def justConvertFilenames(species, outputdir):
import gene_associations
gene_to_symbol = gene_associations.getGeneToUid(species, ("hide", "Ensembl-Symbol"))
import OBO_import
symbol_to_gene = OBO_import.swapKeyValues(gene_to_symbol)
for filename in os.listdir(outputdir):
if ".pdf" in filename or ".png" in filename:
fn = string.replace(filename, ".pdf", "")
fn = string.replace(fn, ".png", "")
newname = string.split(fn, "__")
if newname[0] in gene_to_symbol:
new_filename = str(filename)
if "__" in filename:
new_filename = string.split(filename, "__")[1]
elif "\\" in filename:
new_filename = string.split(filename, "\\")[1]
elif "/" in filename:
new_filename = string.split(filename, "/")[1]
nnname = gene_to_symbol[newname[0]][0] + "-SashimiPlot_" + new_filename
try:
os.rename(os.path.join(outputdir, filename), os.path.join(outputdir, nnname))
except Exception:
pass
else:
continue
def justConvertFilenames(species, outputdir):
import gene_associations
gene_to_symbol = gene_associations.getGeneToUid(species,
('hide', 'Ensembl-Symbol'))
import OBO_import
symbol_to_gene = OBO_import.swapKeyValues(gene_to_symbol)
for filename in os.listdir(outputdir):
if '.pdf' in filename or '.png' in filename:
fn = string.replace(filename, '.pdf', '')
fn = string.replace(fn, '.png', '')
newname = string.split(fn, '__')
if newname[0] in gene_to_symbol:
new_filename = str(filename)
if '__' in filename:
new_filename = string.split(filename, '__')[1]
elif '\\' in filename:
new_filename = string.split(filename, '\\')[1]
elif '/' in filename:
new_filename = string.split(filename, '/')[1]
nnname = gene_to_symbol[
newname[0]][0] + '-SashimiPlot_' + new_filename
try:
os.rename(os.path.join(outputdir, filename),
os.path.join(outputdir, nnname))
except Exception:
pass
else:
continue
def filterRows(input_file,output_file,filterDB=None,logData=False):
orderlst={}
counter=[]
export_object = open(output_file,'w')
firstLine = True
Flag=0;
species="Hs"
import OBO_import; import ExpressionBuilder
gene_to_symbol_db = ExpressionBuilder.importGeneAnnotations(species)
symbol_to_gene = OBO_import.swapKeyValues(gene_to_symbol_db)
for line in open(input_file,'rU').xreadlines():
flag1 = 0
data = cleanUpLine(line)
values = string.split(data,'\t')
if firstLine:
firstLine = False
if Flag==0:
export_object.write(line)
else:
try: symbolID = gene_to_symbol_db[values[0]][0]
except Exception: symbolID = values[0]
if symbolID in filterDB:
counter=[index for index, value in enumerate(filterDB) if value == symbolID]
for it in range(0,len(counter)):
orderlst[counter[it]]=line
try:
for i in range(0,len(orderlst)):
export_object.write(orderlst[i])
except Exception:
print i,filterDB[i]
export_object.close()
print 'Filtered rows printed to:',output_file
def sashmi_plot_list(bamdir,eventsToVisualizeFilename,PSIFilename,events=None):
import gene_associations
gene_to_symbol = gene_associations.getGeneToUid(species,('hide','Ensembl-Symbol'))
import OBO_import; symbol_to_gene = OBO_import.swapKeyValues(gene_to_symbol)
if events==None:
splicing_events,expandedSearch = importSplicingEventsToVisualize(eventsToVisualizeFilename)
else:
### Replace any ":" from the input events
#for i in range(len(events)): events[i] = string.replace(events[i],':','__')
expandedSearch = True
for i in range(len(events)):
gene = string.split(events[i],'__')[0]
if gene in gene_to_symbol:
symbol = gene_to_symbol[gene][0]
elif 'ENS' not in gene or 'G0000' in gene:
if gene in symbol_to_gene:
ensID = symbol_to_gene[gene][0]
symbol = gene
events[i] = ensID ### translate this ID to an Ensembl gene ID for propper SashimiPlot lookup
splicing_events = events ### optionally get from supplied variable
if len(splicing_events)==0:
print eventsToVisualizeFilename
forceNoCompatibleEventsInFile
print 'Exporting plots',
### Determine Groups for Coloring
groups_file = 'None'
dir_list = unique.read_directory(root_dir+'/ExpressionInput')
for file in dir_list:
if 'groups.' in file:
groups_file = root_dir+'/ExpressionInput/'+file
if groups_file != None:
try:
import ExpressionBuilder
sample_group_db = ExpressionBuilder.simplerGroupImport(groups_file)
groups=[]
for sample in sample_group_db:
if sample_group_db[sample] not in groups:
groups.append(sample_group_db[sample]) ### create an ordered list of unique group
except Exception:
groups = ['None']
#print traceback.format_exc()
pass
processed_events = formatAndSubmitSplicingEventsToSashimiPlot(PSIFilename, bamdir, splicing_events, sample_group_db, groups, False)
mopup_events = getMopUpEvents(splicing_events, processed_events)
### Do the same for supplied gene queries or junctions that didn't map above using the gene expression values as a guide
#print len(splicing_events),len(processed_events),len(mopup_events)
processed_events = formatAndSubmitSplicingEventsToSashimiPlot(steady_state_exp_file,bamdir,mopup_events,sample_group_db,groups,expandedSearch)
if len(processed_events)>0:
mopup_events = getMopUpEvents(mopup_events, processed_events)
processed_events = formatAndSubmitSplicingEventsToSashimiPlot(PSIFilename, bamdir, mopup_events, sample_group_db, groups, True)
return gene_to_symbol
