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Renamed documentation directory.

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File doc/COPYING

-                    GNU GENERAL PUBLIC LICENSE
-                       Version 3, 29 June 2007
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- Copyright (C) 2007 Free Software Foundation, Inc. <http://fsf.org/>
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-  IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
-WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
-THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
-GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
-USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
-DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
-PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
-EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
-SUCH DAMAGES.
-
-  17. Interpretation of Sections 15 and 16.
-
-  If the disclaimer of warranty and limitation of liability provided
-above cannot be given local legal effect according to their terms,
-reviewing courts shall apply local law that most closely approximates
-an absolute waiver of all civil liability in connection with the
-Program, unless a warranty or assumption of liability accompanies a
-copy of the Program in return for a fee.
-
-                     END OF TERMS AND CONDITIONS
-
-            How to Apply These Terms to Your New Programs
-
-  If you develop a new program, and you want it to be of the greatest
-possible use to the public, the best way to achieve this is to make it
-free software which everyone can redistribute and change under these terms.
-
-  To do so, attach the following notices to the program.  It is safest
-to attach them to the start of each source file to most effectively
-state the exclusion of warranty; and each file should have at least
-the "copyright" line and a pointer to where the full notice is found.
-
-    <one line to give the program's name and a brief idea of what it does.>
-    Copyright (C) <year>  <name of author>
-
-    This program is free software: you can redistribute it and/or modify
-    it under the terms of the GNU General Public License as published by
-    the Free Software Foundation, either version 3 of the License, or
-    (at your option) any later version.
-
-    This program is distributed in the hope that it will be useful,
-    but WITHOUT ANY WARRANTY; without even the implied warranty of
-    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-    GNU General Public License for more details.
-
-    You should have received a copy of the GNU General Public License
-    along with this program.  If not, see <http://www.gnu.org/licenses/>.
-
-Also add information on how to contact you by electronic and paper mail.
-
-  If the program does terminal interaction, make it output a short
-notice like this when it starts in an interactive mode:
-
-    <program>  Copyright (C) <year>  <name of author>
-    This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
-    This is free software, and you are welcome to redistribute it
-    under certain conditions; type `show c' for details.
-
-The hypothetical commands `show w' and `show c' should show the appropriate
-parts of the General Public License.  Of course, your program's commands
-might be different; for a GUI interface, you would use an "about box".
-
-  You should also get your employer (if you work as a programmer) or school,
-if any, to sign a "copyright disclaimer" for the program, if necessary.
-For more information on this, and how to apply and follow the GNU GPL, see
-<http://www.gnu.org/licenses/>.
-
-  The GNU General Public License does not permit incorporating your program
-into proprietary programs.  If your program is a subroutine library, you
-may consider it more useful to permit linking proprietary applications with
-the library.  If this is what you want to do, use the GNU Lesser General
-Public License instead of this License.  But first, please read
-<http://www.gnu.org/philosophy/why-not-lgpl.html>.

File doc/modules/assess1.py

-import orange
-import obiAssess
-import obiGeneSets
-
-gs = obiGeneSets.collections([":kegg:hsa"])
-data = orange.ExampleTable("DLBCL.tab")
-
-asl = obiAssess.AssessLearner()
-ass = asl(data, "hsa", geneSets=gs)
-
-print "Enrichments for the first example (10 pathways)"
-enrichments = ass(data[0])
-for patw, enric in sorted(enrichments.items())[:10]:
-    print patw, enric
-

File doc/modules/assess2.py

-import orange
-import obiAssess
-import obiGeneSets
-
-gs = obiGeneSets.collections([":kegg:hsa"])
-data = orange.ExampleTable("DLBCL.tab")
-
-asl = obiAssess.AssessLearner()
-ass = asl(data, "hsa", geneSets=gs)
-
-def genesetsAsAttributes(data, ass, domain=None):
-    """
-    Construct new data set with gene sets as attributes from data
-    set "data" with assess model "ass".
-    """
-
-    ares = {}
-    for ex in data:
-        cres = ass(ex)
-        for name,val in cres.items():
-            aresl = ares.get(name, [])
-            aresl.append(val)
-            ares[name] = aresl
-
-    ares = sorted(ares.items())
-
-    if not domain: #construct new domain instance if needed
-        domain = orange.Domain([ orange.FloatVariable(name=name) \
-            for name in [ a[0] for a in ares]], data.domain.classVar )
-
-    examples = [ [ b[zap] for a,b in ares ] + \
-        [ data[zap][-1] ]   for zap in range(len(data)) ]
-
-    et = orange.ExampleTable(domain, examples)
-    return et
-
-tdata = genesetsAsAttributes(data, ass)
-
-print "First 10 attributes of the first example in transformed data set"
-for pathw, enric in zip(tdata.domain,tdata[0])[:10]:
-    print pathw.name, enric.value

File doc/modules/assess3.py

-import orange
-import obiAssess
-import obiGeneSets
-
-gs = obiGeneSets.collections([":kegg:hsa"])
-data = orange.ExampleTable("DLBCL.tab")
-
-asl = obiAssess.AssessLearner()
-
-def genesetsAsAttributes(data, ass, domain=None):
-    """
-    Construct new data set with gene sets as attributes from data
-    set "data" with assess model "ass".
-    """
-
-    ares = {}
-    for ex in data:
-        cres = ass(ex)
-        for name,val in cres.items():
-            aresl = ares.get(name, [])
-            aresl.append(val)
-            ares[name] = aresl
-
-    ares = sorted(ares.items())
-
-    if not domain: #construct new domain instance if needed
-        domain = orange.Domain([ orange.FloatVariable(name=name) \
-            for name in [ a[0] for a in ares]], data.domain.classVar )
-
-    examples = [ [ b[zap] for a,b in ares ] + \
-        [ data[zap][-1] ]   for zap in range(len(data)) ]
-
-    et = orange.ExampleTable(domain, examples)
-    return et
-
-offer = None
-
-def transformLearningS(data):
-    ass = asl(data, "hsa", geneSets=gs)
-    et = genesetsAsAttributes(data, ass)
-
-    global offer
-    offer = (et.domain, ass) #save assess model
-
-    return et
-   
-def transformTestingS(data):
-    global offer
-    if not offer:
-        a = fdfsdsdd #exception
-
-    domain, ass = offer
-    offer = None
-
-    return genesetsAsAttributes(data, ass, domain)
-
-
-import orngBayes, orngTest, orngStat
-learners = [ orngBayes.BayesLearner() ]
-
-resultsOriginal = orngTest.crossValidation(learners, data, folds=10)
-resultsTransformed = orngTest.crossValidation(learners, data, folds=10, 
-    pps = [("L", transformLearningS), ("T", transformTestingS)])
-
-print "Original", "CA:", orngStat.CA(resultsOriginal), "AUC:", orngStat.AUC(resultsOriginal)
-print "Transformed", "CA:", orngStat.CA(resultsTransformed), "AUC:", orngStat.AUC(resultsTransformed)
-

