 + pheatmap(cxdat, main=main, show_colnames = F, width=30, height=30, + fontsize_row=fontsize_col, border_color=NA) +} + + + + +runTest = function(n=10) +{ + set.seed (NULL) + Y = data.frame(y1=runif (n),y2=runif(n)) + Xv <- runif(n*n) + X <- matrix(Xv, nrow = n, ncol = n) + + mydf <- data.frame(Y, X) + + regres_out = dolasso_generic(predvars=X,depvars=Y,debugOn=T,p.cutoff = 0.05,maxsteps=10000,nfold=10, + descr='randomdata',logtransform=F,do_standard=do_standard,defaultFam="gaussian",alpha=0.05) + return(regres_out) +} +]]> +options(width=120) +alpha = $alpha +nfold = $nfold +Out_Dir = "$html_file.files_path" +Input = "$input1" +myTitle = "$title" +outtab = "$model_file" +logtrans = as.logical("$logtrans") +do_standard = as.logical("$do_standard") +fam = "$model.fam" +xvar_cols_in = "$xvar_cols" +force_xvar_cols_in = "$force_xvar_cols" +xvar_cols = as.numeric(strsplit(xvar_cols_in,",")[[1]]) +force_xvar_cols = c() +if (force_xvar_cols_in > "") +{ + force_xvar_cols = as.numeric(strsplit(force_xvar_cols_in,",")[[1]]) + allx = c(xvar_cols,force_xvar_cols) + xvar_cols = unique(allx) + xvar_cols = xvar_cols[order(xvar_cols)] +} + +indat = read.table(Input,head=T,sep='\t') +datcols = colnames(indat) + +x = indat[,xvar_cols] +xcolnames = datcols[xvar_cols] +penalties = rep(1,length(datcols)) +penalty = penalties[xvar_cols] +if (force_xvar_cols_in > "") +{ + penalties[force_xvar_cols] = 0 + penalty = penalties[xvar_cols] + forcedin = paste(datcols[which(penalties == 0)],collapse=',') +} + +if (file.exists(Out_Dir) == F) dir.create(Out_Dir) +out_full = F +out_full_file = NA +#if $model.fam == "cox": + cox_time = $model.cox_time + cox_status = $model.cox_status + out_full = as.logical("$model.output_full") + if (out_full == T) { out_full_file="$full_cox" } + yvar_cols = c(cox_time,cox_status) + ycolnames = c('time','status') + istat = as.double(indat[,cox_status]) + itime = as.double(indat[,cox_time]) + y = data.frame(time = itime, status = istat) + ustat = unique(istat) + if ((length(ustat) != 2) | (! 1 %in% ustat ) | (! 0 %in% ustat)) + { + print.noquote(paste('INPUT ERROR: status must have 0 (censored) or 1 (event) but found',paste(ustat,collapse=',') )) + quit(save='no',status=1) + } + y = as.matrix(y) + x = as.matrix(x) + forced = paste(force_xvar_cols,collapse=',') + print.noquote(paste('Cox model: Got yvar=',paste(ycolnames,collapse=','),'cols=',paste(yvar_cols,collapse=','),'n preds=',length(xcolnames), + 'forced in=',forced)) + regres_out = dolasso_cox(x=x,y=y,debugOn=F,maxsteps=10000,nfold=nfold,xcolnames=xcolnames,ycolnames=ycolnames,out_full=out_full,out_full_file=out_full_file, + descr=myTitle,logtransform=logtrans,do_standard=do_standard,alpha=alpha,penalty=penalty) +#else: + yvar_cols = "$model.yvar_cols" + yvar_cols = as.numeric(strsplit(yvar_cols,",")[[1]]) + y = indat[,yvar_cols] + ycolnames = colnames(indat)[yvar_cols] + print.noquote(paste('Got yvar=',paste(ycolnames,collapse=','),'cols',paste(yvar_cols,collapse=','),'n preds=',length(xcolnames),'forced in=',paste(force_xvar_cols,collapse=','))) + regres_out = dolasso_generic(predvars=x,depvars=y,debugOn=F, maxsteps=10000,nfold=nfold,xcolnames=xcolnames,ycolnames=ycolnames, + descr=myTitle,logtransform=logtrans,do_standard=do_standard,defaultFam=fam,alpha=alpha,penalty=penalty) +#end if +print.noquote('Results preview:') +print.noquote(regres_out) +write.table(regres_out,outtab,quote=FALSE, sep="\t",row.names=F) +print.noquote('SessionInfo for this R session:') +sessionInfo() +warnings() + +</configfile> +</configfiles> + +</tool> + + +
