Mercurial > repos > fubar > rglasso
changeset 5:2b7e28499fd8 draft
Fixes to paths in git for deps
| author | fubar |
|---|---|
| date | Sat, 31 Oct 2015 06:12:27 -0400 |
| parents | fb4959ed5b2b |
| children | 875e1cd37bce |
| files | .shed.yml readme.rst rgToolFactory.py rg_nri.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 test-data/genTest.R test-data/nri_test1.xls test-data/nri_test1_out.html test-data/nri_test1_out.xls tool_dependencies.xml |
| diffstat | 14 files changed, 0 insertions(+), 3126 deletions(-) [+] |
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--- a/.shed.yml Sat Oct 31 02:26:24 2015 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,9 +0,0 @@ -categories: -- Statistics -description: glmnet and lars for galaxy -long_description: | - Allows hybrid lasso-ridge regression using glmnet -name: rglasso_1_9_8 -owner: fubar -remote_repository_url: https://github.com/galaxyproject/tools-iuc/tree/master/tools/rglasso -type: unrestricted
--- a/readme.rst Sat Oct 31 02:26:24 2015 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,39 +0,0 @@ -glmnet wrappers -=============== - -This is a self installing Galaxy tool exposing the glmnet_ R package which has excellent documentation at -glmnet_ Minimal details are provided in this wrapper - please RTM to get the best out of it. - -The tool exposes the entire range of penalised maximum likelihood -GLM models ranging from pure lasso (set alpha to 1) to pure ridge-regression (set alpha to 0). - -These models can be k-fold internally cross validated to help select an "optimal" predictive or classification -algorithm. Predictive coefficients for each included independent variable are output for each model. - -Predictors can be forced into models to adjust for known confounders or explanatory factors. - -The glmnet_ implementation of the coordinate descent algorithm is fast and efficient even on relatively large problems -with tens of thousands of predictors and thousands of samples - such as normalised microarray intensities and anthropometry -on a very large sample of obese patients. - -The user supplies a tabular file with rows as samples and columns containing observations, then chooses -as many predictors as required. A separate model will be output for each of potentially multiple dependent -variables. Models are reported as the coefficients for terms in an 'optimal' model. -These optimal predictors are selected by repeatedly setting -aside a random subsample, building a model in the remainder and estimating AUC or deviance -using k (default 10) fold internal cross validation. For each of these steps, a random 1/k -of the samples are set aside and used to estiamte performance of an optimal model estimated -from the remaining samples. Plots are provided showing the range of these (eg 10) internal validation -estimates and mean model AUC (binomial) or residual deviance plots at each penalty increment step. - -A full range of link functions are available including Gaussian, Poisson, Binomial and -Cox proportional hazard time to failure for censored data in this wrapper. - -Note that multinomial and multiresponse gaussian models are NOT yet implemented since I have not yet -had use for them - send code! - -.. _glmnet: http://web.stanford.edu/~hastie/glmnet/glmnet_alpha.html - -Wrapper author: Ross Lazarus -19 october 2014 -
--- a/rgToolFactory.py Sat Oct 31 02:26:24 2015 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,649 +0,0 @@ -# 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): - """ - 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 - 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.html = [] - self.cl = (opts.interpreter,self.sfile) - 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.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,cwd=self.opts.output_dir,env=my_env) - retval = p.wait() - 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() - else: - p = subprocess.Popen(self.cl,shell=False,env=my_env) - retval = p.wait() - if self.opts.make_HTML: - self.makeHtml() - return retval - - - def remove_me_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() - return retval - - -def main(): - u = """ - This is a Galaxy wrapper. It expects to be called by a special purpose tool.xml as (eg): - <command interpreter="python">rgToolFactory.py --script_path "$scriptPath" --tool_name "foo" --interpreter "Rscript" - </command> - The tool writes a script to a scriptPath using a configfile. - Some things in the script are templates. - The idea here is that depending on how this code is called, it uses the specified interpreter - to run a (hopefully correct) script/template. Optionally generates a clone of itself - which will run that same script/template as a toolshed repository tarball for uploading to a toolshed. - There's now an updated version which allows arbitrary parameters. - And so it goes. - """ - 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/rg_nri.xml Sat Oct 31 02:26:24 2015 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,633 +0,0 @@ -<tool id="rg_nri" name="NRI" version="0.03"> - <description>and other model improvement measures</description> - <requirements> - <requirement type="package" version="3.2.2">R_3_2_2</requirement> - <requirement type="package" version="1.3.18">graphicsmagick</requirement> - <requirement type="package" version="9.10">ghostscript</requirement> - <requirement type="package" version="3.2">glmnet_lars_3_2</requirement> - </requirements> - <command interpreter="python"> - rgToolFactory.py --script_path "$runme" --interpreter "Rscript" --tool_name "rg_NRI" - --output_dir "$html_file.files_path" --output_html "$html_file" --make_HTML "yes" - </command> -<configfiles> -<configfile name="runme"> - -<![CDATA[ - -### http://www.researchgate.net/publication/264672640_R_function_for_Risk_Assessment_Plot__reclassification_metrics_NRI_IDI_cfNRI code -### http://cjasn.asnjournals.org/content/early/2012/05/24/CJN.09590911.full is the reference -### lots of little tweaks and but fixes. Using t as a variable name seems fraught to me. -### Ross Lazarus october 2014 for a Galaxy tool wrapper using the toolfactory infrastucture - -############################################################################# -###Functions to create risk assessment plots and associated summary statistics -############################################################################# -### -### (c) 2012 Dr John W Pickering, john.pickering@otago.ac.nz, and Dr David Cairns -### Last modified August 2014 -### -### Redistribution and use in source and binary forms, with or without -### modification, are permitted provided that the following conditions are met: -### * Redistributions of source code must retain the above copyright -### notice, this list of conditions and the following disclaimer. -### * Redistributions in binary form must reproduce the above copyright -### notice, this list of conditions and the following disclaimer in -### the documentation and/or other materials provided with the distribution - -### FUNCTIONS -### raplot -### Produces a Risk Assessment Plot and outputs the coordinates of the four curves -### Based on: Pickering, J. W. and Endre, Z. H. (2012). New Metrics for Assessing Diagnostic Potential of -### Candidate Biomarkers. Clinical Journal of the American Society of Nephrology, 7, 1355–1364. doi:10.2215/CJN.09590911 -### -### statistics.raplot -### Produces the NRIs, IDIs, IS, IP, AUCs. -### Based on: Pencina, M. J., D'Agostino, R. B. and Steyerberg, E. W. (2011). -### Extensions of net reclassification improvement calculations to measure usefulness of new biomarkers. Statistics in Medicine, 30(1), 11–21. doi:10.1002/sim.4085 -### Pencina, M. J., D'Agostino, R. B. and Vasan, R. S. (2008). Evaluating the added predictive ability of a new marker: From area under the ROC curve to reclassification and beyond. -### Statistics in Medicine, 27(2), 157–172. doi:10.1002/sim.2929 -### DeLong, E., DeLong, D. and Clarke-Pearson, D. (1988). Comparing the areas under 2 or more correlated receiver operating characteristic curves - a nonparametric approach. -### Biometrics, 44(3), 837–845. -### -### summary.raplot -### Produces the NRIs, IDIs, IS, IP, AUCs with confidence intervals using a bootstrap or asymptotic procedure. (I prefer bootstrap which is chosed by cis=c("boot")) - - -### Required arguments for all functions: -### x1 is calculated risk (eg from a glm) for the null model, i.e. predict(,type="response") on a glm object -### x2 is calculated risk (eg from a glm) for the alternative model -### y is the case-control indicator (0 for controls, 1 for cases) -### Optional argument -### t are the boundaries of the risks for each group (ie 0, 1 and the thresholds beteween. eg c(0,0,3,0,7,1)). If missing, defaults to c(0, the incidence, 1) - - -### risk assessment plot - -library('e1071') -library('caret') -library('pROC') -library('Hmisc') -library('pracma') - -raplot = function(x1, x2, y, outplot, title) { - - roc.model1 = roc(y, x1) - roc.model2 = roc(y, x2) - sens.model1 = roc.model1\$sensitivities - spec.model1 = 1 - roc.model1\$specificities - n.model1 = length(sens.model1) - thresh.model1 = roc.model1\$thresholds - thresh.model1 = thresh.model1[c(-1,-n.model1)] - sens.model1 = sens.model1[c(-1,-n.model1)] - spec.model1 = spec.model1[c(-1,-n.model1)] - sens.model2 = roc.model2\$sensitivities - spec.model2 = 1 - roc.model2\$specificities - n.model2 = length(sens.model2) - thresh.model2 = roc.model2\$thresholds - thresh.model2[1]=0 - thresh.model2[length(thresh.model2)]=1 - thresh.model2 = thresh.model2[c(-1,-n.model2)] - sens.model2 = sens.model2[c(-1,-n.model2)] - spec.model2 = spec.model2[c(-1,-n.model2)] - - n.model1 = length(sens.model1) - n.model2 = length(sens.model2) - - ### actual plotting - pdf(outplot) - plot(thresh.model1, sens.model1, xlim = c(0, 1), ylim = c(0, 1), type = "n", - lty = 2, lwd = 2, xlab = "Risk of Event", ylab = "", col = "black", main=title) - grid() - - polygon(x = c(thresh.model1, thresh.model2[n.model2:1]), - y = c(sens.model1, sens.model2[n.model2:1]), border = NA, col = gray(0.8)) - polygon(x = c(thresh.model1, thresh.model2[n.model2:1]), - y = c(spec.model1, spec.model2[n.model2:1]), border = NA, col = gray(0.8)) - - lines(thresh.model1, sens.model1, type = "l", lty = 2, lwd = 2, col = "black") - lines(thresh.model2, sens.model2, type = "l", lty = 1, lwd = 2, col = "black") - - lines(thresh.model1, spec.model1, type = "l", lty = 2, lwd = 2, col = "red") - lines(thresh.model2, spec.model2, type = "l", lty = 1, lwd = 2, col = "red") - - text(x = -0.15, y = 0.4, labels = "Sensitivity, ", col = "black", xpd = TRUE, srt = 90) - text(x = -0.15, y = 0.4 + 0.175, labels = "1-Specificity", col = "red", xpd = TRUE, srt = 90) - legend("topleft", c("Event: New model", "Event: Baseline model", - "No Event: New model", "No Event: Baseline model"), - col = c("black", "black", "red", "red"), - lty = c(1,2, 1, 2), lwd = 2, bty = "n") - dev.off() - return(data.frame("Null.p.sens"=thresh.model1, - "Null.sens"=sens.model1, - "Null.p.1spec"=thresh.model1, - "Null.1spec"=sens.model1, - "Alt.p.sens"=thresh.model2, - "Alt.sens"=sens.model2, - "Alt.p.1spec"=thresh.model2, - "Alt.1spec"=sens.model2)) - -} - - - -### statistics from a raplot (is an adaptation of improveProb() from Hmisc) - -statistics.raplot = function(x1, x2, y, threshvec) -{ - - s = is.na(x1 + x2 + y) ###Remove rows with missing data - if (any(s)) { - smiss = sum(s) - s = !s - x1 = x1[s] - x2 = x2[s] - y = y[s] - print.noquote(paste('Warning: removed',smiss,'cases with missing values')) - } - n = length(y) - y = as.numeric(y) - u = sort(unique(y)) - if (length(u) != 2 || u[1] != 0 || u[2] != 1) { - print.noquote("INPUT ERROR: y must have only two values: 0 and 1") - sink() - quit(save="no",status=2) - } - r = range(x1, x2) - if (r[1] < 0 || r[2] > 1) { - print.noquote("INPUT ERROR: x1 and x2 must be in [0,1]") - sink() - quit(save="no",status=3) - } - incidence=sum(y)/n - if (missing(threshvec)) { - threshvec=c(0, incidence,1) - print(paste('threshvec missing. using',paste(threshvec,collapse=','))) - } - a = (y == 1) - b = (y == 0) - na = sum(a) - nb = sum(b) - d = x2 - x1 - ### NRI - n.thresh=length(threshvec)-1 - risk.class.x1.ev=cut2(x1[a],threshvec) - risk.class.x2.ev=cut2(x2[a],threshvec) - thresh=c() - lt = length(threshvec) - for (i in 1:(lt-1)) { - thresh[i] = paste("[",toString(threshvec[i]),",",toString(threshvec[i+1]),"]") - } - levels(risk.class.x1.ev)=thresh - levels(risk.class.x2.ev)=thresh - cM.ev=confusionMatrix(risk.class.x2.ev,risk.class.x1.ev) - pup.ev=0 - pdown.ev=0 - for (i in 1:(n.thresh-1)) { pup.ev = pup.ev + sum(cM.ev\$table[(i+1):n.thresh,i])} - for (i in 2:n.thresh) { pdown.ev = pdown.ev + sum(cM.ev\$table[1:(i-1),i])} - pup.ev=pup.ev/na - pdown.ev=pdown.ev/na - risk.class.x1.ne=cut2(x1[b],threshvec) - risk.class.x2.ne=cut2(x2[b],threshvec) - levels(risk.class.x1.ne)=thresh - levels(risk.class.x2.ne)=thresh - cM.ne=confusionMatrix(risk.class.x2.ne,risk.class.x1.ne) - pup.ne=0 - pdown.ne=0 - for (i in 1:(n.thresh-1)){pup.ne=pup.ev+sum(cM.ne\$table[(i+1):n.thresh,i])} - for (i in 2:n.thresh){pdown.ne=pdown.ne+sum(cM.ne\$table[1:(i-1),i])} - pdown.ne=pdown.ne/nb - pup.ne=pup.ne/nb - nri = pup.ev - pdown.ev - (pup.ne - pdown.ne) - se.nri = sqrt((pup.ev + pdown.ev)/na + (pup.ne + pdown.ne)/nb) - z.nri = nri/se.nri - nri.ev = pup.ev - pdown.ev - se.nri.ev = sqrt((pup.ev + pdown.ev)/na) - z.nri.ev = nri.ev/se.nri.ev - nri.ne = pdown.ne - pup.ne - se.nri.ne = sqrt((pdown.ne + pup.ne)/nb) - z.nri.ne = nri.ne/se.nri.ne - ### Category Free NRI calculations - cfpup.ev = mean(d[a] > 0) - cfpup.ne = mean(d[b] > 0) - cfpdown.ev = mean(d[a] < 0) - cfpdown.ne = mean(d[b] < 0) - cfnri = cfpup.ev - cfpdown.ev - (cfpup.ne - cfpdown.ne) - se.cfnri = sqrt((cfpup.ev + cfpdown.ev)/na + (cfpup.ne + cfpdown.ne)/nb) - z.cfnri = cfnri/se.cfnri - cfnri.ev = cfpup.ev - cfpdown.ev - se.cfnri.ev = sqrt((cfpup.ev + cfpdown.ev)/na) - z.cfnri.ev = cfnri.ev/se.cfnri.ev - cfnri.ne = cfpdown.ne - cfpup.ne - se.cfnri.ne = sqrt((cfpdown.ne + cfpup.ne)/nb) - z.cfnri.ne = cfnri.ne/se.cfnri.ne - ### IDI calculations - improveSens = sum(d[a])/na - improveSpec = -sum(d[b])/nb - idi.ev = mean(improveSens) - idi.ne = mean(improveSpec) - idi = idi.ev - idi.ne - var.ev = var(d[a])/na - se.idi.ev = sqrt(var.ev) - z.idi.ev = idi.ev/se.idi.ev - var.ne = var(d[b])/nb - se.idi.ne = sqrt(var.ne) - z.idi.ne = idi.ne/se.idi.ne - se.idi = sqrt(var.ev + var.ne) - z.idi = idi/se.idi - ### AUC calculations - roc.x1 = roc(y, x1) - auc.x1 = auc(roc.x1) - ci.auc.x1 = ci.auc(roc.x1) - se.auc.x1 = (ci.auc.x1[3] - auc.x1)/qnorm(0.975) - roc.x2 = roc(y, x2) - auc.x2 = auc(roc.x2) - ci.auc.x2 = ci.auc(roc.x2) - se.auc.x2 = (ci.auc.x2[3] - auc.x2)/qnorm(0.975) - roc.test.x1.x2 = roc.test(roc.x1, roc.x2) ###Uses the default Delong method - sens.x1 = roc.x1\$sensitivities - spec.x1 = 1 - roc.x1\$specificities - n.x1 = length(sens.x1) - x1 = roc.x1\$thresholds - x1 = x1[c(-1,-n.x1)] - sens.x1 = sens.x1[c(-1,-n.x1)] - spec.x1 = spec.x1[c(-1,-n.x1)] - sens.x2 = roc.x2\$sensitivities - spec.x2 = 1 - roc.x2\$specificities - n.x2 = length(sens.x2) - x2 = roc.x2\$thresholds - x2 = x2[c(-1,-n.x2)] - sens.x2 = sens.x2[c(-1,-n.x2)] - spec.x2 = spec.x2[c(-1,-n.x2)] - ### Integrated sensitivity and 1-specificity calculations - is.x1 = trapz(x = x1, y = sens.x1) ### area under curves (relates to integrated sens, 1-spec) - is.x2 = trapz(x = x2, y = sens.x2) - ip.x1 = trapz(x = x1, y = spec.x1) - ip.x2 = trapz(x = x2, y = spec.x2) - - ### Output - output = c(n, na, nb, pup.ev, pup.ne, pdown.ev, pdown.ne, nri, se.nri, z.nri, - nri.ev, se.nri.ev, z.nri.ev, nri.ne, se.nri.ne, z.nri.ne, - cfpup.ev, cfpup.ne, cfpdown.ev, cfpdown.ne, cfnri, se.cfnri, z.cfnri, - cfnri.ev, se.cfnri.ev, z.cfnri.ev, cfnri.ne, se.cfnri.ne, z.cfnri.ne, - improveSens, improveSpec, idi.ev, se.idi.ev, z.idi.ev, idi.ne, - se.idi.ne, z.idi.ne, idi, se.idi, z.idi, is.x1, NA, is.x2, NA, - ip.x1, NA, ip.x2, NA, auc.x1, se.auc.x1, auc.x2, se.auc.x2, - roc.test.x1.x2\$p.value,incidence) - names(output) = c("n", "na", "nb", "pup.ev", "pup.ne", "pdown.ev", "pdown.ne", - "nri", "se.nri", "z.nri", "nri.ev", "se.nri.ev", "z.nri.ev", - "nri.ne", "se.nri.ne", "z.nri.ne", - "cfpup.ev", "cfpup.ne", "cfpdown.ev", "cfpdown.ne", - "cfnri", "se.cfnri", "z.cfnri", "cfnri.ev", "se.cfnri.ev", "z.cfnri.ev", - "cfnri.ne", "se.cfnri.ne", "z.cfnri.ne", "improveSens", "improveSpec", - "idi.ev", "se.idi.ev", "z.idi.ev", "idi.ne", "se.idi.ne", - "z.idi.ne", "idi", "se.idi", "z.idi", "is.x1", "se.is.x1", - "is.x2", "se.is.x2", "ip.x1", "se.ip.x1", "ip.x2", "se.ip.x2", - "auc.x1", "se.auc.x1", "auc.x2", "se.auc.x2", - "roc.test.x1.x2.pvalue","incidence") - resdf = data.frame(N=n, Na=na, Nb=nb, pup.ev=pup.ev, pup.ne=pup.ne, pdown.ev=pdown.ev, pdown.ne=pdown.ne, NRI=nri, NRI.se=se.nri, NRI.z=z.nri, - NRI.ev=nri.ev, NRI.ev.se=se.nri.ev, NRI.ev.z=z.nri.ev, NRI.ne=nri.ne, NRI.ne.se=se.nri.ne, NRI.ne.z=z.nri.ne, - cfpup.ev=cfpup.ev, cfpup.ne=cfpup.ne, cfpdown.ev=cfpdown.ev, cfpdown.ne=cfpdown.ne, CFNRI=cfnri, CFNRI.se=se.cfnri, CFNRI.z=z.cfnri, - CFNRI.ev=cfnri.ev, CFNRI.ev.se=se.cfnri.ev, CFNRI.ev.z=z.cfnri.ev, CFNRI.ne=cfnri.ne, CFNRI.ne.se=se.cfnri.ne, CFNRI.ne.z=z.cfnri.ne, - improvSens=improveSens, improvSpec=improveSpec, IDI.ev=idi.ev, IDI.ev.se=se.idi.ev, IDI.ev.z=z.idi.ev, IDI.ne=idi.ne, - IDI.ne.se=se.idi.ne, IDI.ne.z=z.idi.ne, IDI=idi, IDI.se=se.idi, IDI.z=z.idi, isx1=is.x1, isx2=is.x2, - ipxi=ip.x1, ipx2=ip.x2, AUC.x1=auc.x1, AUC.x1.se=se.auc.x1, AUC.x2=auc.x2, AUC.x2.se=se.auc.x2, - roctestpval=roc.test.x1.x2\$p.value,incidence=incidence) - tr = t(resdf) - tresdf = data.frame(measure=colnames(resdf),value=tr[,1]) - return(list(resdf=tresdf,output=output)) -} - - -### More