File doc/modules/enrichment_graph.png

Removed
Old image

File doc/modules/geneMatch.py

-import obiGene
-import obiKEGG
-
-targets = obiKEGG.KEGGOrganism("9606").get_genes() #human NCBI ID
-
-gmkegg = obiGene.GMKEGG("9606")
-gmgo = obiGene.GMGO("9606")
-gmkegggo = obiGene.matcher([[gmkegg, gmgo]], direct=False)
-
-gmkegg.set_targets(targets)
-gmgo.set_targets(targets)
-gmkegggo.set_targets(targets)
-
-genes = [ "cct7", "pls1", "gdi1", "nfkb2", "dlg7" ]
-
-print "%12s" % "gene", "%12s" % "KEGG", "%12s" % "GO", "%12s" % "KEGG+GO"
-for gene in genes:
-    print "%12s" % gene, "%12s" % gmkegg.umatch(gene), \
-          "%12s" % gmgo.umatch(gene), \
-          "%12s" % gmkegggo.umatch(gene)
-

File doc/modules/geneMatch1.py

-import obiGene
-import obiKEGG
-
-keggorg = obiKEGG.KEGGOrganism("mmu")
-kegg_genes = keggorg.get_genes() 
-
-query = [ "Fndc4", "Itgb8", "Cdc34", "Olfr1403" ] 
-
-gm = obiGene.GMKEGG("mmu") #use KEGG aliases for gene matching
-gm.set_targets(kegg_genes) #set KEGG gene aliases as targets
-
-pnames = keggorg.list_pathways()
-
-for name in query:
-    match = gm.umatch(name) # matched kegg alias or None
-    if match:
-    	pwys = keggorg.get_pathways_by_genes([match])
-        print name, "is in", [ pnames[p] for p in pwys ] 

File doc/modules/geo_gds1.py

-"""
-Print out some information on specific GEO's data set.
-Does not download the data set.
-"""
-
-import obiGEO
-import textwrap
-
-gdsinfo = obiGEO.GDSInfo()
-gds = gdsinfo["GDS10"]
-
-print "ID:", gds["dataset_id"]
-print "Features:", gds["feature_count"]
-print "Genes:", gds["gene_count"]
-print "Organism:", gds["platform_organism"]
-print "PubMed ID:", gds["pubmed_id"]
-print "Sample types:"
-for sampletype in set([sinfo["type"] for sinfo in gds["subsets"]]):
-    ss = [sinfo["description"] for sinfo in gds["subsets"] if sinfo["type"]==sampletype]
-    print "  %s (%s)" % (sampletype, ", ".join(ss))
-print
-print "Description:"
-print "\n".join(textwrap.wrap(gds["description"], 70))

File doc/modules/geo_gds2.py

-import obiGEO
-reload(obiGEO)
-
-# gds = obiGEO.GDS("GDS10")
-gds = obiGEO.GDS("GDS1210")
-
-data = gds.getdata(report_genes=True, transpose=False)
-print "report_genes=True, transpose=False"
-print "Report=Genes, Rows=Genes/Spots"
-print "rows=%d cols=%d has_class=%s" % (len(data), len(data.domain.attributes), data.domain.classVar<>None)
-print
-
-data = gds.getdata(report_genes=False, transpose=False)
-print "report_genes=False, transpose=False"
-print "Report=Spots, Rows=Genes/Spots"
-print "rows=%d cols=%d has_class=%s" % (len(data), len(data.domain.attributes), data.domain.classVar<>None)
-print
-
-data = gds.getdata(report_genes=True, transpose=True)
-print "report_genes=True, transpose=True"
-print "Report=Genes, Rows=Samples"
-print "rows=%d cols=%d has_class=%s" % (len(data), len(data.domain.attributes), data.domain.classVar<>None)
-print "Class values:", " ".join([str(cv) for cv in data.domain.classVar.values]) 
-print
-
-
-data = gds.getdata(report_genes=True, transpose=True, sample_type="tissue")
-print 'report_genes=True, transpose=True sample_type="tissue"'
-print "Report=Genes, Rows=Samples"
-print "rows=%d cols=%d has_class=%s" % (len(data), len(data.domain.attributes), data.domain.classVar<>None)
-print "Class values:", " ".join([str(cv) for cv in data.domain.classVar.values]) 
-print

File doc/modules/geo_gds3.py

-import obiGEO
-
-gds = obiGEO.GDS("GDS1676")
-data = gds.getdata(sample_type="infection")
-print "Genes: %d, Samples: %d" % (len(data), len(data.domain.attributes))
-
-for a in data.domain.attributes:
-    print a.name, a.attributes

File doc/modules/geo_gds4.py

-import orngServerFiles
-import glob
-import re
-
-filenames = glob.glob(orngServerFiles.localpath("GEO") + "/GDS*.soft.gz")
-m = re.compile("(GDS[0-9]*).soft")
-print "%d data files cached:" % len(filenames)
-print " ".join([m.search(fn).group(1) for fn in filenames])
-

File doc/modules/geo_gds5.py

-"""
-Check all data files from GEO, find those which include at least N
-samples in all sample subsets of at least one sample type. Useful
-when, for instance, filtering out the data sets that could be used for
-supervised machine learning.
-"""
-
-import obiGEO
-
-def valid(info, n=40):
-    """Return a set of subset types containing more than n samples in every subset"""
-    invalid = set()
-    subsets = set([sinfo["type"] for sinfo in info["subsets"]])
-    for sampleinfo in info["subsets"]:
-        if len(sampleinfo["sample_id"]) < n:
-            invalid.add(sampleinfo["type"])
-    return subsets.difference(invalid)
-
-def report(stypes, info):
-    """Pretty-print GDS and valid susbset types"""
-    for id, sts in stypes:
-        print id
-        for st in sts:
-            print "  %s:" % st,
-            gds = info[id]
-            print ", ".join(["%s/%d" % (sinfo["description"], len(sinfo["sample_id"])) \
-                             for sinfo in gds["subsets"] if sinfo["type"]==st])
-
-gdsinfo = obiGEO.GDSInfo()
-valid_subset_types = [(id, valid(info)) for id, info in gdsinfo.items() if valid(info)]
-report(valid_subset_types, gdsinfo)

File doc/modules/geo_gds6.py

-import obiGEO
-import orange
-import orngTest
-import orngStat
-
-gds = obiGEO.GDS("GDS2960")
-data = gds.getdata(sample_type="disease state", transpose=True)
-print "Samples: %d, Genes: %d" % (len(data), len(data.domain.attributes))
-
-learners = [orange.LinearLearner]
-results = orngTest.crossValidation(learners, data, folds=10)
-print "AUC = %.3f" % orngStat.AUC(results)[0]