def importDataSimple(filename,input_type,MOD=None,Species=None):
id_db={}
fn = filepath(filename)
x=0
for line in open(fn,'rU').xreadlines():
data = cleanUpLine(line)
t = string.split(data,'\t')
if data[0]=='#' and x==0: x=0
elif x==0:
column_headers = t[1:]
if input_type != 'LineageProfiler':
try: column_headers = t[2] ### exclude the ID, system code and p-value column headers
except Exception: column_headers = 'NA'
x=1
else:
if x==1 and input_type != 'LineageProfiler':
### get system conversions
system_code = t[1]
import GO_Elite
import OBO_import
system_codes,source_types,mod_types = GO_Elite.getSourceData()
source_data = system_codes[system_code]
try:
Mod=mod ### global established in upstream functions
speciescode = species_code
except Exception:
Mod=MOD
speciescode = Species
if source_data == Mod:
source_is_mod = True
else:
source_is_mod = False
mod_source = Mod+'-'+source_data+'.txt'
gene_to_source_id = gene_associations.getGeneToUid(speciescode,('hide',mod_source))
source_to_gene = OBO_import.swapKeyValues(gene_to_source_id)
if input_type != 'LineageProfiler':
if source_is_mod == True:
try: id_db[t[0]] = float(t[2])
except Exception: id_db[t[0]] = 'NA'
elif t[0] in source_to_gene:
mod_ids = source_to_gene[t[0]]
for mod_id in mod_ids:
try: value = t[2]
except Exception: value = 'NA'
if value == '+': value = 1
elif value == '-': value = -1
try: id_db[mod_id] = float(value) ### If multiple Ensembl IDs in dataset, only record the last associated fold change
except Exception: id_db[mod_id] = 'NA'
break
else:
id_db[t[0]]= map(float,t[1:]) ### Applies to LineageProfiler
x+=1
#print len(id_db),column_headers
return id_db,column_headers
def importDataSimple(filename, input_type):
id_db = {}
fn = filepath(filename)
x = 0
for line in open(fn, 'rU').xreadlines():
data = cleanUpLine(line)
t = string.split(data, '\t')
if data[0] == '#': x = 0
elif x == 0:
column_headers = t[1:]
if input_type != 'LineageProfiler':
column_headers = t[
2] ### exclude the ID, system code and p-value column headers
x = 1
else:
if x == 1 and input_type != 'LineageProfiler':
### get system conversions
system_code = t[1]
import GO_Elite
import OBO_import
system_codes, source_types, mod_types = GO_Elite.getSourceData(
)
source_data = system_codes[system_code]
if source_data == mod:
source_is_mod = True
else:
source_is_mod = False
mod_source = mod + '-' + source_data + '.txt'
gene_to_source_id = gene_associations.getGeneToUid(
species_code, ('hide', mod_source))
source_to_gene = OBO_import.swapKeyValues(
gene_to_source_id)
if input_type != 'LineageProfiler':
if source_is_mod == True:
id_db[t[0]] = float(t[2])
elif t[0] in source_to_gene:
mod_ids = source_to_gene[t[0]]
for mod_id in mod_ids:
id_db[mod_id] = float(
t[2]
) ### If multiple Ensembl IDs in dataset, only record the last associated fold chagne
else:
id_db[t[0]] = map(float, t[1:]) ### Applies to LineageProfiler
x += 1
return id_db, column_headers
def exportSymbolRelationships(pathway_to_symbol,selected_species,pathway_type,type):
if selected_species != None: ### Restrict to selected species only
current_species_dirs=selected_species
else:
current_species_dirs = unique.read_directory('/'+database_dir)
for species in current_species_dirs:
if '.' not in species:
ens_dir = database_dir+'/'+species+'/gene-'+type+'/Ensembl-'+pathway_type+'.txt'
ens_data = export.ExportFile(ens_dir)
if 'mapp' in type: ens_data.write('GeneID\tSystem\tGeneSet\n')
else: ens_data.write('GeneID\tGeneSet\n')
try: ens_to_entrez = gene_associations.getGeneToUid(species,('hide','Ensembl-EntrezGene'))
except Exception: ens_to_entrez ={}
if len(ens_to_entrez)>0:
entrez_dir = database_dir+'/'+species+'/gene-'+type+'/EntrezGene-'+pathway_type+'.txt'
entrez_data = export.ExportFile(entrez_dir)
if 'mapp' in type: entrez_data.write('GeneID\tSystem\tGeneSet\n')
else: entrez_data.write('GeneID\tGeneSet\n')
#print 'Exporting '+pathway_type+' databases for:',species
try: gene_to_source_id = gene_associations.getGeneToUid(species,('hide','Ensembl-Symbol'))
except Exception: gene_to_source_id={}
source_to_gene = OBO_import.swapKeyValues(gene_to_source_id)
source_to_gene = lowerSymbolDB(source_to_gene)
for pathway in pathway_to_symbol:
for symbol in pathway_to_symbol[pathway]:
try:
genes = source_to_gene[symbol]
for gene in genes:
if 'mapp' in type: ens_data.write(gene+'\tEn\t'+pathway+'\n')
else: ens_data.write(gene+'\t'+pathway+'\n')
if gene in ens_to_entrez:
for entrez in ens_to_entrez[gene]:
if 'mapp' in type: entrez_data.write(entrez+'\tL\t'+pathway+'\n')
else: entrez_data.write(entrez+'\t'+pathway+'\n')
except Exception: null=[]
ens_data.close()
try: entrez_data.close()
except Exception: null=[]
def justConvertFilenames(species,outputdir):
import gene_associations
gene_to_symbol = gene_associations.getGeneToUid(species,('hide','Ensembl-Symbol'))
import OBO_import; symbol_to_gene = OBO_import.swapKeyValues(gene_to_symbol)
for filename in os.listdir(outputdir):
if '.pdf' in filename or '.png' in filename:
fn = string.replace(filename,'.pdf','')
fn = string.replace(fn,'.png','')
newname=string.split(fn,'__')