comprehensive summary statistics from a raplot -### Choice of confidence intervals determined through asymptotics or bootstrapping (n.boot = ### of bootstrap resamples) -### dp is number of decimal places for results table - -summary.raplot = function(x1, x2, y, threshvec, cis = "boot", conf.level = 0.95, n.boot = 2000, dp = 4, stat_ra=NA) -{ - results = stat_ra - if (cis == "boot") { - print.noquote("Bootstrap estimates for SE") - results.boot = matrix(NA, n.boot, length(names(results))) - - colnames(results.boot) = names(results) - - for (i in 1:n.boot) { - ###boot.index = sample(length(cc.status), replace = TRUE) - ###risk.model1.boot = risk.model1[boot.index] - ###risk.model2.boot = risk.model2[boot.index] - ###cc.status.boot = cc.status[boot.index] - boot.index = sample(length(y), replace = TRUE) - risk.model1.boot = x1[boot.index] - risk.model2.boot = x2[boot.index] - cc.status.boot = y[boot.index] - r = statistics.raplot(x1 = risk.model1.boot, x2 = risk.model2.boot, y = cc.status.boot) - results.boot[i, ] = r\$output - } - - results.se.boot = apply(results.boot, 2, sd) - print(paste(results.se.boot,collapse=',')) - - - results[grep("se", names(results))] = results.se.boot[grep("se", names(results)) - 1] - - } - - - - ### calculate cis and return - - z = abs(qnorm((1 - conf.level)/2)) - - results.matrix = matrix(NA, 24, 2) - - results.matrix[1, ] = c("Total (n)", results["n"]) - results.matrix[2, ] = c("Events (n)", results["na"]) - results.matrix[3, ] = c("Non-events (n)", results["nb"]) - results.matrix[4, ] = c("Category free NRI and summary statistics","-------------------------") - results.matrix[5, ] = c("cfNRI events (%)", - paste(round(100*results["cfnri.ev"], dp-2), " (", - round(100*results["cfnri.ev"] - z * 100*results["se.cfnri.ev"], dp-2), - " to ", round(100*results["cfnri.ev"] + - z * 100*results["se.cfnri.ev"], dp-2), ")", sep = "")) - results.matrix[6, ] = c("cfNRI non-events (%)", - paste(round(100*results["cfnri.ne"], dp-2), " (", - round(100*results["cfnri.ne"] - z * 100*results["se.cfnri.ne"], dp)-2, - " to ", round(100*results["cfnri.ne"] + z * 100*results["se.cfnri.ne"], - dp-2), ")", sep = "")) - results.matrix[7, ] = c("cfNRI (%)", - paste(round(100*results["cfnri"], dp-2), " (", - round(100*results["cfnri"] - z * 100*results["se.cfnri"], dp-2), - " to ", round(100*results["cfnri"] + z * 100*results["se.cfnri"], - dp-2), ")", sep = "")) - results.matrix[8, ] = c("NRI and summary statistics","-------------------------") - results.matrix[9, ] = c("NRI events (%)", - paste(round(100*results["nri.ev"], dp-2), " (", - round(100*results["nri.ev"] - z * 100*results["se.nri.ev"], dp-2), - " to ", round(100*results["nri.ev"] + - z * 100*results["se.nri.ev"], dp-2), ")", sep = "")) - results.matrix[10, ] = c("NRI non-events (%)", - paste(round(100*results["nri.ne"], dp-2), " (", - round(100*results["nri.ne"] - z * 100*results["se.nri.ne"], dp-2), - " to ", round(100*results["nri.ne"] + z * 100*results["se.nri.ne"], - dp-2), ")", sep = "")) - results.matrix[11, ] = c("NRI (%)", - paste(round(100*results["nri"], dp-2), " (", - round(100*results["nri"] - z * 100*results["se.nri"], dp-2), - " to ", round(100*results["nri"] + z * 100*results["se.nri"], - dp-2), ")", sep = "")) - results.matrix[12, ] = c("IDI and summary statistics","-------------------------") - results.matrix[13, ] = c("IDI events", - paste(round(results["idi.ev"], dp), " (", - round(results["idi.ev"] - z * results["se.idi.ev"], dp), - " to ", round(results["idi.ev"] + z * results["se.idi.ev"], - dp), ")", sep = "")) - results.matrix[14, ] = c("IDI non-events", - paste(round(results["idi.ne"], dp), " (", - round(results["idi.ne"] - z * results["se.idi.ne"], dp), - " to ", round(results["idi.ne"] + z * results["se.idi.ne"], - dp), ")", sep = "")) - results.matrix[15, ] = c("IDI", - paste(round(results["idi"], dp), " (", - round(results["idi"] - z * results["se.idi"], dp), - " to ", round(results["idi"] + z * results["se.idi"], - dp), ")", sep = "")) - results.matrix[16, ] = c("IS (null model)", - paste(round(results["is.x1"], dp), " (", - round(results["is.x1"] - z * results["se.is.x1"], dp), - " to ", round(results["is.x1"] + z * results["se.is.x1"], - dp), ")", sep = "")) - results.matrix[17, ] = c("IS (alt model)", - paste(round(results["is.x2"], dp), " (", - round(results["is.x2"] - z * results["se.is.x2"], dp), - " to ", round(results["is.x2"] + z * results["se.is.x2"], - dp), ")", sep = "")) - results.matrix[18, ] = c("IP (null model)", - paste(round(results["ip.x1"], dp), " (", - round(results["ip.x1"] - z * results["se.ip.x1"], dp), - " to ", round(results["ip.x1"] + z * results["se.ip.x1"], - dp), ")", sep = "")) - results.matrix[19, ] = c("IP (alt model)", - paste(round(results["ip.x2"], dp), " (", - round(results["ip.x2"] - z * results["se.ip.x2"], dp), - " to ", round(results["ip.x2"] + z * results["se.ip.x2"], - dp), ")", sep = "")) - results.matrix[20, ] = c("AUC","-------------------------") - results.matrix[21, ] = c("AUC (null model)", - paste(round(results["auc.x1"], dp), " (", - round(results["auc.x1"] - z * results["se.auc.x1"], dp), - " to ", round(results["auc.x1"] + z * results["se.auc.x1"], - dp), ")", sep = "")) - results.matrix[22, ] = c("AUC (alt model)", - paste(round(results["auc.x2"], dp), " (", - round(results["auc.x2"] - z * results["se.auc.x2"], dp), - " to ", round(results["auc.x2"] + z * results["se.auc.x2"], - dp), ")", sep = "")) - results.matrix[23, ] = c("difference (P)", round(results["roc.test.x1.x2.pvalue"], dp)) - results.matrix[24, ] = c("Incidence", round(results["incidence"], dp)) - - return(results.matrix) -} - - - -]]> - -options(width=120) -options(digits=5) -logf = file("rgNRI.log", open = "a") -sink(logf,type = c("output", "message")) -Out_Dir = "$html_file.files_path" -Input1 = "$input1" -Input2 = "$input2" -myTitle = "$title" -outtab = "$nri_file" -input1_obs = $input1_observed -input1_pred = $input1_predicted -input1_id = $input1_id -input2_obs = $input2_observed -input2_pred = $input2_predicted -input2_id = $input2_id -in1 = read.table(Input1,head=T,sep='\t') -in2 = read.table(Input2,head=T,sep='\t') -id1 = in1[,input1_id] -id2 = in2[,input2_id] -useme1 = in1[which(id1 %in% id2),] -useme2 = in2[which(id2 %in% id1),] -id1 = useme1[,input1_id] -id2 = useme2[,input2_id] -useme1 = useme1[order(id1),] -useme2 = useme2[order(id2),] -x1 = useme1[,input1_pred] -x2 = useme2[,input2_pred] -y1 = useme1[,input1_obs] -y2 = useme2[,input2_obs] -n.boot = $CImeth.nboot -conf.level = 0.95 -cis = "$CImeth.cis" -digits = 4 -nydiff = sum(y1 != y2) -if (nydiff > 0) { - print.noquote(paste('Input error: observed status column has',nydiff,'differences - cannot reliably proceed')) - quit(save="no",status=1) - } -y = y2 -outplot = 'rgNRI_EventRisk.pdf' -res = raplot(x1=x1, x2=x2, y=y, outplot=outplot,title=myTitle) - -stats = statistics.raplot(x1=x1, x2=x2, y=y) -res1 = stats\$resdf -out1 = stats\$output -print.noquote('Results:') -print.noquote(res1,digits=4) -res2 = summary.raplot(x1=x1, x2=x2, y=y, cis = cis, conf.level = conf.level, n.boot = n.boot, dp = digits, stat_ra=out1) -print.noquote('Summary:') -print.noquote(res2,digits=4) -write.table(format(res1,digits=4),outtab,quote=F, col.names=F,sep="\t",row.names=F) -print.noquote('SessionInfo for this R session:') -sessionInfo() -print.noquote('warnings for this R session:') -warnings() -sink() -</configfile> -</configfiles> - <inputs> - <param name="title" type="text" value="NRI test" label="Plot Title" help="Will appear as the title for the comparison plot"/> - <param name="input1" type="data" format="tabular" label="Select a tabular file from the baseline model with predicted and observed outcome column for each subject" - multiple='False' help="Observed and predicted status columns must be selected from this file below - NOTE both models must be in same order with exact matches in all observed outcomes" optional="False"/> - <param name="input1_observed" label="Select column containing observed outcome (0 for no event, 1 for an event)" type="data_column" data_ref="input1" numerical="True" - multiple="False" use_header_names="True" optional="False" help = "Observed outcomes are compared in the two files to check that the datasets are from the same data"/> - <param name="input1_predicted" label="Select column containing predicted event probabilies from baseline model" type="data_column" data_ref="input1" numerical="True" - multiple="False" use_header_names="True" optional="False" help="Must be in range 0-1"/> - <param name="input1_id" label="Select column containing subject ID from baseline model" type="data_column" data_ref="input1" numerical="True" - multiple="False" use_header_names="True" optional="False" help="Subect IDs are needed to match subjects to compare predictions in the two inputs"/> - <param name="input2" type="data" format="tabular" label="Select