File doc/modules/gsea1.py

-import orange, obiGsea, obiGene
-
-data = orange.ExampleTable("iris")
-
-gen1 = dict([
-    ("sepal",["sepal length", "sepal width"]), 
-    ("petal",["petal length", "petal width", "petal color"])
-    ])
-
-res = obiGsea.runGSEA(data, matcher=obiGene.matcher([]), minSize=2, geneSets=gen1)
-print "%5s  %6s %6s %s" % ("LABEL", "NES", "P-VAL", "GENES")
-for name,resu in res.items():
-    print "%5s  %6.3f %6.3f %s" % (name, resu["nes"], resu["p"], str(resu["genes"]))

File doc/modules/gsea2.py

-import obiDicty
-import obiGeneSets
-import obiGsea
-import orange
-import obiGene
-
-dbc = obiDicty.DatabaseConnection()
-data = dbc.getData(sample='pkaC-', time="8")[0] #get first chip
-
-print "First 10 examples"
-for ex in data[:10]:
-    print ex
-
-matcher=obiGene.matcher([[obiGene.GMKEGG("ddi"),obiGene.GMDicty()]])
-
-genesets =  obiGeneSets.collections([":kegg:ddi"])
-res = obiGsea.runGSEA(data, matcher=matcher, minPart=0.05, geneSets=genesets, 
-    permutation="gene")
-
-print "GSEA results"
-print "%-40s %6s %6s %6s %7s" % ("LABEL", "NES", "P-VAL", "SIZE", "MATCHED") 
-for name,resu in res.items()[:10]: 
-    print "%-40s %6.3f %6.3f %6d %7d" % (name[:30], resu["nes"], resu["p"], 
-        resu["size"], resu["matched_size"]) 
-

File doc/modules/gsea3.py

-import obiGeneSets
-import obiGsea
-import orange
-import obiGene
-import obiGEO
-
-import obiGEO
-gds = obiGEO.GDS("GDS10")
-data = gds.getdata() 
-
-print "Possible phenotype descriptors:"
-print map(lambda x: x[0], obiGsea.allgroups(data).items())
-
-matcher=obiGene.matcher([obiGene.GMKEGG("9606")])
-
-phenVar = "tissue"
-geneVar = "gene" #use gene meta variable for gene names
-
-genesets =  obiGeneSets.collections([":kegg:hsa"])
-res = obiGsea.runGSEA(data, matcher=matcher, minPart=0.05, geneSets=genesets, 
-    permutation="class", n=10, phenVar=phenVar, geneVar=geneVar)
-
-print
-print "GSEA results (choosen descriptor: tissue)"
-print "%-40s %6s %6s %6s %7s" % ("LABEL", "NES", "FDR", "SIZE", "MATCHED") 
-for name,resu in sorted(res.items(), key=lambda x: x[1]["fdr"])[:10]: 
-    print "%-40s %6.3f %6.3f %6d %7d" % (name[:30], resu["nes"], resu["fdr"], 
-        resu["size"], resu["matched_size"]) 

File doc/modules/mirnaExamle1.py

-import random
-import obimiRNA
-
-miRNAs = obimiRNA.ids()
-
-print 'miRNA name\tAccession_Number\t\tSequence\t\tPre-forms\n'
-for m in random.sample(miRNAs, 10):
-    accession = obimiRNA.get_info(m).matACC
-    sequence = obimiRNA.get_info(m).matSQ
-    preForms = obimiRNA.get_info(m).pre_forms
-    print '%s\t%s\t\t%s\t\t%s' % (m, accession, sequence, preForms)

File doc/modules/mirnaExamle2.py

-import random
-import obimiRNA
-
-mirnaHSA = obimiRNA.ids('hsa')
-
-for pm in reduce(lambda x,y: x+y, [obimiRNA.get_info(m).pre_forms.split(',') for m in random.sample(mirnaHSA,3)]):                                    
-    pre_miRNA = obimiRNA.get_info(pm,type='pre')
-    print
-    print 'Pre-miRNA name: %s' % pm
-    print 'Accession Number: %s' % pre_miRNA.preACC
-    print 'Accession Number of mature form(s): %s' % pre_miRNA.matACCs
-    print 'PubMed accession number(s): %s' % pre_miRNA.pubIDs
-    print 'Pre-miRNAs clustered together with %s: %s' % (pm, pre_miRNA.clusters)
-    print 'Link to miRBase: %s' % pre_miRNA.web_addr

File doc/modules/mirnaExamle3.py

-import random
-import obiGO
-import obimiRNA
-
-annotations = obiGO.Annotations('hsa',obiGO.Ontology())
-miRNAs = random.sample(obimiRNA.ids('hsa'),10)
-
-print 'miRNA\tNumber of annotations\tGO_IDs\n'
-for mi,goList in obimiRNA.get_GO(miRNAs, annotations, goSwitch=False).items():
-    if goList:
-        print '%s\t%d\t%s' % (mi, len(goList), ','.join(goList[0:4])+'...')

File doc/modules/mirnaExamle4.py

-import random
-import obiGO
-import obimiRNA
-
-annotations = obiGO.Annotations('hsa',obiGO.Ontology())
-
-miRNAs = random.sample(obimiRNA.ids('hsa'),10)
-
-dict_all = obimiRNA.get_GO(miRNAs, annotations, goSwitch=False)
-dict_enr = obimiRNA.get_GO(miRNAs, annotations, enrichment=True, goSwitch=False)
-
-dict_tfidf = obimiRNA.filter_GO(dict_all, annotations, reverse=False)
-
-print '#\tmiRNA name\t# All GO terms\t# Enriched GO terms\t# Filtred GO terms\n'
-for n,m in enumerate(miRNAs):
-    print '%d\t%s\t\t%d\t\t%d\t\t%d' % (n+1,m,len(dict_all[m]),len(dict_enr[m]),len(dict_tfidf[m]))

File doc/modules/mirnaExamle5.py

-import random
-import obimiRNA
-
-miRNAs = random.sample(obimiRNA.ids('hsa'),10)
-
-mirPath_all= obimiRNA.get_pathways(miRNAs,enrichment=False, pathSwitch=False)
-mirPath_enr = obimiRNA.get_pathways(miRNAs,enrichment=True, pathSwitch=False)
-
-print 'miRNA_name\t# of pathways\t# of enriched pathways\n'
-for m in miRNAs:
-    print '%s\t\t%d\t\t%d' % (m,len(mirPath_all[m]),len(mirPath_enr[m]))

File doc/modules/mirnaExample1.py

-import random
-import obimiRNA
-
-miRNAs = obimiRNA.ids()
-
-print 'miRNA name\tAccession_Number\t\tSequence\t\tPre-forms\n'
-for m in random.sample(miRNAs, 10):
-    accession = obimiRNA.get_info(m).matACC
-    sequence = obimiRNA.get_info(m).matSQ
-    preForms = obimiRNA.get_info(m).pre_forms
-    print '%s\t%s\t\t%s\t\t%s' % (m, accession, sequence, preForms)