if newname[0] in gene_to_symbol:
new_filename = str(filename)
if '__' in filename:
new_filename = string.split(filename,'__')[1]
elif '\\' in filename:
new_filename = string.split(filename,'\\')[1]
elif '/' in filename:
new_filename = string.split(filename,'/')[1]
nnname=gene_to_symbol[newname[0]][0]+'-SashimiPlot_'+new_filename
try: os.rename(os.path.join(outputdir, filename), os.path.join(outputdir,nnname))
except Exception: pass
else:
continue
def sashmi_plot_list(bamdir,
eventsToVisualizeFilename,
PSIFilename,
events=None):
import gene_associations
gene_to_symbol = gene_associations.getGeneToUid(species,
('hide', 'Ensembl-Symbol'))
import OBO_import
symbol_to_gene = OBO_import.swapKeyValues(gene_to_symbol)
if events == None:
splicing_events, expandedSearch = importSplicingEventsToVisualize(
eventsToVisualizeFilename)
else:
### Replace any ":" from the input events
#for i in range(len(events)): events[i] = string.replace(events[i],':','__')
expandedSearch = True
for i in range(len(events)):
gene = string.split(events[i], '__')[0]
if gene in gene_to_symbol:
symbol = gene_to_symbol[gene][0]
elif 'ENS' not in gene or 'G0000' in gene:
if gene in symbol_to_gene:
ensID = symbol_to_gene[gene][0]
symbol = gene
events[
i] = ensID ### translate this ID to an Ensembl gene ID for propper SashimiPlot lookup
splicing_events = events ### optionally get from supplied variable
if len(splicing_events) == 0:
print eventsToVisualizeFilename
forceNoCompatibleEventsInFile
print 'Exporting plots',
### Determine Groups for Coloring
groups_file = 'None'
dir_list = unique.read_directory(root_dir + '/ExpressionInput')
for file in dir_list:
if 'groups.' in file:
groups_file = root_dir + '/ExpressionInput/' + file
if groups_file != None:
try:
import ExpressionBuilder
sample_group_db = ExpressionBuilder.simplerGroupImport(groups_file)
groups = []
for sample in sample_group_db:
if sample_group_db[sample] not in groups:
groups.append(sample_group_db[sample]
) ### create an ordered list of unique group
except Exception:
groups = ['None']
#print traceback.format_exc()
pass
processed_events = formatAndSubmitSplicingEventsToSashimiPlot(
PSIFilename, bamdir, splicing_events, sample_group_db, groups, False)
mopup_events = getMopUpEvents(splicing_events, processed_events)
### Do the same for supplied gene queries or junctions that didn't map above using the gene expression values as a guide
#print len(splicing_events),len(processed_events),len(mopup_events)
processed_events = formatAndSubmitSplicingEventsToSashimiPlot(
steady_state_exp_file, bamdir, mopup_events, sample_group_db, groups,
expandedSearch)
if len(processed_events) > 0:
mopup_events = getMopUpEvents(mopup_events, processed_events)
processed_events = formatAndSubmitSplicingEventsToSashimiPlot(
PSIFilename, bamdir, mopup_events, sample_group_db, groups, True)
return gene_to_symbol
def reformatPolyAdenylationCoordinates(species,force):
""" PolyA annotations are currently only available from UCSC for human, but flat file
annotations from 2003-2006 are available for multiple species. Convert these to BED format"""
version={}
version['Rn'] = '2003(rn3)'
version['Dr'] = '2003(zv4)'
version['Gg'] = '2004(galGal2)'
version['Hs'] = '2006(hg8)'
version['Mm'] = '2004(mm5)'
print 'Exporting polyADB_2 coordinates as BED for',species
### Obtain the necessary database files
url = 'http://altanalyze.org/archiveDBs/all/polyAsite.txt'
output_dir = 'AltDatabase/ucsc/'+species + '/'
if force == 'yes':
filename, status = update.download(url,output_dir,'')
else: filename = output_dir+'polyAsite.txt'
### Import the refseq to Ensembl information
import gene_associations; import OBO_import; import EnsemblImport; import export
try:
ens_unigene = gene_associations.getGeneToUid(species,'Ensembl-UniGene')
print len(ens_unigene),'Ensembl-UniGene entries imported'
external_ensembl = OBO_import.swapKeyValues(ens_unigene); use_entrez='no'
except Exception:
ens_entrez = gene_associations.getGeneToUid(species,'Ensembl-EntrezGene')
print len(ens_entrez),'Ensembl-EntrezGene entries imported'
external_ensembl = OBO_import.swapKeyValues(ens_entrez); use_entrez='yes'
gene_location_db = EnsemblImport.getEnsemblGeneLocations(species,'RNASeq','key_by_array')
export_bedfile = output_dir+species+'_polyADB_2_predictions.bed'
print 'exporting',export_bedfile
export_data = export.ExportFile(export_bedfile)
header = '#'+species+'\t'+'polyADB_2'+'\t'+version[species]+'\n'
export_data.write(header)
fn=filepath(filename); x=0; not_found={}
for line in open(fn,'rU').xreadlines():
data = cleanUpLine(line)
if x==0: x=1
else:
siteid,llid,chr,sitenum,position,supporting_EST,cleavage = string.split(data,'\t')
if chr == 'chrM': chr = 'chrMT' ### MT is the Ensembl convention whereas M is the Affymetrix and UCSC convention
if chr == 'M': chr = 'MT' ### MT is the Ensembl convention whereas M is the Affymetrix and UCSC convention
if species in siteid:
if 'NA' not in chr: chr = 'chr'+chr
strand = '+'; geneid = siteid
pos_start = str(int(position)-1); pos_end = position
if use_entrez=='no':
external_geneid = string.join(string.split(siteid,'.')[:2],'.')