a tabular file from the new model with predicted and observed outcome columns for each subject" - multiple='False' help="Observed and predicted status columns must be selected from this file below" /> - <param name="input2_observed" label="Select column containing observed outcome (0 for no event, 1 for an event)" type="data_column" data_ref="input2" numerical="True" - multiple="False" use_header_names="True" optional="False" help = "Observed outcomes are compared in the two files to check that the datasets are from the same data"/> - <param name="input2_predicted" label="Select column containing predicted event probabilities from the new model" type="data_column" data_ref="input2" numerical="True" - multiple="False" use_header_names="True" optional="False" help="Must be in range 0-1"/> - <param name="input2_id" label="Select column containing subject ID from the new model" type="data_column" data_ref="input2" numerical="True" - multiple="False" use_header_names="True" optional="False" help="Subect IDs are needed to match subjects to compare predictions in the two inputs"/> - <conditional name="CImeth"> - <param name="cis" type="select" label="CI calculation method" - help="Bootstrap will take time - a long time for thousands - asymptotic is quick and informative"> - <option value="asymptotic" selected="true">Asymptotic estimate</option> - <option value="boot">Bootstrap for empirical CIs</option> - </param> - <when value="boot"> - <param name="nboot" type="integer" value="1000" label="Number of bootstrap replicates"/> - </when> - <when value="asymptotic"> - <param name="nboot" type="hidden" value="1000"/> - </when> - </conditional> - </inputs> - <outputs> - <data format="html" name="html_file" label="${title}.html"/> - <data format="tabular" name="nri_file" label="${title}_nrires.xls"/> - </outputs> - <tests> - <test> - <param name='title' value='nri_test1' /> - <param name='input1' value='nri_test1.xls' ftype='tabular' /> - <param name='input2' value='nri_test1.xls' ftype='tabular' /> - <param name='input1_id' value="1" /> - <param name='input1_observed' value="2" /> - <param name='input1_predicted' value="3" /> - <param name='input2_observed' value="2" /> - <param name='input2_predicted' value="4" /> - <output name='html_file' file='nri_test1_out.html' compare='diff' lines_diff='10' /> - <output name='nri_file' file='nri_test1_out.xls' /> - </test> -</tests> -<help> - -**Before you start** - -This is a simple tool to calculate various measures of improvement in prediction between two models described in pickering_paper_ -It is based on an R script pickering_code_ written by Dr John W Pickering and Dr David Cairns from sunny Otago University which -has been debugged and slightly adjusted to fit a Galaxy tool wrapper. - - -**What it does** - -Copied from the documentation in pickering_code_ :: - - - Functions to create risk assessment plots and associated summary statistics - - - (c) 2012 Dr John W Pickering, john.pickering@otago.ac.nz, and Dr David Cairns - Last modified August 2014 - - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions are met: - * Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - * Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in - the documentation and/or other materials provided with the distribution - - FUNCTIONS - raplot - Produces a Risk Assessment Plot and outputs the coordinates of the four curves - Based on: Pickering, J. W. and Endre, Z. H. (2012). New Metrics for Assessing Diagnostic Potential of - Candidate Biomarkers. Clinical Journal of the American Society of Nephrology, 7, 1355–1364. doi:10.2215/CJN.09590911 - - statistics.raplot - Produces the NRIs, IDIs, IS, IP, AUCs. - Based on: Pencina, M. J., D'Agostino, R. B. and Steyerberg, E. W. (2011). Extensions of net reclassification improvement calculations to - measure usefulness of new biomarkers. Statistics in Medicine, 30(1), 11–21. doi:10.1002/sim.4085 - Pencina, M. J., D'Agostino, R. B. and Vasan, R. S. (2008). Evaluating the added predictive ability of a new marker: From area under the - ROC curve to reclassification and beyond. - Statistics in Medicine, 27(2), 157–172. doi:10.1002/sim.2929 - DeLong, E., DeLong, D. and Clarke-Pearson, D. (1988). Comparing the areas under 2 or more correlated receiver operating characteristic curves - a nonparametric approach. - Biometrics, 44(3), 837–845. - - summary.raplot - Produces the NRIs, IDIs, IS, IP, AUCs with confidence intervals using a bootstrap or asymptotic procedure. (I prefer bootstrap which is chosed by cis=c("boot")) - - - Required arguments for all functions: - x1 is calculated risk (eg from a glm) for the null model, i.e. predict(,type="response") on a glm object - x2 is calculated risk (eg from a glm) for the alternative model - y is the case-control indicator (0 for controls, 1 for cases) - Optional argument - t are the boundaries of the risks for each group (ie 0, 1 and the thresholds beteween. eg c(0,0,3,0,7,1)). If missing, defaults to c(0, the incidence, 1) - - -**Input** - -The observed and predicted outcomes from two models to be compared. - -**Output** - -Lots'o'measures (TM) see pickering_paper_ for details - -**Attributions** - -pickering_paper_ is the paper the caclulations performed by this tool is based on - -pickering_code_ is the R function from John Pickering exposed by this Galaxy tool with minor modifications and hacks by Ross Lazarus. - -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 -.. _pickering_code: http://www.researchgate.net/publication/264672640_R_function_for_Risk_Assessment_Plot__reclassification_metrics_NRI_IDI_cfNRI -.. _pickering_paper: http://cjasn.asnjournals.org/content/early/2012/05/24/CJN.09590911.full -.. _Galaxy: http://getgalaxy.org - - -</help> - -<citations> - <citation type="doi">doi: 10.2215/CJN.09590911</citation> -</citations> -</tool> - - -
--- a/rglasso_cox.xml Sat Oct 31 02:26:24 2015 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,910 +0,0 @@ -<tool id="rglasso_cox" name="Lasso" version="0.03"> - <description>and cox regression using elastic net</description> - <requirements> - <requirement type="package" version="3.2.2">R_3_2_2</requirement> - <requirement type="package" version="1.3.18">graphicsmagick</requirement> - <requirement type="package" version="9.10">ghostscript</requirement> - <requirement type="package" version="3.2">glmnet_lars_3_2</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> -<configfiles> -<configfile name="runme"> -<![CDATA[ -library('glmnet') -library('lars') -library('survival') -library('pec') - - -message=function(x) {print.noquote(paste(x,sep=''))} - - -ross.cv.glmnet = function (x, y, weights, offset = NULL, lambda = NULL, type.measure = c("mse", - "deviance", "class", "auc", "mae"), nfolds = 10, foldid, - grouped = TRUE, keep = FALSE, parallel = FALSE, ...) -{ - if (missing(type.measure)) - type.measure = "default" - else type.measure = match.arg(type.measure) - if (!is.null(lambda) && length(lambda) < 2) - stop("Need more than one value of lambda for cv.glmnet") - N = nrow(x) - if (missing(weights)) - weights = rep(1, N) - else weights = as.double(weights) - y = drop(y) - glmnet.call = match.call(expand.dots = TRUE) - sel = match(c("type.measure", "nfolds", "foldid", "grouped", - "keep"), names(glmnet.call), F) - if (any(sel)) - glmnet.call = glmnet.call[-sel] - glmnet.call[[1]] = as.name("glmnet") - glmnet.object = glmnet(x, y, weights = weights, offset = offset, - lambda = lambda, ...) - glmnet.object\$call = glmnet.call - is.offset = glmnet.object\$offset - lambda = glmnet.object\$lambda - if (inherits(glmnet.object, "multnet")) { - nz = predict(glmnet.object, type = "nonzero") - nz = sapply(nz, function(x) sapply(x, length)) - nz = ceiling(apply(nz, 1, median)) - } - else nz = sapply(predict(glmnet.object, type = "nonzero"), - length) - if (missing(foldid)) - foldid = sample(rep(seq(nfolds), length = N)) - else nfolds = max(foldid) - if (nfolds < 3) - stop("nfolds must be bigger than 3; nfolds=10 recommended") - outlist = as.list(seq(nfolds)) - if (parallel && require(foreach)) { - outlist = foreach(i = seq(nfolds), .packages = c("glmnet")) %dopar% - { - sel = foldid == i - if (is.matrix(y)) - y_sub = y[!sel, ] - else y_sub = y[!sel] - if (is.offset) - offset_sub = as.matrix(offset)[!sel, ] - else offset_sub = NULL - glmnet(x[!sel, , drop = FALSE], y_sub, lambda = lambda, - offset = offset_sub, weights = weights[!sel], - ...) - } - } - else { - for (i in seq(nfolds)) { - sel = foldid == i - if (is.matrix(y)) - y_sub = y[!sel, ] - else y_sub = y[!sel] - if (is.offset) - offset_sub = as.matrix(offset)[!sel, ] - else offset_sub = NULL - outlist[[i]] = glmnet(x[!sel, , drop = FALSE], - y_sub, lambda = lambda, offset = offset_sub, - weights = weights[!sel], ...) - } - } - fun = paste("cv", class(glmnet.object)[[1]], sep = ".") - cvstuff = do.call(fun, list(outlist, lambda, x, y, weights, - offset, foldid, type.measure, grouped, keep)) - cvm = cvstuff\$cvm - cvsd = cvstuff\$cvsd - cvname = cvstuff\$name - - out = list(lambda = lambda, cvm = cvm, cvsd = cvsd, cvup = cvm + - cvsd, cvlo = cvm - cvsd, nzero = nz, name = cvname, glmnet.fit = glmnet.object) - if (keep) - out = c(out, list(fit.preval = cvstuff\$fit.preval, foldid = foldid)) - - lamin = if (type.measure == "auc") - getmin(lambda, -cvm, cvsd) - else getmin(lambda, cvm, cvsd) - out = c(out, as.list(lamin)) - hitsse = rep(0,ncol(x)) - hitsmin = rep(0,ncol(x)) - names(hitsse) = colnames(x) - names(hitsmin) = colnames(x) - olmin = lamin\$lambda.min - ol1sd = lamin\$lambda.1se - lambs = c(olmin,ol1sd) - names(lambs) = c('olmin','ol1sd') - for (cvfit in outlist) { - colmin = which(cvfit\$lambda == olmin) - col1se = which(cvfit\$lambda == ol1sd) - nzmin = which(cvfit\$beta[,colmin] != 0) - nz1se = which(cvfit\$beta[,col1se] != 0) - hitsse[nz1se] = hitsse[nz1se] + 1 - hitsmin[nzmin] = hitsmin[nzmin] + 1 - } - obj = c(out,list(cvhits.1se=hitsse,cvhits.min=hitsmin)) - class(obj) = "cv.glmnet" - obj -} - -mdsPlot = function(dm,myTitle,groups=NA,outpdfname,transpose=T) -{ - - samples = colnames(dm) - mt = myTitle - 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) - } - npred = ncol(mydata) - d = dist(mydata) - fit = cmdscale(d,eig=TRUE, k=min(10,npred-2)) - xmds = fit\$points[,1] - ymds = fit\$points[,2] - pdf(outpdfname) - plot(xmds, ymds, xlab="Dimension 1", ylab="Dimension 2", - main=paste(mt,"MDS Plot"),type="n", col=pcols, cex=0.35) - text(xmds, ymds, labels = row.names(mydata), cex=0.35, col=pcols) - grid(col="lightgray",lty="dotted") - dev.off() -} - - -getpredp_logistic = function(x,yvec,yvarname,id) -{ - yvals = unique(yvec) - if (length(yvals) != 2) { - message(c('ERROR: y does not have 2 values =',paste(yvals,collapse=','))) - return(NA) - } - cols = colnames(x) - if (length(cols) == 0) { - message('ERROR: No columns in input x? Cannot predict!') - return(NA) - } - cn = paste(cols, collapse = ' + ') - - formstring=paste("y ~",cn) - form = as.formula(formstring) - ok = complete.cases(x) - - if (sum(ok) < length(ok)) { - x = x[ok,] - yvec = yvec[ok] - id = id[ok] - } - nx = data.frame(id=id,x,y=yvec) - print('nx,yvec:') - print(head(nx,n=3)) - print(yvec) - mdl = glm(form, data=nx, family="binomial", na.action=na.omit) - message(c('Model format =',formstring)) - message(paste('Predictive model details used to generate logistic outcome probabilities for',yvarname,':')) - print(summary(md1)) - print(anova(md1)) - predp = predict(md1,nx,type="response") - p1 = data.frame(id=id,pred_response=predp,obs_response=yvec) - return(p1) -} - -getpredp_cox = function(x,time,status,id,predict_at) -{ - cols = colnames(x) - if (length(cols) == 0) { - message('ERROR: No columns in input x? Cannot predict!') - return(NA) - } - cn = paste(colnames(x), collapse = ' + ') - - formstring=paste("Surv(time, status) ~",cn) - - form = as.formula(formstring) - - ok = complete.cases(x) - - if (sum(ok) < length(ok)) { - x = x[ok,] - time = time[ok] - status = status[ok] - id = id[ok] - } - nx = data.frame(x,time=time,status=status) - m1 = coxph(form, data=nx,singular.ok=TRUE) - print.noquote('Predictive model details used to generate survival probabilities:') - print.noquote(m1) - predpq = predictSurvProb(object=m1, newdata=nx, times=predict_at) - predpq = 1-predpq - colnames(predpq) = paste('p_surv_to',predict_at,sep='_') - p1 = data.frame(id=id,predpq,time=time,status=status) - return(p1) -} - - -dolasso_cox = function(x,y,debugOn=F,maxsteps=10000,nfold=10,xcolnames,ycolnames,optLambda='lambda.1se',out_full=F,out_full_file=NA, - out_pred=F,out_pred_file=NA,cox_id=NA, descr='Cox test',do_standard=F,alpha=0.9,penalty,predict_at,mdsplots=F) -{ - logf = file("cox_rglasso.log", open = "a") - sink(logf,type = c("output", "message")) - res = NULL - if (mdsplots==T) { - outpdfname = 'cox_x_in_sample_space_MDS.pdf' - p = try({ mdsPlot(x,'measurements in sample space',groups=NA,outpdfname=outpdfname,transpose=T) },T) - if (class(p) == "try-error") - { - print.noquote(paste('Unable to produce predictors in sample space mds plot',p)) - } - outpdfname = 'cox_samples_in_x_space_MDS.pdf' - p = try({mdsPlot(x,'samples in measurement space',groups=y,outpdfname=outpdfname,transpose=F) },T) - if (class(p) == "try-error") - { - print.noquote(paste('Unable to produce samples in measurement space mds plots',p)) - } - } - if (is.na(predict_at)) { predict_at = quantile(y) } - message(paste('@@@ Cox model will be predicted at times =',paste(predict_at,collapse=','))) - do_standard = do_standard - standardize = do_standard - normalize = do_standard - p = try({larsres = glmnet(x,y,family='cox',standardize=standardize,alpha=alpha,penalty.factor=penalty )},T) - if (class(p) == "try-error") - { - print.noquote('Unable to run cox glmnet on your data') - print.noquote(p) - 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(format(allres,digits=5),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 - - p = try({larscv=ross.cv.glmnet(x,y,family=fam,type.measure='deviance',penalty=penalty)},T) - if (class(p) == "try-error") { - print.noquote(paste('Unable to cross validate your data',p)) - sink() - return(NA) - } - lse = larscv\$cvhits.1se - lmin = larscv\$cvhits.min - tot = lse + lmin - allhits = data.frame(hits_lambda_1se = lse,hits_lambda_min = lmin) - nzhits = allhits[which(tot != 0),] - message('Times each predictor was selected in CV models (excluding zero count predictors)') - print.noquote(nzhits) - out_nz_file = 'cox_cross_validation_model_counts.xls' - write.table(nzhits,out_nz_file,quote=FALSE, sep="\t",row.names=F) - - outpdf = paste('cox',descr,'glmnet_cvdeviance.pdf',sep='_') - - p = try( - { - pdf(outpdf) - plot(larscv,main='Deviance',label=T) - grid() - dev.off() - },T) - if (optLambda == 'lambda.min') { - best_lambda = larscv\$lambda.min - bestcoef = as.matrix(coef(larscv, s = "lambda.min")) - } else { - best_lambda = larscv\$lambda.1se - bestcoef = as.matrix(coef(larscv, s = "lambda.1se")) - } - inmodel = which(bestcoef != 0) - coefs = bestcoef[inmodel] - preds = rownames(bestcoef)[inmodel] - - names(coefs) = preds - pen = as.logical( ! penalty[inmodel]) - if (out_pred==T) - { - if (length(inmodel) > 0 ) { - predcols = inmodel - xmat = as.matrix(x[,predcols]) - colnames(xmat) = preds - bestpred = getpredp_cox(x=xmat,time=y[,'time'],status=y[,'status'],id=cox_id, predict_at=predict_at) - pred = data.frame(responsep=bestpred, best_lambda=best_lambda,lamchoice=optLambda,alpha=alpha) - write.table(pred,out_pred_file,quote=FALSE, sep="\t",row.names=F) - } else { print.noquote('WARNING: No coefficients in selected model to predict with - no predictions made') } - } - if (debugOn) { - print.noquote(paste('best_lambda=',best_lambda,'saving cox respreds=',paste(names(coefs),collapse=','),'as predictors of survival. Coefs=',paste(coefs,collapse=','))) - } - p = try({res = data.frame(regulator=names(coefs),partial_likelihood=coefs,forced_in=pen,glmnet_model='cox',best_lambda=best_lambda, - lambdaChoice=optLambda,alpha=alpha)},T) - if (class(p) == "try-error") { - message(paste('@@@ unable to return a dataframe',p)) - sink() - return(NA) - } - print.noquote('@@@ Results preview:') - print.noquote(res,digits=5) - sink() - return(res) - -} - - -do_lasso = function(x=NA,y=NA,do_standard=T,debugOn=T,defaultFam="gaussian",optLambda='minLambda',descr='description', indx=1,target='target',sane=F, - alpha=0.9,nfold=10,penalty=c(),out_pred=F,out_pred_file='outpred',yvarname='yvar',id=c(),mdsplots=F) -{ - logf = file(paste(target,"rglasso.log",sep='_'), open = "a") - sink(logf,type = c("output", "message")) - res = NA - phe_is_bin = (length(unique(y)) == 2) - forcedin = paste(colnames(x)[which(penalty == 0)],collapse=',') - fam = "gaussian" - if (defaultFam %in% c("poisson","binomial","gaussian","multinomial")) fam=defaultFam - if (phe_is_bin == T) { - fam = "binomial" - } - print.noquote(paste('target=',target,'is binary=',phe_is_bin,'dim(x)=',paste(dim(x),collapse=','),'length(y)=',length(y),'force=',forcedin,'fam=',fam)) - standardize = do_standard - p = try({larsres = glmnet(x,y,family=fam,standardize=standardize,maxit=10000,alpha=alpha,penalty.factor=penalty) },T) - if (class(p) == "try-error") - { - print(paste('ERROR: unable to run glmnet for target',target,'error=',p)) - sink() - return(NA) - } - - mt = paste('Glmnet fraction deviance for',target) - outpdf = paste(target,'glmnetPath.pdf',sep='_') - pdf(outpdf) - plot(larsres,main=mt,label=T) - grid() - dev.off() - - outpdf = paste(target,'glmnetDeviance.pdf',sep='_') - - mt2 = paste('Glmnet lambda for',target) - - pdf(outpdf) - plot(larsres,xvar="lambda",main=mt2,label=T) - grid() - dev.off() - - larscv = NA - if (fam=="binomial") { - tmain = paste(target,'AUC') - outpdf = paste(target,'glmnetCV_AUC.pdf',sep='_') - p = try({larscv = ross.cv.glmnet(x=x,y=y,family=fam,type.measure='auc')},T) - } else { - tmain = paste(target,'CV MSE') - outpdf = paste(target,'glmnetCV_MSE.pdf',sep='_') - p = try({larscv = ross.cv.glmnet(x=x,y=y,family=fam,type.measure='mse')},T) - } - if (class(p) == "try-error") - { - print(paste('ERROR: unable to run cross validation for target',target,'error=',p)) - sink() - return(NA) - } - - pdf(outpdf) - plot(larscv,main=tmain) - grid() - dev.off() - - lse = larscv\$cvhits.1se - lmin = larscv\$cvhits.min - tot = lse + lmin - allhits = data.frame(pred=colnames(x),hits_lambda_1se = lse,hits_lambda_min = lmin) - nzhits = allhits[which(tot != 0),] - message('Total hit count for each predictor over all CV models (excluding zero count predictors)') - print.noquote(nzhits) - out_nz_file = paste(target,'cross_validation_model_counts.xls',sep='_') - write.table(nzhits,out_nz_file,quote=FALSE, sep="\t",row.names=F) - - ipenalty = c(0,penalty) - if (optLambda == 'lambda.min') { - best_lambda = larscv\$lambda.min - bestpred = as.matrix(coef(larscv, s = "lambda.min")) - } else { - best_lambda = larscv\$lambda.1se - bestpred = as.matrix(coef(larscv, s = "lambda.1se")) - } - inmodel = which(bestpred != 0) - coefs = bestpred[inmodel,1] - preds = rownames(bestpred)[inmodel] - iforced = ipenalty[inmodel] - forced = ! as.logical(iforced) - names(coefs) = preds - ncoef = length(coefs) - 1 - if (out_pred==T && fam=="binomial") - { - print.noquote(paste('Predicting',target,'probabilities from binomial glmnet at alpha',alpha,'and lambda',best_lambda)) - bestpred = predict.glmnet(larsres,s=best_lambda,newx=x,type="response") - bestpred = exp(bestpred)/(1+exp(bestpred)) - pred = data.frame(id=id,y=y,predp=as.vector(bestpred), best_lambda=best_lambda) - write.table(pred,out_pred_file,quote=FALSE, sep="\t",row.names=F) - } - 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,ncoef=ncoef, - best_lambda=best_lambda,lambdaChoice=optLambda,alpha=alpha),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=0.9,nfold=10,xcolnames=c(),ycolnames=c(),optLambda='minLambda', out_pred_file=NA, - descr="describe me",do_standard=F,defaultFam="gaussian",penalty=c(),out_pred=F,cox_id=c(),mdsplots=F,xfilt=0.95) -{ - logf = file("rglasso.log", open = "a") - sink(logf,type = c("output", "message")) - xdat = predvars - xm = data.matrix(xdat) - res = NULL - id = cox_id - depnames = ycolnames - ndep = length(depnames) - if (mdsplots==T) { - outpdfname = 'rglasso_x_in_sample_space_MDS.pdf' - p = try({ mdsPlot(xm,'measurements in sample space',groups=NA,outpdfname=outpdfname,transpose=T) },T) - if (class(p) == "try-error") - { - print.noquote(paste('Unable to produce predictors in sample space mds plot. Error:',p)) - } - outpdfname = 'rglasso_samples_in_x_space_MDS.pdf' - p = try({mdsPlot(xm,'samples in measurement space',groups=NA,outpdfname=outpdfname,transpose=F) },T) - if (class(p) == "try-error") - { - print.noquote(paste('Unable to produce samples in measurement space mds plot. Error:',p)) - } - } - ndat = nrow(xm) - cfracs = colSums(! is.na(xm))/ndat - keepme = (cfracs >= xfilt) - print.noquote(paste('Removing', sum(! keepme), 'xvars with more than',xfilt,'fraction missing')) - vars = apply(xm,2,var,na.rm=T) - xm = xm[,keepme] - 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] - } - if (fam == "binomial") {y = as.factor(y)} - x = xm - id = cox_id - if (fam != "cox") { - ok = complete.cases(x,y) - if (sum(! ok) > 0) { - message(paste('@@@ Removing',sum(! ok),'cases with missing y of',length(y),'@@@')) - y = y[(ok)] - x = x[(ok),] - id = id[(ok)] - } - } - ok = complete.cases(y) - 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 { - if (i == 1) { outpred = out_pred_file - } else { - outpred = paste(target,'predicted_output.xls') - } - regres = do_lasso(x=x,y=y,do_standard=do_standard,debugOn=debugOn,defaultFam=defaultFam,optLambda=optLambda,out_pred_file=outpred, - descr=descr,indx=i,target=target,alpha=alpha,nfold=nfold,penalty=penalty,out_pred=out_pred,yvarname=target,id=id,mdsplots=mdsplots) - if (! is.na(regres)) { res = rbind(res,regres) } - } - } - print.noquote('@@@ Results preview:') - print.noquote(res,digits=5) - 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',do_standard=do_standard,defaultFam="gaussian",alpha=0.05) - return(regres_out) -} -]]> -options(width=512) -options(digits=5) -alpha = $alpha -nfold = $nfold -optLambda = "$optLambda" -Out_Dir = "$html_file.files_path" -Input = "$input1" -indat = read.table(Input,head=T,sep='\t') -datcols = colnames(indat) -myTitle = "$title" -outtab = "$model_file" -do_standard = as.logical("$do_standard") -mdsplots = as.logical("$mdsplots") -fam = "$model.fam" -xvar_cols_in = "$xvar_cols" -force_xvar_cols_in = "$force_xvar_cols" -xvar_cols = as.numeric(strsplit(xvar_cols_in,",")[[1]]) -force_xvar_cols = c() -penalties = rep(1,length(datcols)) -forced_in = NA - -logxform = "$logxform_cols" -if (logxform != "None") { - logxform_cols = as.numeric(strsplit(logxform,",")[[1]]) - if (length(logxform_cols) > 0) { - small = 1e-10 - sset = indat[,logxform_cols] - zeros = which(sset==0,arr.ind=T) - nz = nrow(zeros) - if (nz > 0) { - message(paste('Log transforming encountered',nz,'zeros - added 1e-10')) - sset[zeros] = sset[zeros] + small - lset = log(sset) - indat[,logxform_cols] = lset - } - } -} -if (force_xvar_cols_in != "None") -{ - 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)] - penalties[force_xvar_cols] = 0 -} -penalty = penalties[xvar_cols] -forcedin = paste(datcols[which(penalties == 0)],collapse=',') -cox_id_col = NA -cox_id = NA - -message(paste('@@@ Using alpha =',alpha,'for all models')) -x = indat[,xvar_cols] -nx = nrow(x) -cx = ncol(x) -message(paste('@@@@ Input has',nx,'samples and',cx,'predictors')) -if (cx > nx) { -message('@@@ WARNING: Models will have more variables than cases so glmnet will likely return one of many possible solutions! Please DO NOT expect reliable results - glmnet is clever but not magical @@@') -} - -xcolnames = datcols[xvar_cols] - -if (file.exists(Out_Dir) == F) dir.create(Out_Dir) -out_full = F -out_full_file = NA -out_pred_file = "" -out_pred = as.logical("$model.output_pred") - -#if $model.fam == "binomial" or $model.fam == "cox": - cox_id_col = $model.cox_id - cox_id = indat[,cox_id_col] - if (out_pred == T) { - out_pred_file="$output_pred_file" - rownames(x) = cox_id - } -#end if -#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="$output_full_file" } - yvar_cols = c(cox_time,cox_status) - ycolnames = c('time','status') - istat = as.double(indat[,cox_status]) - itime = as.double(indat[,cox_time]) - predict_at = quantile(itime) - if ("$model.predict_at" > "") - { - pa = "$model.predict_at" - predict_at = as.numeric(strsplit(pa,",")[[1]]) - } - y = data.frame(time = itime, status = istat) - ustat = unique(istat) - if ((length(ustat) != 2) | (! 1 %in% ustat ) | (! 0 %in% ustat)) - { - print.noquote(paste('INPUT ERROR: status must have 0 (censored) or 1 (event) but found',paste(ustat,collapse=',') )) - quit(save='no',status=1) - } - y = as.matrix(y) - x = as.matrix(x) - print.noquote(paste('@@@ Cox model will predict yvar=',datcols[cox_status],'using cols=',paste(xcolnames,collapse=','),'n preds=',length(xcolnames), - 'forced in=',forcedin)) - regres_out = dolasso_cox(x=x,y=y,debugOn=F,maxsteps=10000,nfold=nfold,xcolnames=xcolnames,ycolnames=ycolnames,optLambda=optLambda,out_full=out_full,out_full_file=out_full_file, - out_pred=out_pred,out_pred_file=out_pred_file,cox_id=cox_id,descr=myTitle,do_standard=do_standard,alpha=alpha,penalty=penalty,predict_at=predict_at,mdsplots=mdsplots) -#else: - yvar_cols = "$model.yvar_cols" - yvar_cols = as.numeric(strsplit(yvar_cols,",")[[1]]) - ycolnames = datcols[yvar_cols] - print.noquote(paste('@@@',fam,'model will predict yvar=',paste(ycolnames,collapse=','),'using cols=',paste(xcolnames,collapse=','),'n preds=',length(xcolnames), - 'forced in=',forcedin)) - y = data.matrix(indat[,yvar_cols]) - print.noquote(paste('Model will use',fam,'link function to predict yvar=',paste(ycolnames,collapse=','),'n preds=',length(xcolnames),'forced in=',forcedin)) - regres_out = dolasso_generic(predvars=x,depvars=y,debugOn=F, maxsteps=10000,nfold=nfold,xcolnames=xcolnames,ycolnames=ycolnames,optLambda=optLambda,out_pred_file=out_pred_file, - descr=myTitle,do_standard=do_standard,defaultFam=fam,alpha=alpha,penalty=penalty,out_pred=out_pred,cox_id=cox_id,mdsplots=mdsplots) -#end if - -write.table(format(regres_out,digits=5),outtab,quote=FALSE, sep="\t",row.names=F) -print.noquote('@@@ SessionInfo for this R session:') -sessionInfo() -warnings() - -</configfile> -</configfiles> - <inputs> - <param name="title" type="text" value="lasso test" 