File doc/modules/mirnaExample2.py

-import random
-import obimiRNA
-
-mirnaHSA = obimiRNA.ids('hsa')
-
-for pm in reduce(lambda x,y: x+y, [obimiRNA.get_info(m).pre_forms.split(',') for m in random.sample(mirnaHSA,3)]):                                    
-    pre_miRNA = obimiRNA.get_info(pm,type='pre')
-    print
-    print 'Pre-miRNA name: %s' % pm
-    print 'Accession Number: %s' % pre_miRNA.preACC
-    print 'Accession Number of mature form(s): %s' % pre_miRNA.matACCs
-    print 'PubMed accession number(s): %s' % pre_miRNA.pubIDs
-    print 'Pre-miRNAs clustered together with %s: %s' % (pm, pre_miRNA.clusters)
-    print 'Link to miRBase: %s' % pre_miRNA.web_addr

File doc/modules/mirnaExample3.py

-import random
-import obiGO
-import obimiRNA
-
-annotations = obiGO.Annotations('hsa',obiGO.Ontology())
-miRNAs = random.sample(obimiRNA.ids('hsa'),10)
-
-print 'miRNA\tNumber of annotations\tGO_IDs\n'
-for mi,goList in obimiRNA.get_GO(miRNAs, annotations, goSwitch=False).items():
-    if goList:
-        print '%s\t%d\t%s' % (mi, len(goList), ','.join(goList[0:4])+'...')

File doc/modules/mirnaExample4.py

-import random
-import obiGO
-import obimiRNA
-
-annotations = obiGO.Annotations('hsa',obiGO.Ontology())
-
-miRNAs = random.sample(obimiRNA.ids('hsa'),10)
-
-dict_all = obimiRNA.get_GO(miRNAs, annotations, goSwitch=False)
-dict_enr = obimiRNA.get_GO(miRNAs, annotations, enrichment=True, goSwitch=False)
-
-dict_tfidf = obimiRNA.filter_GO(dict_all, annotations, reverse=False)
-
-print '#\tmiRNA name\t# All GO terms\t# Enriched GO terms\t# Filtred GO terms\n'
-for n,m in enumerate(miRNAs):
-    print '%d\t%s\t\t%d\t\t%d\t\t%d' % (n+1,m,len(dict_all[m]),len(dict_enr[m]),len(dict_tfidf[m]))

File doc/modules/mirnaExample5.py

-import random
-import obimiRNA
-
-miRNAs = random.sample(obimiRNA.ids('hsa'),10)
-
-mirPath_all= obimiRNA.get_pathways(miRNAs,enrichment=False, pathSwitch=False)
-mirPath_enr = obimiRNA.get_pathways(miRNAs,enrichment=True, pathSwitch=False)
-
-print 'miRNA_name\t# of pathways\t# of enriched pathways\n'
-for m in miRNAs:
-    print '%s\t\t%d\t\t%d' % (m,len(mirPath_all[m]),len(mirPath_enr[m]))

File doc/modules/obiArrayExpress-test.py

-import obiArrayExpress
-from pprint import pprint
-
-# test the gene_atlas_summary
-summary = obiArrayExpress.get_atlas_summary(["Kalrn", "Ptprd", "Mbp", "Cyfip2"], "Mus musculus")
-pprint(summary)
-
-# test query_atlas_simple
-results = obiArrayExpress.query_atlas_simple(genes=["Kalrn", "Ptprd", "Mbp", "Cyfip2"], organism="Mus musculus", regulation="up", condition="brain")
-pprint(results)
-
-# test Atlas Conditions
-gene_cond1 = obiArrayExpress.AtlasConditionGeneProperty("Goterm", "Is", "translation")
-gene_cond2 = obiArrayExpress.AtlasConditionGeneProperty("Disease", "Is", "cancer")
-org_cond = obiArrayExpress.AtlasConditionOrganism("Homo sapiens")
-
-conditions = obiArrayExpress.AtlasConditionList([gene_cond1, gene_cond2, org_cond])
-results = obiArrayExpress.query_atlas(conditions)
-pprint(results)
-
-# test ArrayExpress experiments, files query
-
-results = obiArrayExpress.query_experiments(accession="E-MEXP-31")
-pprint(results)
-
-results = obiArrayExpress.query_experiments(species="Homo sapines", expdesign="dose+response", ef="CellType")
-pprint(results)
-
-results = obiArrayExpress.query_experiments(species="Homo sapiens", gxa=True, assaycount=(1, 5), miamescore=(3, 5))
-pprint(results)
-
-results = obiArrayExpress.query_files(species="Mus musculus", gxa=True, keywords=["lung", "cancer"], miamescore=(3, 5), format="xml")
-print results