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/coxlassotest.html Tue Oct 21 22:53:57 2014 -0400 @@ -0,0 +1,140 @@ +<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd"> + <html xmlns="http://www.w3.org/1999/xhtml" xml:lang="en" lang="en"> + <head> <meta http-equiv="Content-Type" content="text/html; charset=utf-8" /> + <meta name="generator" content="Galaxy rgToolFactory.py tool output - see http://g2.trac.bx.psu.edu/" /> + <title></title> + <link rel="stylesheet" href="/static/style/base.css" type="text/css" /> + </head> + <body> + <div class="toolFormBody"> + +<div class="infomessage">Galaxy Tool "rglasso" run at 19/10/2014 13:11:14</div><br/> +<div class="toolFormTitle">cox images and outputs</div> +(Click on a thumbnail image to download the corresponding original PDF image)<br/> +<div><table class="simple" cellpadding="2" cellspacing="2"> +<tr> +<td><a href="cox_Coxglmnettest_glmnet_cvdeviance.pdf"><img src="cox_Coxglmnettest_glmnet_cvdeviance.png" title="Click to download a PDF of cox_Coxglmnettest_glmnet_cvdeviance.pdf" hspace="5" width="400" + alt="Image called cox_Coxglmnettest_glmnet_cvdeviance.pdf"/></a></td> + +<td><a href="cox_Coxglmnettest_glmnetdev.pdf"><img src="cox_Coxglmnettest_glmnetdev.png" title="Click to download a PDF of cox_Coxglmnettest_glmnetdev.pdf" hspace="5" width="400" + alt="Image called cox_Coxglmnettest_glmnetdev.pdf"/></a></td> +</tr> + +</table></div> + +<div class="toolFormTitle">cox log output</div> + +<pre> + +[1] "All partial likelihood coefficients from Cox model at minimum lambda of 0.00138091491730048:" + +3 x 1 sparse Matrix of class "dgCMatrix" + + 1 + +rhubarb 1.124517e-03 + +vegemite 1.444990e-01 + +apple 3.344565e-06 + + +</pre> + +<div class="toolFormTitle">rglasso log output</div> + +<pre> + +Loading required package: Matrix + +Loading required package: methods + +Loaded glmnet 1.9-8 + +Loaded lars 1.2 + +Warning messages: + +1: In if (class(larsres) == "try-error") { : + + the condition has length > 1 and only the first element will be used + +2: In plot.window(...) : "label" is not a graphical parameter + +3: In plot.xy(xy, type, ...) : "label" is not a graphical parameter + +4: In axis(side = side, at = at, labels = labels, ...) : + + "label" is not a graphical parameter + +5: In axis(side = side, at = at, labels = labels, ...) : + + "label" is not a graphical parameter + +6: In box(...) : "label" is not a graphical parameter + +7: In title(...) : "label" is not a graphical parameter + + +</pre> + +<div class="toolFormTitle">Other log output</div> + +<pre> + +## Toolfactory generated command line = Rscript - None None + +[1] Cox model: Got yvar= time,status cols 1 n preds= 3 forced in= 3 + +[2] Cox model: Got yvar= time,status cols 2 n preds= 3 forced in= 3 + +[1] Results preview: + + regulator partial_likelihood forced_in glmnet_model best_lambda + +rhubarb rhubarb 0.001180324 TRUE cox 0.01508333 + +vegemite vegemite 0.093827440 FALSE cox 0.01508333 + +[1] SessionInfo for this R session: + +R version 3.1.0 (2014-04-10) + +Platform: x86_64-unknown-linux-gnu (64-bit) + +locale: + + [1] LC_CTYPE=en_AU.UTF-8 LC_NUMERIC=C LC_TIME=en_AU.UTF-8 LC_COLLATE=en_AU.UTF-8 + + [5] LC_MONETARY=en_AU.UTF-8 LC_MESSAGES=en_AU.UTF-8 LC_PAPER=en_AU.UTF-8 LC_NAME=C + + [9] LC_ADDRESS=C LC_TELEPHONE=C LC_MEASUREMENT=en_AU.UTF-8 LC_IDENTIFICATION=C + +attached base packages: + +[1] methods stats graphics grDevices utils datasets base + +other attached packages: + +[1] lars_1.2 glmnet_1.9-8 Matrix_1.1-4 + +loaded via a namespace (and not attached): + +[1] grid_3.1.0 lattice_0.20-29 + + +</pre> + +<div class="toolFormTitle">All output files available for downloading</div> + +<div><table class="colored" cellpadding="3" cellspacing="3"><tr><th>Output File Name (click to view)</th><th>Size</th></tr> + +<tr><td><a href="cox_Coxglmnettest_glmnet_cvdeviance.pdf">cox_Coxglmnettest_glmnet_cvdeviance.pdf</a></td><td>6.4 KB</td></tr> +<tr class="odd_row"><td><a href="cox_Coxglmnettest_glmnetdev.pdf">cox_Coxglmnettest_glmnetdev.pdf</a></td><td>5.2 KB</td></tr> +<tr><td><a href="cox_rglasso.log">cox_rglasso.log</a></td><td>228 B</td></tr> +<tr class="odd_row"><td><a href="rglasso.Rscript">rglasso.Rscript</a></td><td>9.8 KB</td></tr> +<tr><td><a href="rglasso_error.log">rglasso_error.log</a></td><td>671 B</td></tr> +<tr class="odd_row"><td><a href="rglasso_runner.log">rglasso_runner.log</a></td><td>1.1 KB</td></tr> +</table></div><br/> +</div></body></html> +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/coxlassotest_modelres.xls Tue Oct 21 22:53:57 2014 -0400 @@ -0,0 +1,3 @@ +regulator partial_likelihood forced_in glmnet_model best_lambda +rhubarb 0.0011803238918088 TRUE cox 0.0150833254995026 +vegemite 0.0938274399971897 FALSE cox 0.0150833254995026
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tool_dependencies.xml Tue Oct 21 22:53:57 2014 -0400 @@ -0,0 +1,30 @@ +<?xml version="1.0"?> +<tool_dependency> + <package name="R_3_1_1" version="3.1.1"> + <repository changeset_revision="6ca21e466ef6" name="package_r_3_1_1" owner="fubar" prior_installation_required="True" toolshed="https://testtoolshed.g2.bx.psu.edu" /> + </package> + <package name="graphicsmagick" version="1.3.18"> + <repository changeset_revision="2fd4eb971ba5" name="package_graphicsmagick_1_3" owner="iuc" prior_installation_required="True" toolshed="https://testtoolshed.g2.bx.psu.edu" /> + </package> + <package name="ghostscript" version="9.10"> + <repository changeset_revision="9345d2740f0c" name="package_ghostscript_9_10" owner="devteam" prior_installation_required="True" toolshed="https://testtoolshed.g2.bx.psu.edu" /> + </package> + <package name="glmnet_lars_2_14" version="2.14"> + <install version="1.0"> + <actions> + <action type="setup_r_environment"> + <repository changeset_revision="6ca21e466ef6" name="package_r_3_1_1" owner="fubar" toolshed="https://testtoolshed.g2.bx.psu.edu"> + <package name="R_3_1_1" version="3.1.1" /> + </repository> + <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/glmnet_1.9-8.tar.gz?raw=true</package> + <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/lars_1.2.tar.gz?raw=true</package> + <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/lattice_0.20-29.tar.gz?raw=true</package> + </action> + </actions> + </install> + <readme> + Yeee Haaa! + Lasso for Galaxy + </readme> + </package> +</tool_dependency>