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' optional="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="False" - multiple="True" use_header_names="True" force_select="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="False" - multiple="True" use_header_names="True" force_select="False"/> - <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"> - <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="False" - multiple="True" use_header_names="True" help = "If multiple, each will be modelled against all the x variables and reported separately." force_select="True"/> - <param name="output_full" type="hidden" value='F' /> - <param name="output_pred" type="hidden" value='F' /> - <param name="cox_id" label="Select column containing a unique sample identifier" - help = "Only really needed for output sample specific predicted values downstream." - type="data_column" data_ref="input1" numerical="False" force_select="True" - multiple="False" use_header_names="True" /> - </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="False" - multiple="True" use_header_names="True" help = "If multiple, each will be modelled against all the x variables and reported separately." force_select="True"/> - <param name="output_full" type="hidden" value='F' /> - <param name="output_pred" type="select" label="Create a tabular output with predicted values for each subject from the optimal model for (eg) NRI estimates" > - <option value="F" selected="true">No predicted value output file</option> - <option value="T">Create a predicted value output file</option> - </param> - <param name="cox_id" label="Select column containing a unique sample identifier" - help = "Only really needed for output sample specific predicted values downstream." - type="data_column" data_ref="input1" numerical="False" force_select="True" - multiple="False" use_header_names="True" /> - <param name="predict_at" type="hidden" value='' /> - - </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." force_select="True"/> - <param name="output_full" type="hidden" value='F' /> - <param name="output_pred" type="hidden" value='F' /> - <param name="predict_at" type="hidden" value='' /> - <param name="cox_id" label="Select column containing a unique sample identifier" - help = "Optional. Only really needed for output sample specific predicted values downstream. Free - enjoy" - type="data_column" data_ref="input1" numerical="True" force_select="False" - multiple="False" use_header_names="True" /> - </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" force_select="True" - 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" force_select="True" - 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="cox_id" label="Select column containing a unique sample identifier" - help = "Optional. Only really needed for output sample specific predicted values downstream. Free - enjoy" - type="data_column" data_ref="input1" numerical="False" force_select="False" - multiple="False" use_header_names="True" /> - <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> - <param name="output_pred" type="select" label="Create a tabular output with predicted values for each subject from the optimal model for (eg) NRI estimates" > - <option value="F" selected="true">No predicted value output file</option> - <option value="T">Create a predicted value output file</option> - </param> - <param name="predict_at" type="text" value='' label="Provide a comma separated list of times to make a prediction for each subject" - optional="True" help="Default (blank) will return predictions at 0%,25%,50%,75%,100% of the observed times which should be informative" /> - - </when> - </conditional> - <param name="optLambda" type="select" label="Value to use when reporting optimal model and coefficients" help="minLambda will have more predictors - 1SDLambda will be more parsimonious"> - <option value="lambda.1se" selected="true">Lambda + 1 SE of min MSE or AUC (fewer coefficients - more false negatives)</option> - <option value="lambda.min">Lambda at min MSE or max AUC (more coefficients - more false positives)</option> - </param> - <param name="logxform_cols" optional="True" label="Select numeric columns to be log transformed before use as predictors or dependent variables" type="data_column" - data_ref="input1" numerical="True" multiple="True" use_header_names="True" help = "The wisdom of doing this depends entirely on your predictors - eg can help diminish long-tailed outlier influence" - force_select="False"/> - <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="mdsplots" type="select" label="Generate MDS plots of samples in measurement space and measurements in sample space" > - <option value="False" selected="true">No MDS plots</option>l - <option value="True">Yes create MDS plots</option> - </param> - <param name="alpha" type="float" value="0.95" 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" 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="output_full_file" label="${title}_full_cox_model.xls"> - <filter>model['output_full'] == 'T'</filter> - </data> - <data format="tabular" name="output_pred_file" label="${title}_predicted_from_model.xls"> - <filter>model['output_pred'] == '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='logxform_cols' value='' /> - <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='cox_id' value='1' /> - <param name='predict_at' value='' /> - <param name='fam' value='cox' /> - <param name='yvar_cols' value='' /> - <param name='xvar_cols' value='3,4,5' /> - <param name='force_xvar_cols' value='3' /> - <param name='output_full' value='F' /> - <param name='output_pred' value='F' /> - <output name='model_file' file='coxlassotest_modelres.xls'> - <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_line line="regulator	partial_likelihood	forced_in	glmnet_model	best_lambda	lambdaChoice	alpha" /> - <has_n_columns n="7" /> - </assert_contents> - </output> - <output name='html_file' file='coxlassotest.html' compare='diff' lines_diff='16' /> - </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. - -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. - -**Warning about the tyrany of dimensionality** - -Yes, this package will select 'optimal' models even when you (optimistically) supply more predictors than you have cases. -The model returned is unlikely to represent the only informative regularisation path through your data - if you run repeatedly with -exactly the same settings, you will probably see many different models being selected. -This is not a software bug - the real problem is that you just don't have enough information in your data. - -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. Predictors 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. - -**Predicted event probabilities for Cox and Logistic models** - -If you want to compare (eg) two competing clinical predictions, there's a companion generic NRI tool -for predicted event probabilities. Estimates dozens of measures of improvement in prediction. Currently only works for identical id subjects -but can probably be extended to independent sample predictions. - -Given a model, we can generate a predicted p (for status 1) in binomial or cox frameworks so models can be evaluated in terms of NRI. -Of course, estimates are likely substantially inflated over 'real world' performance by being estimated from the same sample - but you probably -already knew that since you were smart enough to reach this far down into the on screen help. The author salutes you, intrepid reader! - -It may seem an odd thing to do, but we can predict p for an event for each subject from our original data, given a parsimonious model. Doing -this for two separate models (eg, forcing in an additional known explanatory measurement to the new model) allows comparison of the two models -predicted status for each subject, or the same model in independent populations to see how badly it does - -**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> - -<citations> - <citation type="bibtex"> -@Article{Friedman2010, title = {Regularization Paths for Generalized Linear Models via Coordinate Descent}, - author = {Jerome Friedman and Trevor Hastie and Robert Tibshirani}, - journal = {Journal of Statistical Software}, - year = {2010}, - volume = {33}, - number = {1}, - pages = {1--22}, - url = {http://www.jstatsoft.org/v33/i01/} - } - </citation> - <citation type="doi"> -10.1093/bioinformatics/bts573 - </citation> -</citations> -</tool>