File doc/modules/obiAssess.htm

-<html>
-
-<head>
-<title>obiAssess: pathway enrichment for each sample</title>
-<link rel=stylesheet href="../style.css" type="text/css">
-<link rel=stylesheet href="style-print.css" type="text/css" media=print>
-</head>
-
-<body>
-<h1>obiAssess: pathway enrichment for each sample</h1>
-<index name="modules/assess enrichment gsea">
-
-<p>Gene Set Enrichment Analysis (GSEA) is a method which tries to identify groups of genes that are regulated together. It computes pathway enrichments for the whole data set. ASSESS in inspired by GSEA and it computes enrichments for each sample in the data set.</p>
-
-<p>ASSESS takes gene expression with sample phenotypes and computes gene set enrichments for given gene sets. First pathway &quot;models&quot; have to be created with AssessLearner. Afterwards they are used to calculate enrichments for each pair of sample and pathway.</p>
-
-<h2>AssessLearner</h2>
-
-Class is used to build models, that can be used later to determine enrichments scores for each example. Note that domains of input data to <code>AssessLearner</code> and <code>Assess</code> instances must be the same.
-
-<dl class=attributes>
-
-<dt>__call__(self, data, organism, geneSets, minSize=3, maxSize=1000, minPart=0.1, classValues=None, rankingf=None)</dt>
-<dd>Function <code>__call__</code> returns an instance of class <code>Assess</code> which can be given an example and returns its enrichemnt in all pathways. Argument descriptions follow. 
-
-<dl class=arguments>
-
-  <dt>data</dt>
-  <dd>An <A href="ExampleTable.htm"><CODE>ExampleTable</CODE></A> with gene expression data. An example
-  should correspond to a sample with its phenotype (class value). Attributes represent individual genes. Their names 
-  should be meaningful gene aliases.</dd>
-
-  <dt>organism</dt>
-  <dd>Organism code as used in KEGG. Needed for matching gene names in data to those in gene sets. Some
-  examples: <code>hsa</code> for human, <code>mmu</code> for mouse. This is an required argument.</dd> 
-
-  <dt>classValues</dt>
-  <dd>A pair of class values describing phenotypes that are chosen as two distinct phenotypes on which gene correlations
-  are computed. Only examples with one of chosen class values are considered for analysis. If not specified, first
-  two class values in <code>classVar</code> attribute descriptor are used.</dd>
-
-  <dt>geneSets</dt>
-  <dd>A python dictionary of gene sets, where key is a gene set name which points to a list of gene aliases for genes
-  in the gene set. Default: gene sets in your collection directory.</dd>
-
-  <dt>minSize, maxSize</dt>
-  <dd>Minimum and maximum number of genes from gene set also present in the data set for that gene set to be analysed.
-  Defaults: 3 and 1000.</dd>
-
-  <dt>minPart</dt>
-  <dd>Minimum fraction of genes from the gene set also present in the data set for that gene set to be analysed. Default: 0.1.</dd> 
-
-  <dt>rankingf</dt>
-  <dd>Used to specify model type for individual gene sets. See source code for reference. We recommend leaving the parameter blank. In that case, a parametric model from Edelman, 2006 is used.</dd>
-
-</dl>
-
-</dd>
-
-</dl>
-
-<h2>Assess</h2>
-
-<dl class=attributes>
-
-<dt>__init__(**kwargs)</dt>
-<dd>Function <code>__init__</code> is usually called only by <code>AssessLearner</code>. It is used to save built &quot;model&quot; data. Saves all keyword arguments into object's namespace.</dd>
-
-<dt>__call__(example)</dt>
-<dd>Returns enrichments of all gene sets for this example. Enrichments are returned in a dictionary, where keys are gene set and values their enrichments. Note that example's domain must be the same as the domain on which the &quot;model&quot; was built.</dd>
-
-
-</dl>
-
-<h3>Example 1</h3>
-
-This example prints enrichmentes for the first sample in the data set. It uses KEGG as a gene set source.
-
-<p class="header"><a href="assess1.py">assess1.py</a> (uses <a href="http://www.ailab.si/orange/datasets/DLBCL.tab">DLBCL.tab</a>)</p>
-
-<xmp class=code>import orange
-import obiAssess
-import obiGeneSets
-
-gs = obiGeneSets.collections([":kegg:hsa"])
-data = orange.ExampleTable("DLBCL.tab")
-
-asl = obiAssess.AssessLearner()
-ass = asl(data, "hsa", geneSets=gs)
-
-print "Enrichments for the first example (10 pathways)"
-enrichments = ass(data[0])
-for patw, enric in sorted(enrichments.items())[:10]:
-    print patw, enric
-</xmp>
-
-<p>Output:</p>
-
-<xmp class=code>Enrichments for the first example (10 pathways)
-[KEGG] 1- and 2-Methylnaphthalene degradation -0.84674671525
-[KEGG] 3-Chloroacrylic acid degradation -0.587923507915
-[KEGG] ABC transporters - General -0.292198856631
-[KEGG] Acute myeloid leukemia 0.305086037192
-[KEGG] Adherens junction 0.387903973883
-[KEGG] Adipocytokine signaling pathway 0.404448748545
-[KEGG] Alanine and aspartate metabolism 0.400113861834
-[KEGG] Alkaloid biosynthesis I -0.677360944415
-[KEGG] Alkaloid biosynthesis II -0.437492650183
-[KEGG] Allograft rejection 0.491535468415
-</xmp>
-
-<h3>Example 2: transforming data sets</h3>
-
-This example builds a new data set, where attributes are gene sets instead of genes. It prints first 10 attributes for the first example of transformed data set. Note, that the output matches previous example (well, with the exception of floating point discrepancies).
-
-<p class="header"><a href="assess2.py">assess2.py</a> (uses <a href="http://www.ailab.si/orange/datasets/DLBCL.tab">DLBCL.tab</a>)</p>
-
-<xmp class=code>import orange
-import obiAssess
-import obiGeneSets
-
-gs = obiGeneSets.collections([":kegg:hsa"])
-data = orange.ExampleTable("DLBCL.tab")
-
-asl = obiAssess.AssessLearner()
-ass = asl(data, "hsa", geneSets=gs)
-
-def genesetsAsAttributes(data, ass, domain=None):
-    """
-    Construct new data set with gene sets as attributes from data
-    set "data" with assess model "ass".
-    """
-
-    ares = {}
-    for ex in data:
-        cres = ass(ex)
-        for name,val in cres.items():
-            aresl = ares.get(name, [])
-            aresl.append(val)
-            ares[name] = aresl
-
-    ares = sorted(ares.items())
-
-    if not domain: #construct new domain instance if needed
-        domain = orange.Domain([ orange.FloatVariable(name=name) \
-            for name in [ a[0] for a in ares]], data.domain.classVar )
-
-    examples = [ [ b[zap] for a,b in ares ] + \
-        [ data[zap][-1] ]   for zap in range(len(data)) ]
-
-    et = orange.ExampleTable(domain, examples)
-    return et
-
-tdata = genesetsAsAttributes(data, ass)
-
-print "First 10 attributes of the first example in transformed data set"
-for pathw, enric in zip(tdata.domain,tdata[0])[:10]:
-    print pathw.name, enric.value
-</xmp>
-
-<p>Output:</p>
-
-<xmp class=code>First 10 attributes of the first example in transformed data set
-[KEGG] 1- and 2-Methylnaphthalene degradation -0.846746742725
-[KEGG] 3-Chloroacrylic acid degradation -0.587923526764
-[KEGG] ABC transporters - General -0.292198866606
-[KEGG] Acute myeloid leukemia 0.305086046457
-[KEGG] Adherens junction 0.387903988361
-[KEGG] Adipocytokine signaling pathway 0.404448747635
-[KEGG] Alanine and aspartate metabolism 0.400113850832
-[KEGG] Alkaloid biosynthesis I -0.6773609519
-[KEGG] Alkaloid biosynthesis II -0.437492638826
-[KEGG] Allograft rejection 0.491535454988
-</xmp>
-
-<h3>Example 3: testing transformed data set quality</h3>
-
-We measure CA and AUC of transformed data set using cross validation and compare them to the original data set. Care needs to be taken to prevent overfitting: we must not use any knowledge about testing set when creating &quot;ASSESS models&quot; and we have to use the same &quot;ASSESS model&quot; for both learning and testing set. We solve this by saving the model to a global variable.
-
-<p class="header">part of <a href="assess3.py">assess3.py</a> (uses <a href="http://www.ailab.si/orange/datasets/DLBCL.tab">DLBCL.tab</a>)</p>
-
-<xmp class=code>offer = None
-
-def transformLearningS(data):
-    ass = asl(data, "hsa", geneSets=gs)
-    et = genesetsAsAttributes(data, ass)
-
-    global offer
-    offer = (et.domain, ass) #save assess model
-
-    return et
-   
-def transformTestingS(data):
-    global offer
-    if not offer:
-        a = fdfsdsdd #exception
-
-    domain, ass = offer
-    offer = None
-
-    return genesetsAsAttributes(data, ass, domain)
-
-
-import orngBayes, orngTest, orngStat
-learners = [ orngBayes.BayesLearner() ]
-
-resultsOriginal = orngTest.crossValidation(learners, data, folds=10)
-resultsTransformed = orngTest.crossValidation(learners, data, folds=10, 
-    pps = [("L", transformLearningS), ("T", transformTestingS)])
-
-print "Original", "CA:", orngStat.CA(resultsOriginal), "AUC:", orngStat.AUC(resultsOriginal)
-print "Transformed", "CA:", orngStat.CA(resultsTransformed), "AUC:", orngStat.AUC(resultsTransformed)
-</xmp>
-
-<p>Output:</p>
-
-<xmp class=code>Original CA: [0.8214285714285714] AUC: [0.78583333333333338]
-Transformed CA: [0.80714285714285716] AUC: [0.84250000000000003]
-</xmp>
-
-<HR>
-<H2>References</H2>
-
-<p>Edelman E, Porrello A, Guinney J, Balakumaran B, Bild A, Febbo PG, Mukherjee S. Analysis of sample set enrichment scores: assaying the enrichment of sets of genes for individual samples in genome-wide expression profiles. Bioinformatics. 2006 Jul 15; 22(14):e108-16. </p>
-
-</body>
-</html>
-