else: external_geneid=llid
if external_geneid in external_ensembl:
ens_geneid = external_ensembl[external_geneid][0]
geneid += '-'+ens_geneid
chr,strand,start,end = gene_location_db[ens_geneid]
else:
not_found[external_geneid]=[]
bed_format = string.join([chr,pos_start,pos_end,geneid,'0','-'],'\t')+'\n' ### We don't know the strand, so write out both strands
export_data.write(bed_format)
bed_format = string.join([chr,pos_start,pos_end,geneid,'0',strand],'\t')+'\n'
export_data.write(bed_format)
export_data.close()
def performGeneSetORA(geneset_dir):
""" Perform over-representation analysis (ORA) on any provided Gene Set """
start_time = time.time()
geneset_type = getResourceType(geneset_dir)
#permuted_z_scores={}; original_mapp_z_score_data={}
if geneset_type == 'Pathways': geneset_type = 'WikiPathways'
### Since MAPP tables can be provided by the user, allow the file to be missing
if geneset_dir == 'UserSuppliedAssociations':
gene_to_mapp = gene_associations.importGeneCustomData(
species_code, system_codes, custom_sets_folder, mod)
geneset_type = geneset_dir
else:
try:
gene_to_mapp = gene_associations.importGeneMAPPData(
species_code, geneset_dir)
except Exception:
gene_to_mapp = {}
mapp_to_gene = OBO_import.swapKeyValues(gene_to_mapp)
if len(gene_to_mapp) == 0:
return 0, None
else:
###Calculate primary z-scores for GeneSets
mapp_to_mod_genes = getGenesInPathway(
input_gene_list, gene_to_mapp) ### For summary reporting
mapp_input_gene_count, Rm, input_linked_mapp = countGenesInPathway(
input_gene_list, gene_to_mapp, 'yes')
mapp_denominator_gene_count, Nm, denom_linked_mapp = countGenesInPathway(
denominator_gene_list, gene_to_mapp, 'yes')
#print Nm,"unique genes, linked to GeneSets and in dataset and", Rm, "unique GeneSets\n linked genes matching criterion."
calculateZScores(mapp_input_gene_count, mapp_denominator_gene_count,
Nm, Rm, mapp_to_gene, 'MAPP')
if use_FET == 'no':
permute_mapp_inputs = []
###Begin GeneSets Permutation Analysis
try:
original_increment = int(permutations / 10)
increment = original_increment
except Exception:
null = None
x = 0
if permutations != 0: print '*',
for permute_input_list in permute_inputs:
if x == increment:
increment += original_increment
print '*',
x += 1
permute_mapp_input_gene_count, null, null = countGenesInPathway(
permute_input_list, gene_to_mapp, 'no')
permute_mapp_inputs.append(permute_mapp_input_gene_count)
calculatePermuteZScores(permute_mapp_inputs,
mapp_denominator_gene_count, Nm, Rm)
calculatePermuteStats(original_mapp_z_score_data)
adjustPermuteStats(original_mapp_z_score_data)
mapp_headers = formatHeaders(gene_file, input_count, input_linked_mapp,
denom_count, denom_linked_mapp, Rm, Nm,
'MAPP', OBO_date)
exportPathwayData(original_mapp_z_score_data, gene_file, mapp_headers,
geneset_type, 'local')
### Export all gene associations (added in version 1.21)
exportPathwayToGeneAssociations(mapp_to_mod_genes, mod, gene_file,
gene_annotations, geneset_type,
'local')
end_time = time.time()
time_diff = formatTime(start_time, end_time)
print "Initial results for %s calculated in %s seconds" % (
geneset_type, time_diff)
permute_mapp_inputs = []
return 1, mapp_to_mod_genes
def reformatPolyAdenylationCoordinates(species, force):
""" PolyA annotations are currently only available from UCSC for human, but flat file
annotations from 2003-2006 are available for multiple species. Convert these to BED format"""
version = {}
version['Rn'] = '2003(rn3)'
version['Dr'] = '2003(zv4)'
version['Gg'] = '2004(galGal2)'
version['Hs'] = '2006(hg8)'
version['Mm'] = '2004(mm5)'
print 'Exporting polyADB_2 coordinates as BED for', species
### Obtain the necessary database files
url = 'http://altanalyze.org/archiveDBs/all/polyAsite.txt'
output_dir = 'AltDatabase/ucsc/' + species + '/'
if force == 'yes':
filename, status = update.download(url, output_dir, '')
else:
filename = output_dir + 'polyAsite.txt'
### Import the refseq to Ensembl information
import gene_associations
import OBO_import
import EnsemblImport
import export
try:
ens_unigene = gene_associations.getGeneToUid(species,
'Ensembl-UniGene')
print len(ens_unigene), 'Ensembl-UniGene entries imported'
external_ensembl = OBO_import.swapKeyValues(ens_unigene)
use_entrez = 'no'
except Exception:
ens_entrez = gene_associations.getGeneToUid(species,
'Ensembl-EntrezGene')
print len(ens_entrez), 'Ensembl-EntrezGene entries imported'
external_ensembl = OBO_import.swapKeyValues(ens_entrez)
use_entrez = 'yes'
gene_location_db = EnsemblImport.getEnsemblGeneLocations(
species, 'RNASeq', 'key_by_array')
export_bedfile = output_dir + species + '_polyADB_2_predictions.bed'
print 'exporting', export_bedfile
export_data = export.ExportFile(export_bedfile)
header = '#' + species + '\t' + 'polyADB_2' + '\t' + version[species] + '\n'
export_data.write(header)
fn = filepath(filename)
x = 0
not_found = {}
for line in open(fn, 'rU').xreadlines():
data = cleanUpLine(line)
if x == 0: x = 1
else:
siteid, llid, chr, sitenum, position, supporting_EST, cleavage = string.split(
data, '\t')
if chr == 'chrM':
chr = 'chrMT' ### MT is the Ensembl convention whereas M is the Affymetrix and UCSC convention
if chr == 'M':
chr = 'MT' ### MT is the Ensembl convention whereas M is the Affymetrix and UCSC convention
if species in siteid:
if 'NA' not in chr: chr = 'chr' + chr
strand = '+'
geneid = siteid
pos_start = str(int(position) - 1)
pos_end = position
if use_entrez == 'no':
external_geneid = string.join(
string.split(siteid, '.')[:2], '.')