--- a/test-data/cox_test.xls Sat Oct 31 02:26:24 2015 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,502 +0,0 @@ -time status rhubarb vegemite apple -575.966708 0 452.405468 30.339584 32286.089057 -1539.245319 1 329.689929 30.603839 15735.863202 -2072.798422 0 534.379263 32.474983 22639.136685 -1638.450154 0 362.522161 30.925996 31108.364370 -1771.625630 0 417.167751 31.444652 31317.491931 -4413.484706 1 459.947526 30.069033 11232.037882 -1519.376431 1 576.868414 30.102709 16539.948172 -4805.620290 0 554.756164 30.685550 9979.125726 -657.497124 0 439.481951 32.305768 22301.057122 -181.498205 1 581.498091 32.549519 10499.737992 -1630.552909 0 580.670671 30.825057 22321.821037 -2285.043061 1 588.034529 32.799742 8067.710675 -3697.405336 0 400.195630 31.826099 20975.730587 -3821.226480 0 561.466202 32.333230 17887.547850 -4810.837196 0 447.004938 32.547717 25383.925946 -3195.877681 1 482.169788 31.420740 27659.409774 -4556.750833 1 415.144072 31.960725 18401.138992 -3797.192061 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--- a/test-data/coxlassotest.html Sat Oct 31 02:26:24 2015 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,167 +0,0 @@ -<!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 18/02/2015 22:06:08</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] @@@ Cox model will be predicted at times = 3.724234,1325.40516225,2373.090418,3650.1730825,4989.405682 - -[1] Times each predictor was selected in CV models (excluding zero count predictors) - - hits_lambda_1se hits_lambda_min - -rhubarb 10 10 - -vegemite 6 6 - -[1] @@@ Results preview: - - regulator partial_likelihood forced_in glmnet_model best_lambda lambdaChoice alpha - -rhubarb rhubarb 0.0012215 TRUE cox 0.04197 lambda.1se 0.95 - - -</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 - -Loading required package: splines - -Warning messages: - -1: In if (is.na(predict_at)) { : - - the condition has length > 1 and only the first element will be used - -2: In if (class(p) == "try-error") { : - - the condition has length > 1 and only the first element will be used - -3: In plot.window(...) : "label" is not a graphical parameter - -4: In plot.xy(xy, type, ...) : "label" is not a graphical parameter - -5: In axis(side = side, at = at, labels = labels, ...) : - - "label" is not a graphical parameter - -6: In axis(side = side, at = at, labels = labels, ...) : - - "label" is not a graphical parameter - -7: In box(...) : "label" is not a graphical parameter - -8: In title(...) : "label" is not a graphical parameter - - -</pre> - -<div class="toolFormTitle">Other log output</div> - -<pre> - -## Toolfactory running rglasso as Rscript script - -[1] @@@ Using alpha = 0.95 for all models - -[1] @@@@ Input has 500 samples and 3 predictors - -[1] @@@ Cox model will predict yvar= status using cols= rhubarb,vegemite,apple n preds= 3 forced in= rhubarb - -[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 LC_MONETARY=en_AU.UTF-8 LC_MESSAGES=en_AU.UTF-8 LC_PAPER=en_AU.UTF-8 LC_NAME=C LC_ADDRESS=C LC_TELEPHONE=C LC_MEASUREMENT=en_AU.UTF-8 LC_IDENTIFICATION=C - -attached base packages: - -[1] splines methods stats graphics grDevices utils datasets base - -other attached packages: - -[1] pec_2.4.4 survival_2.37-7 lars_1.2 glmnet_1.9-8 Matrix_1.1-5 - -loaded via a namespace (and not attached): - -[1] codetools_0.2-10 foreach_1.4.2 grid_3.1.0 iterators_1.0.7 lattice_0.20-29 lava_1.3 prodlim_1.5.1 - -Warning messages: - -1: In if (is.na(predict_at)) { ... : - - the condition has length > 1 and only the first element will be used - -2: In if (class(p) == "try-error") { ... : - - the condition has length > 1 and only the first element will be used - -3: In plot.window(...) : "label" is not a graphical parameter - -4: In plot.xy(xy, type, ...) : "label" is not a graphical parameter - -5: In axis(side = side, at = at, labels = labels, ...) : - - "label" is not a graphical parameter - -6: In axis(side = side, at = at, labels = labels, ...) : - - "label" is not a graphical parameter - -7: In box(...) : "label" is not a graphical parameter - -8: In title(...) : "label" is not a graphical parameter - - -</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>5.3 KB</td></tr> -<tr><td><a href="cox_cross_validation_model_counts.xls">cox_cross_validation_model_counts.xls</a></td><td>42 B</td></tr> -<tr class="odd_row"><td><a href="cox_rglasso.log">cox_rglasso.log</a></td><td>522 B</td></tr> -<tr><td><a href="rglasso.Rscript">rglasso.Rscript</a></td><td>21.5 KB</td></tr> -<tr class="odd_row"><td><a href="rglasso_error.log">rglasso_error.log</a></td><td>803 B</td></tr> -<tr><td><a href="rglasso_runner.log">rglasso_runner.log</a></td><td>1.7 KB</td></tr> -</table></div><br/> -</div></body></html> -
--- a/test-data/coxlassotest_modelres.xls Sat Oct 31 02:26:24 2015 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,2 +0,0 @@ -regulator partial_likelihood forced_in glmnet_model best_lambda lambdaChoice alpha -rhubarb 0.0012215 TRUE cox 0.04197 lambda.1se 0.95
--- a/test-data/genTest.R Sat Oct 31 02:26:24 2015 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,10 +0,0 @@ -ids=c(1:50) -io1 = rep(c(0,0,0,0,1),10) -ip2 = runif(50)+0.1 -ip2[which(ip2>1.0)] = 1.0 -ip1 = runif(50)+0.05 -ip1[which(ip1>1.0)] = 1.0 -df=data.frame(id=ids,input1_observed=io1,input1_predicted=ip1,input2_predicted=ip2) -fout='test-data/nri_test1.xls' -write.table(df,file=fout, quote=FALSE, sep="\t",row.names=F) -# planemo test --job_output_files /home/rlazarus/tmp --test_output /home/rlazarus/tmp/startest/foo.html --update_test_data --galaxy_root /home/rlazarus/galaxy rg_nri.xml
--- a/test-data/nri_test1.xls Sat Oct 31 02:26:24 2015 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,51 +0,0 @@ -id input1_observed input1_predicted input2_predicted -1 0 0.431491391919553 0.786344331596047 -2 0 0.235500678373501 0.37771512279287 -3 0 0.458084875205532 0.343597211781889 -4 0 0.400509147858247 0.50323077281937 -5 1 0.463376078708097 0.28904068809934 -6 0 0.638350193109363 0.745497886231169 -7 0 0.909975615050644 1 -8 0 0.0712537909392267 0.613464191928506 -9 0 0.475019342219457 0.757945845043287 -10 1 0.146289554610848 0.948540200153366 -11 0 0.947521302569658 0.452743433788419 -12 0 0.512624199455604 0.107415618747473 -13 0 0.260238706320524 0.165854234388098 -14 0 0.252348658815026 0.866530107706785 -15 1 0.552841167151928 0.781692814920098 -16 0 0.203356911847368 0.397802447108552 -17 0 0.138653858751059 0.966900746012107 -18 0 0.237485485337675 0.126699626818299 -19 0 0.317045996850356 0.646952681383118 -20 1 0.271802965737879 0.828731852956116 -21 0 0.522744563594461 0.421856845868751 -22 0 0.819749742234126 0.628745510149747 -23 0 0.27876661689952 0.826974717108533 -24 0 0.077877387823537 0.12339704008773 -25 1 0.911161796143279 1 -26 0 0.567042255960405 1 -27 0 0.223712538788095 0.738010874623433 -28 0 0.306199585506693 0.388159935269505 -29 0 0.305726604955271 0.618934138352051 -30 1 0.919456032756716 1 -31 0 0.493467168603092 0.470883537689224 -32 0 0.626042458415031 0.753118774993345 -33 0 0.739080456737429 0.812446624180302 -34 0 0.786149174161255 0.768807894224301 -35 1 0.65491428244859 0.855509085487574 -36 0 0.62289561489597 0.585996081028134 -37 0 0.297479379642755 0.357051227381453 -38 0 0.796507286094129 0.775113077275455 -39 0 0.581927774194628 0.569900369411334 -40 1 0.76229884494096 0.877794308261946 -41 0 0.845583727071062 0.804134764615446 -42 0 0.79128703516908 1 -43 0 0.0507813768461347 0.97111975508742 -44 0 0.49284805515781 0.101141295302659 -45 1 0.665627157501876 0.121852198988199 -46 0 0.059045681450516 0.996109608234838 -47 0 0.0814651515800506 0.852028644317761 -48 0 0.162336849560961 1 -49 0 0.577456943178549 0.343083152920008 -50 1 0.905623372131959 0.651731353392825
--- a/test-data/nri_test1_out.html Sat Oct 31 02:26:24 2015 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,34 +0,0 @@ -<!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 "rg_NRI" run at 08/01/2015 16:12:57</div><br/> -<div class="toolFormTitle">rg log output</div> - -<pre> - -Error in library("e1071") : there is no package called ‘e1071’ - -Execution halted - - -</pre> - -<div class="toolFormTitle">Other log output</div> -/tmp/tmpq72Dni/job_working_directory/000/2/dataset_2_files/rg_NRI_runner.log is empty<br/> -<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="rg_NRI.Rscript">rg_NRI.Rscript</a></td><td>18.1 KB</td></tr> -<tr class="odd_row"><td><a href="rg_NRI_error.log">rg_NRI_error.log</a></td><td>84 B</td></tr> -<tr><td><a href="rg_NRI_runner.log">rg_NRI_runner.log</a></td><td>48 B</td></tr> -</table></div><br/> -</div></body></html> -
--- a/tool_dependencies.xml Sat Oct 31 02:26:24 2015 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,120 +0,0 @@ -<?xml version="1.0"?> -<tool_dependency> - <package name="R_3_2_2" version="3.2.2"> - <repository changeset_revision="883acf7a3ddb" name="package_r_3_2_2" owner="mvdbeek" prior_installation_required="True" toolshed="https://testtoolshed.g2.bx.psu.edu" /> - </package> - <package name="graphicsmagick" version="1.3.18"> - <repository changeset_revision="bff3f66adff2" 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_3_2" version="3.2"> - <install version="1.0"> - <actions> - <action type="setup_r_environment"> - <repository changeset_revision="883acf7a3ddb" name="package_r_3_2_2" owner="mvdbeek" toolshed="https://testtoolshed.g2.bx.psu.edu"> - 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