File doc/modules/obiBioMart-query.py

-from obiBioMart import *
-
-## Printing attribute configurations 
-
-connection = BioMartConnection("http://www.biomart.org/biomart/martservice")
-registry = connection.registry()
-#for schema in registry.virtual_schemas()[:1]:
-#    for database in schema.marts()[:1]:
-#        for dataset in database.datasets()[:2]:
-#            for attrTree in dataset.configuration().attributes():
-#                if not getattr(attrTree, "hidden", "false") == "true":
-#                    print dataset.name, "has attribute", getattr(attrTree, "displayName", "<unknown>")
-                    
-## Printing dataset attributes
-
-database = registry["ensembl"]
-dataset = database["hsapiens_gene_ensembl"]
-
-for attr in dataset.attributes():
-    print attr
-
-for filter in dataset.filters():
-    print filter
-                    
-query = BioMartQuery(connection, dataset="hsapiens_gene_ensembl", attributes=["ensembl_transcript_id", "chromosome_name"], 
-                     filters=[("chromosome_name", ["22"])])
-print query.get_count()
-
-print query.run()
-
-query = BioMartQuery(connection)
-query.set_dataset("hsapiens_gene_ensembl")
-query.add_filter("chromosome_name", ["22"])
-query.add_attribute("ensembl_transcript_id")
-query.add_attribute("chromosome_name")
-query.add_attribute("uniprot_swissprot")
-print query.get_count()
-print query.run()

File doc/modules/obiBioMart.htm

Empty file removed.

File doc/modules/obiChem.htm

-<html>
-<head>
-<link rel=stylesheet href="../style.css" type="text/css">
-</head>
-<body>
-
-<h1>obiChem: A library for searching frequent molecular fragments</h1>
-<index name="modules/molecular fragments">
-<p>obi implements the following classes
-<ul>
-    <li>FragmentMiner   : The main class that does the search</li>
-    <li>Fragment        : Representation of the fragment</li>
-    <li>Fragmenter      : A class that is used to fragment an ExampleTable</li>
-    <li>FragmentBasedLearner    : A learner wrapper class that first runs the molecular fragmentation on the data</li>
-</ul>
-</p>
-
-<h2>FragmentMiner</h2>
-<p>A class for finding frequent molecular fragments</p>
-<p class=section>Attributes</p>
-<dl class=attributes>
-    <dt>active</dt>
-    <dd>list of smiles codes of active molecules</dd>
-    <dt>inactive</dt>
-    <dd>list of smiles codes of inactive molecules</dd>
-    <dt>minSupport</dt>
-    <dd>minimum frequency in the active set of the fragments to search for</dd>
-    <dt>maxSupport</dt>
-    <dd>maximum frequency in the inactive set of the fragments to search for</dd>
-    <dt>addWholeRings</dt>
-    <dd>if True rings will be added as a whole rather then atom by atom</dd>
-    <dt>canonicalPruning</dt>
-    <dd>if True a cache of all cannonical codes of all fragments will be kept to avoid redundant search</dd>
-    <dt>findClosed</dt>
-    <dd>finds only fragments that are not sub-structures of any other fragment with the same support (default: True)</dd>
-</dl>
-<p class=section>Methods</p>
-<dl class=methods>
-    <dt>Search()</dt>
-    <dd>Runs the fragment search algorithm and returns a list of found fragments</dd>
-</dl>
-<h3>Example</h3>
-<XMP class=code>miner = FragmentMiner(active = ["NC(C)C(=O)O", "NC(CS)C(=O)O", "NC(CO)C(=O)O"], inactive = [], minSupport = 0.6)
-for fragment in miner.Search():
-    print fragment.ToSmiles() , "Support: %.3f" %fragment.Support()</XMP>
-
-<h2>Fragment</h2>
-<p>A class representing a molecular fragment</p>
-<p class=section>Methods</p>
-<dl class=methods>
-    <dt>ToOBMol()</dt>
-    <dd>Returns an openbabel.OBMol object representation</dd>
-    <dt>ToSmiles()</dt>
-    <dd>Returns a SMILES code representation</dd>
-    <dt>ToCanonicalSmiles()</dt>
-    <dd>Returns a canonical SMILES code representation</dd>
-    <dt>Support()</dt>
-    <dd>Returns the support of the fragment in the active set</dd>
-    <dt>OcurrencesIn(smiles)</dt>
-    <dd>Returns the number of times a fragment is containd in the molecule represented by the <code>smiles</code> code argument</dd>
-    <dt>ContainedIn(smiles)</dt>
-    <dd>Returns True if the fragment is present in the molecule represented by the <code>smiles</code> code argument</dd>
-</dl>
-
-<h2>Fragmenter</h2>
-<p>An object that is used to fragment an ExampleTable</p>
-<p class=section>Attributes</p>
-<dl class=attributes>
-    <dt>minSupport</dt>
-    <dd>minimum frequency in the active set of the fragments to search for (default: 0.2)</dd>
-    <dt>maxSupport</dt>
-    <dd>maximum frequency in the inactive set of the fragments to search for (default: 0.2)</dd>
-    <dt>findClosed</dt>
-    <dd>finds only fragments that are not sub-structures of any other fragment with the same support (default: True)</dd>
-</dl>
-<p class=section>Methods</p>
-<dl class=methods>
-    <dt>__call__(data, smilesAttr, activeFunc)</dt>
-    <dd>Takes a data-set, and runs the FragmentMiner on it. Returns a new data-set and the fragments.
-        The new data-set contains new attributes that represent the presence of a fragment that was found.
-        <p class>Arguments</p>
-        <dl class=arguments>
-            <dt>data</dt>
-            <dd>the dataset</dd>
-            <dt>smilesAttr</dt>
-            <dd>the attribute in the data that contains the SMILES codes (if none is provided it will try to make a smart guess)</dd>
-            <dt>activeFunc</dt>
-            <dd>a function that takes an example from the data-set and returns True if the example should be
-                    considered as active (if none is provided all examples are considered active)</dd>
-       </dl>
-    </dd>
-</dl>
-<h3>Example</h3>
-<XMP class=code>fragmenter=Fragmenter(minSupport=0.1, maxSupport=0.05)
-data, fragments=fragmenter(data, "SMILES")
-</XMP>
-
-<h2>FragmentBasedLearner</h2>
-<p>A learner wrapper class that first runs the molecular fragmentation on the data.</p>
-<p class=section>Attributes</p>
-<dl class=attributes>
-    <dt>smilesAttr</dt>
-    <dd>Attribute in the data that contains the smiles codes (if none is provided it will try to make a smart guess)</dd>
-    <dt>learner</dt>
-    <dd>learner that will be used to actualy learn on the fragmented data (default: orngSVM.SVMLearner)</dd>
-    <dt>minSupport</dt>
-    <dd>minimum frequency in the active set of the fragments to search for</dd>
-    <dt>maxSupport</dt>
-    <dd>maximum frequency in the inactive set of the fragments to search for</dd>
-    <dt>activeFunc</dt>
-    <dd>a function that takes an example from the learning data-set and returns True if the example should be
-                    considered as active (if none is provided all examples are considered active)</dd>
-    <dt>findClosed</dt>
-    <dd>finds only fragments that are not sub-structures of any other fragment with the same support (default: True)</dd>
-    
-</dl>