else:
external_geneid = llid
if external_geneid in external_ensembl:
ens_geneid = external_ensembl[external_geneid][0]
geneid += '-' + ens_geneid
chr, strand, start, end = gene_location_db[ens_geneid]
else:
not_found[external_geneid] = []
bed_format = string.join(
[chr, pos_start, pos_end, geneid, '0', '-'], '\t'
) + '\n' ### We don't know the strand, so write out both strands
export_data.write(bed_format)
bed_format = string.join(
[chr, pos_start, pos_end, geneid, '0', strand],
'\t') + '\n'
export_data.write(bed_format)
export_data.close()
def performOntologyORA(ontology_dir):
""" Perform over-representation analysis (ORA) on any provided Ontology """
start_time = time.time()
ontology_type = getResourceType(ontology_dir)
######### Import Gene-to-Nested-Ontology #########
gene_to_ontology = gene_associations.importGeneToOntologyData(
species_code, mod, 'nested', ontology_type)
ontology_to_gene = OBO_import.swapKeyValues(gene_to_ontology)
if len(gene_to_ontology) == 0:
return 0, None
else:
######### Calculate primary z-scores for GO terms
ontology_to_mod_genes = getGenesInPathway(
input_gene_list, gene_to_ontology) ### For summary gene reporting
ontology_input_gene_count, Rg, input_linked_ontology = countGenesInPathway(
input_gene_list, gene_to_ontology, 'yes')
ontology_denominator_gene_count, Ng, denom_linked_ontology = countGenesInPathway(
denominator_gene_list, gene_to_ontology, 'yes')
#print Ng,"unique genes, linked to GO and in dataset and", Rg, "unique GO linked genes matching criterion."
calculateZScores(ontology_input_gene_count,
ontology_denominator_gene_count, Ng, Rg,
ontology_to_gene, 'Ontology')
if use_FET == 'no':
###Begining Ontology Permutation Analysis
try:
original_increment = int(permutations / 10)
increment = original_increment
except Exception:
null = None
x = 0
permute_ontology_inputs = []
if permutations != 0: print '*',
for permute_input_list in permute_inputs:
### http://docs.python.org/library/multiprocessing.html
if x == increment:
increment += original_increment
print '*',
x += 1
permute_ontology_input_gene_count, null, null = countGenesInPathway(
permute_input_list, gene_to_ontology, 'no')
permute_input_list = []
permute_ontology_inputs.append(
permute_ontology_input_gene_count)
#if permutations !=0: print 'Gene Ontology finished'
calculatePermuteZScores(permute_ontology_inputs,
ontology_denominator_gene_count, Ng, Rg)
calculatePermuteStats(original_ontology_z_score_data)
adjustPermuteStats(original_ontology_z_score_data)
go_headers = formatHeaders(gene_file, input_count,
input_linked_ontology, denom_count,
denom_linked_ontology, Rg, Ng, 'Ontology',
OBO_date)
exportPathwayData(original_ontology_z_score_data, gene_file,
go_headers, ontology_type, 'Ontology')
### Export all gene associations (added in version 1.21)
exportPathwayToGeneAssociations(ontology_to_mod_genes, mod, gene_file,
gene_annotations, ontology_type,
'Ontology')
end_time = time.time()
time_diff = formatTime(start_time, end_time)
print "Initial results for %s calculated in %s seconds" % (
ontology_type, time_diff)
permute_ontology_inputs = []
return 1, ontology_to_mod_genes
def importGeneExpressionValues(filename,tissue_specific_db,translation_db,useLog=False,previouslyRun=False,species=None):
### Import gene-level expression raw values
fn=filepath(filename); x=0; genes_added={}; gene_expression_db={}
dataset_name = export.findFilename(filename)
max_val=0
print 'importing:',dataset_name
try:
import gene_associations, OBO_import
gene_to_symbol = gene_associations.getGeneToUid(species,('hide','Ensembl-Symbol'))
symbol_to_gene = OBO_import.swapKeyValues(gene_to_symbol)
except Exception: symbol_to_gene={}
for line in open(fn,'rU').xreadlines():
data = cleanUpLine(line)
t = string.split(data,'\t')
if x==0:
if '#' not in data:
for i in t[1:]: sample_headers.append(i)
x=1
else:
gene = t[0]
try: gene = string.split(t[0],'|')[0]
except Exception: pass
#if '-' not in gene and ':E' in gene: print gene;sys.exit()
if analysis_type == 'AltExon':
try: ens_gene,exon = string.split(gene,'-')[:2]
except Exception: exon = gene
gene = exon
if keyed_by == 'translation': ### alternative value is 'primaryID'
"""if gene == 'ENSMUSG00000025915-E19.3':
for i in translation_db: print [i], len(translation_db); break
print gene, [translation_db[gene]];sys.exit()"""
try: gene = translation_db[gene] ### Ensembl annotations
except Exception: pass
try: gene = symbol_to_gene[gene][0] ### If RNASeq is the selected platform and Symbol is the uid
except Exception: pass
if gene in tissue_specific_db:
index,tissue_exp=tissue_specific_db[gene]
try: genes_added[gene]+=1
except Exception: genes_added[gene]=1
proceed=True
try:
exp_vals = t[1:]
if '' in exp_vals:
### If missing values present (PSI values)
exp_vals = ['0.000101' if i=='' else i for i in exp_vals]
useLog = False
exp_vals = map(float, exp_vals)
if platform == 'RNASeq':
if max(exp_vals)>max_val: max_val = max(exp_vals)
#if max(exp_vals)<3: proceed=False
if useLog==False:
exp_vals = map(lambda x: math.log(x+1,2),exp_vals)
if value_type == 'calls': ### Hence, this is a DABG or RNA-Seq expression
exp_vals = produceDetectionCalls(exp_vals,targetPlatform) ### 0 or 1 calls
if proceed:
gene_expression_db[gene] = [index,exp_vals]
except Exception:
print 'Non-numeric values detected:'
x = 5
print t[:x]
while x < t:
t[x:x+5]
x+=5
print 'Formatting error encountered in:',dataset_name; forceError
"""else:
for gene in tissue_specific_db:
if 'Ndufa9:ENSMUSG00000000399:I2.1-E3.1' in gene:
print gene, 'dog';sys.exit()
print gene;kill"""
print len(gene_expression_db), 'matching genes in the dataset and tissue compendium database'
for gene in genes_added:
if genes_added[gene]>1:
del gene_expression_db[gene] ### delete entries that are present in the input set multiple times (not trustworthy)
else: expession_subset.append(gene_expression_db[gene]) ### These contain the rank order and expression
#print len(expession_subset);sys.exit()
expession_subset.sort() ### This order now matches that of
gene_expression_db=[]
if max_val<20 and platform == 'RNASeq' and previouslyRun==False: ### Only allow to happen once
importGeneExpressionValues(filename,tissue_specific_db,translation_db,useLog=True,previouslyRun=True,species=species)
def importGeneExpressionValues(filename,tissue_specific_db,translation_db,useLog=False,previouslyRun=False,species=None):
### Import gene-level expression raw values
fn=filepath(filename); x=0; genes_added={}; gene_expression_db={}
dataset_name = export.findFilename(filename)
max_val=0
print 'importing:',dataset_name
try:
import gene_associations, OBO_import
gene_to_symbol = gene_associations.getGeneToUid(species,('hide','Ensembl-Symbol'))
symbol_to_gene = OBO_import.swapKeyValues(gene_to_symbol)
except Exception: symbol_to_gene={}
for line in open(fn,'rU').xreadlines():
data = cleanUpLine(line)
t = string.split(data,'\t')
if x==0:
if '#' not in data:
for i in t[1:]: sample_headers.append(i)
x=1
else:
gene = t[0]
#if '-' not in gene and ':E' in gene: print gene;sys.exit()
if analysis_type == 'AltExon':
try: ens_gene,exon = string.split(gene,'-')[:2]
except Exception: exon = gene
gene = exon
if keyed_by == 'translation': ### alternative value is 'primaryID'
"""if gene == 'ENSMUSG00000025915-E19.3':
for i in translation_db: print [i], len(translation_db); break
print gene, [translation_db[gene]];sys.exit()"""
try: gene = translation_db[gene] ### Ensembl annotations
except Exception: pass
try: gene = symbol_to_gene[gene][0] ### If RNASeq is the selected platform and Symbol is the uid
except Exception: pass
if gene in tissue_specific_db:
index,tissue_exp=tissue_specific_db[gene]
try: genes_added[gene]+=1
except Exception: genes_added[gene]=1
proceed=True
try:
exp_vals = map(float, t[1:])
if platform == 'RNASeq':
if max(exp_vals)>max_val: max_val = max(exp_vals)
#if max(exp_vals)<3: proceed=False
if useLog==False:
exp_vals = map(lambda x: math.log(x+1,2),exp_vals)
if value_type == 'calls': ### Hence, this is a DABG or RNA-Seq expression
exp_vals = produceDetectionCalls(exp_vals,targetPlatform) ### 0 or 1 calls
if proceed:
gene_expression_db[gene] = [index,exp_vals]
except Exception:
print 'Non-numeric values detected:'
x = 5
print t[:x]
while x < t:
t[x:x+5]
x+=5
print 'Formatting error encountered in:',dataset_name; forceError
print len(gene_expression_db), 'matching genes in the dataset and tissue compendium database'
for gene in genes_added:
if genes_added[gene]>1: del gene_expression_db[gene] ### delete entries that are present in the input set multiple times (not trustworthy)
else: expession_subset.append(gene_expression_db[gene]) ### These contain the rank order and expression
#print len(expession_subset);sys.exit()
expession_subset.sort() ### This order now matches that of
gene_expression_db=[]
if max_val<20 and platform == 'RNASeq' and previouslyRun==False: ### Only allow to happen once
importGeneExpressionValues(filename,tissue_specific_db,translation_db,useLog=True,previouslyRun=True,species=species)
def importDataSimple(filename, input_type, MOD=None, Species=None):
id_db = {}
fn = filepath(filename)
x = 0
for line in open(fn, 'rU').xreadlines():
data = cleanUpLine(line)
t = string.split(data, '\t')
if data[0] == '#' and x == 0: x = 0
elif x == 0:
column_headers = t[1:]
if input_type != 'LineageProfiler':
try:
column_headers = t[
2] ### exclude the ID, system code and p-value column headers
except Exception:
column_headers = 'NA'
x = 1
else:
if x == 1 and input_type != 'LineageProfiler':
### get system conversions
system_code = t[1]
import GO_Elite
import OBO_import
system_codes, source_types, mod_types = GO_Elite.getSourceData(
)
source_data = system_codes[system_code]
try:
Mod = mod ### global established in upstream functions
speciescode = species_code
except Exception:
Mod = MOD
speciescode = Species
if source_data == Mod:
source_is_mod = True
else:
source_is_mod = False
mod_source = Mod + '-' + source_data + '.txt'
gene_to_source_id = gene_associations.getGeneToUid(