File doc/modules/obiGEO.htm

-<html>
-<HEAD>
-<LINK REL=StyleSheet HREF="../style.css" TYPE="text/css">
-<LINK REL=StyleSheet HREF="../style-print.css" TYPE="text/css" MEDIA=print></LINK>
-</HEAD>
-
-<BODY>
-<h1>obiGEO: an interface to NCBI's Gene Expression Omnibus</h1>
-
-<index name="NCBI">
-<index name="Gene Expression Omnibus">
-<index name="microarray data sets">
-
-<p>obiGEO provides an interface
-to <a href="http://www.ncbi.nlm.nih.gov/">NCBI</a>'s 
-<a href="http://www.ncbi.nlm.nih.gov/geo/">Gene Expression Omnibus</a>
-repository. Currently, it only supports
-<a href="http://www.ncbi.nlm.nih.gov/sites/GDSbrowser">GEO
-DataSets</a> information querying and retreival.</p>
-
-<h2>GDSInfo</h2>
-
-<p><INDEX name="classes/GDSInfo (in obiGEO)">GDSInfo is the class that
-    can be used to retreive the infomation about
-    <a href=http://www.ncbi.nlm.nih.gov/sites/GDSbrowser>GEO Data
-    Sets</a>. The class accesses the Orange server file
-    that either resides on the local computer or is
-    automatically retreived from Orange server. Notice that the call
-    of this class does not access any NCBI's servers directly.</p>
-
-<p class=section>Methods</p>
-<dl class=attributes>
-<dt>GDSInfo(force_update=False)</dt>
-<dd><p>Constructor returning the object with GEO DataSets
-  information. If <code>force_update</code> is set
-  to <code>True</code>, the constructor will download GEO DataSets
-  information file (gds_info.pickled) from Orange server, otherwise,
-  it will first check if the local copy exists. The object returned
-  behaves like a dictionary: the keys are GEO DataSets IDs, and the
-  dictionary values for is a dictionary providing various information
-  about the particular data set.</p>
-
-<xmp class=code>>>> import obiGEO
->>> info = obiGEO.GDSInfo()
->>> info.keys()[:5]
->>> ['GDS2526', 'GDS2524', 'GDS2525', 'GDS2522', 'GDS1618']
->>> info['GDS2526']['title']
-'c-MYC depletion effect on carcinoma cell lines'
->>> info['GDS2526']['platform_organism']
-'Homo sapiens'
-</xmp>
-</dd>
-</dl>
-
-<h2>GDS</h2>
-
-<p><INDEX name="classes/GDSInfo (in obiGEO)">GDS is a class that
-    provides methods for retreival of a specific GEO DataSet. The data
-    is provided as Orange's ExampleTable.
-
-<p class=section>Methods</p>
-<dl class=attributes>
-<dt>GDS(gdsname, verbose=False, force_download=False)</dt>
-<dd>Constructor returning the object to be used to retreive GEO
-  DataSet table (samples and gene expressions). <code>gdsname</code>
-  is an NCBI's ID for the data set in the form "GDSn" where "n" is a
-  GDS ID number. Construct checks a local cache directory if the
-  particular data file is loaded locally, else it downloads it from
-  <a href="ftp://ftp.ncbi.nih.gov/pub/geo/DATA/SOFT/GDS/">NCBI's GEO
-  FTP site</a>. The download is forced
-  if <code>force_download=True</code>. The compressed data file
-  resides in the cache directory after the call of the constructor
-  (call to <code>orngServerFiles.localpath("GEO")</code> reveals the
-  path of this directory).</p>
-
-<xmp class=code>>>> import obiGEO
->>> gds = obiGEO.GDS("GDS1676")
->>> print print ", ".join(gds.genes[:10])
-EXO1, BUB1B, LTB4R2, FOXA1, MEN1, LIFR, L1CAM, TRAF3, AKAP1, PIK3CD
->>> gds.info["title"]
-'T cell leukemia cell response to human herpesvirus 6 infection: time course'
->>> print gds
-GDS1676 (Homo sapiens), samples=8, features=2100, genes=667, subsets=8
-</xmp>
-</dd>
-
-<dt>getdata(report_genes=True, transpose=False,
-merge_function=variableMean, sample_type=None,
-remove_unknown=None)</dt>
-<dd><p>The call of this method returns the data from GEO DataSet in
-  Orange format. Micorarray spots reported in the GEO data set can
-  either be merged according to their gene id's
-  (<code>report_genes=True</code>) or can be left as spots. The data
-  matrix can have spots/genes in rows and samples in columns
-  (default, <code>transpose=False</code>) or samples in rows and
-  spots/genes in columns
-  (<code>transpose=True</code>). Argument <code>sample_type</code>
-  defines the type of annotation, or (if <code>transpose=True</code>)
-  the type of class labels to be included in the data set. Namely,
-  with <code>sample_type</code>, the entire annotation of samples will
-  be included either in the class value or in
-  the <code>.attributes</code> field of each data set
-  attributes. Spots with sample profiles that include unknown values
-  are retained by default (<code>remove_unknown=None</code>). They are
-  removed if the proportion of samples with unknown values
-  is above the threshold set by <code>remove_unknown</code>.</p>
-
-<p>The following illustrates how <code>getdata</code> is used to
-  construct a data set with genes in rows and samples in
-  columns. Notice that the annotation about each sample is retained
-  in <code>.attributes</code>. 
-
-<xmp class=code>>>> import obiGEO
->>> gds = obiGEO.GDS("GDS1676") 
->>> data = gds.getdata()
->>> len(data)
-667
->>> data[0]
-[?, ?, -0.803, 0.128, 0.110, -2.000, -1.000, -0.358], {"gene":'EXO1'}
->>> data.domain.attributes[0]
-FloatVariable 'GSM63816'
->>> data.domain.attributes[0].attributes
-Out[191]: {'dose': '20 U/ml IL-2', 'infection': 'acute ', 'time': '1 d'}
-</xmp>
-
-</dd>
-</dl>
-
-<h2>Examples</h2>
-
-<p>The following script prints out some information about a specific data set. It does not download the data set, just uses the (local) GEO data sets information file.</p>
-
-<p class="header"><a href="geo_gds1.py">geo_gds1.py</a></p>
-<xmp class=code>import obiGEO
-import textwrap
-
-gdsinfo = obiGEO.GDSInfo()
-gds = gdsinfo["GDS10"]
-
-print "ID:", gds["dataset_id"]
-print "Features:", gds["feature_count"]
-print "Genes:", gds["gene_count"]
-print "Organism:", gds["platform_organism"]
-print "PubMed ID:", gds["pubmed_id"]
-print "Sample types:"
-for sampletype in set([sinfo["type"] for sinfo in gds["subsets"]]):
-    ss = [sinfo["description"] for sinfo in gds["subsets"] if sinfo["type"]==sampletype]
-    print "  %s (%s)" % (sampletype, ", ".join(ss))
-print
-print "Description:"
-print "\n".join(textwrap.wrap(gds["description"], 70))
-</xmp>
-
-<p>The output of this script is:</p>
-
-<xmp class=code>ID: GDS10
-Features: 39114
-Genes: 20094
-Organism: Mus musculus
-PubMed ID: 11827943
-Sample types:
-  disease state (diabetic, diabetic-resistant, nondiabetic)
-  strain (NOD, Idd3, Idd5, Idd3+Idd5, Idd9, B10.H2g7, B10.H2g7 Idd3)
-  tissue (spleen, thymus)
-
-Description:
-Examination of spleen and thymus of type 1 diabetes nonobese diabetic
-(NOD) mouse, four NOD-derived diabetes-resistant congenic strains and
-two nondiabetic control strains.
-</xmp>
-
-<p>GEO data sets provide a sort of mini ontology for sample labeling. Samples belong to sample subsets, which in turn belong to specific types. Like above GDS10, which has three sample types, of which the subsets for the tissue type are spleen and thymus. If you are into using data sets for supervised data mining, then it would be useful to find out which of the data sets provide enough samples for each label. It is (semantically) convenient to perform classification within sample subsets of the same type. We therefore need a script that go through the entire set of data sets and finds those for which, for a specific type, there are enough samples within each of the subsets. The following script does the work. The function <code>valid</code> is passed the information about the data set and determines which subset types (if any) satisfy the "validity" criteria. The number of requested samples in the subset is by default set to <code>n=40</code>.</p>
-
-<p class="header"><a href="geo_gds5.py">geo_gds5.py</a></p>
-<xmp class=code>import obiGEO
-
-def valid(info, n=40):
-    """Return a set of subset types containing more than n samples in every subset"""
-    invalid = set()
-    subsets = set([sinfo["type"] for sinfo in info["subsets"]])
-    for sampleinfo in info["subsets"]:
-        if len(sampleinfo["sample_id"]) < n:
-            invalid.add(sampleinfo["type"])
-    return subsets.difference(invalid)
-
-def report(stypes, info):
-    """Pretty-print GDS and valid susbset types"""
-    for id, sts in stypes:
-        print id
-        for st in sts:
-            print "  %s:" % st,
-            gds = info[id]
-            print ", ".join(["%s/%d" % (sinfo["description"], len(sinfo["sample_id"])) \
-                             for sinfo in gds["subsets"] if sinfo["type"]==st])
-
-gdsinfo = obiGEO.GDSInfo()
-valid_subset_types = [(id, valid(info)) for id, info in gdsinfo.items() if valid(info)]
-report(valid_subset_types, gdsinfo)
-</xmp>
-
-<p>The requested number of samples, <code>n=40</code>, seems to be a quite a stringent criteria met - at the time of writing of this documentation - by only a few data sets (you may try to lower this threshold):</p>
-
-<xmp class="code">GDS1611
-  genotype/variation: wild type/48, upf1 null mutant/48
-GDS968
-  agent: none/57, UV/57, IR/57
-GDS1490
-  other: non-neural/50, neural/100
-GDS2373
-  gender: male/82, female/48
-GDS1293
-  tissue: raphe magnus/40, somatomotor cortex/41
-GDS2960
-  disease state: control/41, Marfan syndrome/60
-GDS1292
-  tissue: raphe magnus/40, somatomotor cortex/43
-GDS1412
-  protocol: no treatment/47, hormone replacement therapy/42
-</xmp>
-
-<p>Let us now pick one data file from the above (GDS2960) and see if we can predict the disease state. We will use LinearLearner, a fast variant of support vector machines with linear kernel, and within 10-fold cross validation measure AUC, the area under ROC. AUC is the probably for correctly distinguishing between two classes if picking the sample from target (e.g., the disease) and non-target class (e.g., control).</p>
-
-<p class="header"><a href="geo_gds6.py">geo_gds6.py</a></p>
-<xmp class="code">import obiGEO
-import orange
-import orngTest
-import orngStat
-
-gds = obiGEO.GDS("GDS2960")
-data = gds.getdata(sample_type="disease state", transpose=True)
-print "Samples: %d, Genes: %d" % (len(data), len(data.domain.attributes))
-
-learners = [orange.LinearLearner]
-results = orngTest.crossValidation(learners, data, folds=10)
-print "AUC = %.3f" % orngStat.AUC(results)[0]
-</xmp>
-
-<p>The output of this script is:</p>
-
-<xmp class="code">Samples: 101, Genes: 3979
-AUC = 0.985</xmp>
-
-<p>The AUC for this data set is very high, indicating that using this particular gene expression data it is almost trivial to separate the two classes.</p>
-
-
-</body>
-</html>

File doc/modules/obiGO-enrichment.py

-import obiGO
-
-ontology = obiGO.Ontology()
-annotations = obiGO.Annotations("sgd", ontology=ontology)
-
-res = annotations.GetEnrichedTerms(["YGR270W", "YIL075C", "YDL007W"])
-print "Enriched terms:"