speciescode, ('hide', mod_source))
source_to_gene = OBO_import.swapKeyValues(
gene_to_source_id)
if input_type != 'LineageProfiler':
if source_is_mod == True:
try:
id_db[t[0]] = float(t[2])
except Exception:
id_db[t[0]] = 'NA'
elif t[0] in source_to_gene:
mod_ids = source_to_gene[t[0]]
for mod_id in mod_ids:
try:
value = t[2]
except Exception:
value = 'NA'
if value == '+': value = 1
elif value == '-': value = -1
try:
id_db[mod_id] = float(
value
) ### If multiple Ensembl IDs in dataset, only record the last associated fold change
except Exception:
id_db[mod_id] = 'NA'
break
else:
id_db[t[0]] = map(float, t[1:]) ### Applies to LineageProfiler
x += 1
#print len(id_db),column_headers
return id_db, column_headers
def generateMAPPFinderScores(species_title, species_id, source, mod_db,
system_Codes, permute, resources_to_analyze,
file_dirs, parent_root):
global mappfinder_output_dir
global custom_sets_folder
global root
root = parent_root
global mapp_to_mod_genes
global ontology_to_mod_genes
global system_codes
system_codes = system_Codes
criterion_input_folder, criterion_denom_folder, output_dir, custom_sets_folder = file_dirs
previous_denominator_file_dir = ''
ontology_to_mod_genes = {}
mapp_to_mod_genes = {}
global test
test = 'no'
program_type, database_dir = unique.whatProgramIsThis()
if resources_to_analyze == 'Gene Ontology':
resources_to_analyze = 'GeneOntology'
if len(output_dir) == 0: mappfinder_output_dir = 'input/MAPPFinder'
else:
mappfinder_output_dir = output_dir + '/GO-Elite_results/CompleteResults/ORA'
global source_data
source_data = source
global mod
mod = mod_db
global species_code
species_code = species_id
global species_name
species_name = species_title
global gene_to_mapp
global permutations
permutations = permute
global eliminate_redundant_genes
eliminate_redundant_genes = 'yes'
global permuted_z_scores
global ontology_annotations
global original_ontology_z_score_data
global original_mapp_z_score_data
global input_gene_list
global denominator_gene_list
global gene_file
global denom_file_status
global input_count
global denom_count
global gene_annotations
global source_to_gene
global use_FET
if permutations == "FisherExactTest":
use_FET = 'yes' ### Use Fisher's Exact test instead of permutation-based p-values
permutations = 0
else:
use_FET = 'no'
start_time = time.time()
gene_annotations = gene_associations.importGeneData(species_code, mod)
OBO_date = importVersionData('OBO/')
if len(criterion_input_folder) == 0:
import_dir = '/input/GenesToQuery/' + species_code
import_dir_alt = import_dir[1:]
else:
import_dir = criterion_input_folder
import_dir_alt = criterion_input_folder
m = GrabFiles()
m.setdirectory(import_dir)
try:
dir_list = readDirText(
import_dir
) #send a sub_directory to a function to identify all files in a directory
except Exception:
print_out = 'Warning! Input directory location is not a valid folder. Exiting GO-Elite.'
ForceCriticalError(print_out)
try:
denom_dir_list = readDirText(criterion_denom_folder)
except Exception:
print_out = 'Warning! Denominator directory location is not a valid folder. Exiting GO-Elite.'
ForceCriticalError(print_out)
if len(dir_list) == 0:
error_message = 'No files with the extension ".txt" found in the input directory.'
ForceCriticalError(error_message)
if len(denom_dir_list) == 0:
error_message = 'No files with the extension ".txt" found in the denominator directory.'
ForceCriticalError(error_message)
inputs_analyzed = 0
for mappfinder_input in dir_list: #loop through each file in the directory
permuted_z_scores = {}
original_ontology_z_score_data = {}
original_mapp_z_score_data = {}
print 'Performing over-representation analysis (ORA) on', mappfinder_input
gene_file_dir, gene_file = m.searchdirectory(mappfinder_input)
###Import Input gene/source-id lists
input_gene_list, source_data_input, error_message = gene_associations.importUIDsForMAPPFinderQuery(
import_dir_alt + '/' + gene_file, system_codes, 'no')
input_count = len(input_gene_list)
if 'WARNING!!!' in error_message: ### Warn the user about SwissProt issues when importing the denominator
ForceCriticalError(error_message)
if len(criterion_denom_folder) == 0:
denom_folder = '/input/GenesToQuery/' + species_code + '/DenominatorGenes'
else:
denom_folder = criterion_denom_folder
error_warning = "\nThe directory\n" + '[' + denom_folder + ']' + "\nwas not found. Please create the directory\nand place an appropriate denominator file\nor files in it."
denominator_file_dir = identifyGeneFiles(
denom_folder,
gene_file) ###input is in input\Genes, denominator in
try:
denominator_file_dir = identifyGeneFiles(
denom_folder,
gene_file) ###input is in input\Genes, denominator in
denominator_file = string.split(denominator_file_dir, '/')[-1]
print 'Using:', denominator_file, 'for the denominator.'
except Exception:
print_out = "WARNING: No denominator file included in\nthe Denominator directory.\nTo proceed, place all denominator\nIDs in a file in that directory."
ForceCriticalError(print_out)
if denominator_file_dir == previous_denominator_file_dir:
denom_file_status = 'old'
else:
denom_file_status = 'new'
if denom_file_status == 'new':
previous_denominator_file_dir = denominator_file_dir
denominator_gene_list, source_data_denom, error_message = gene_associations.importUIDsForMAPPFinderQuery(
denominator_file_dir, system_codes, 'no')
denom_count = len(denominator_gene_list)
if 'SwissProt' in error_message and 'WARNING!!!' not in error_message:
if len(input_gene_list) == 0:
error_message += '\nNo valid input IDs found. Exiting GO-Elite.'
try:
UI.WarningWindow(
error_message, 'Warning!!! Identifier Error'
) ### Only warn, don't force an exit (if SwissProt full IDs are present)
except Exception:
None
sys.exit()
else:
try:
UI.WarningWindow(
error_message, 'Warning!!! Identifier Error'
) ### Only warn, don't force an exit (if SwissProt full IDs are present)
except Exception:
None
elif len(error_message) > 0:
ForceCriticalError(error_message)
if len(denominator_gene_list) == len(input_gene_list):
print_out = 'Input and Denominator lists have identical counts.\nPlease load a propper denominator set (containing\nthe input list with all assayed gene IDs) before proceeding.'
ForceCriticalError(print_out)
original_denominator_gene_list = []
for id in denominator_gene_list:
original_denominator_gene_list.append(
id
) ###need this to be a valid list not dictionary for permutation analysis
if len(source_data_input) > 0:
source_data = source_data_input ###over-ride source_data if a source was identified from the input file
if source_data != mod:
if denom_file_status == 'new':
mod_source = mod + '-' + source_data + '.txt'
#checkDenominatorMatchesInput(input_gene_list,denominator_gene_list,gene_file) ###This is checked for the source IDs not associated MOD IDs
try:
gene_to_source_id = gene_associations.getGeneToUid(
species_code, mod_source)
print mod_source, 'imported'
except Exception:
try:
if mod == 'EntrezGene': mod = 'Ensembl'
else: mod = 'EntrezGene'
print 'The primary system (MOD) has been switched from', mod_db, 'to', mod, '\n(' + mod_db, 'not supported for the %s ID system).' % source_data
mod_source = mod + '-' + source_data + '.txt'
gene_to_source_id = gene_associations.getGeneToUid(
species_code, mod_source)
except Exception:
print_out = "WARNING: The primary gene ID system '" + mod + "'\ndoes not support relationships with '" + source_data + "'.\nRe-run using a supported primary ID system."
ForceCriticalError(print_out)
source_to_gene = OBO_import.swapKeyValues(gene_to_source_id)
denominator_gene_list = associateInputSourceWithGene(
source_to_gene, denominator_gene_list)
### Introduced the below method in version 1.21 to improve permutation speed (no longer need to search all source IDs)
### Only includes source ID to gene relationships represented in the denominator file (needed for Affymetrix)
source_to_gene = OBO_import.swapKeyValues(
denominator_gene_list)
###Replace input lists with corresponding MOD IDs
input_gene_list = associateInputSourceWithGene(
source_to_gene, input_gene_list)
checkDenominatorMatchesInput(
input_gene_list, denominator_gene_list,
gene_file) ###This is for only the associated MOD IDs
gd = GrabFiles()
gd.setdirectory('/' + database_dir + '/' + species_code + '/gene-mapp')
available_genesets = reorganizeResourceList(gd.getAllFiles(mod))
od = GrabFiles()
od.setdirectory('/' + database_dir + '/' + species_code + '/gene-go')
available_ontologies = reorganizeResourceList(od.getAllFiles(mod))
input_gene_count = len(
input_gene_list
) ###Count number of genes associated with source input IDs
if len(input_gene_list) == 0 or len(denominator_gene_list) == 0:
if len(input_gene_list) == 0:
print_out = 'WARNING!!!! None of the input IDs provided map to genes for ' + mappfinder_input + '. Check to make sure the selected species is correct.'
print_out += '\nSelected species: ' + species_name
print_out += '\nInput ID system: ' + str(source_data_input)
print_out += '\nPrimary ID system (MOD): ' + str(mod)
ForceCriticalError(print_out)
if len(denominator_gene_list) == 0:
print_out = 'WARNING!!!! None of the denominator IDs provided map to genes for ' + denominator_file_dir + '. Check to make sure the selected species is correct.'
print_out += '\nSelected species: ' + species_name
print_out += '\nDenominator ID system: ' + str(source)
print_out += '\nPrimary ID system (MOD):' + str(mod)
ForceCriticalError(print_out)
elif len(available_ontologies) == 0 and len(available_genesets) == 0:
print_out = 'WARNING!!!! No Ontology or GeneSets appear to be available for this species. Please supply and re-analyze.'
ForceCriticalError(print_out)
else:
""" Perform permutation analysis and ORA on available GeneSets or Ontologies"""
inputs_analyzed += 1
global permute_inputs
permute_inputs = []
if permutations != 0 or use_FET == 'no':
buildPermutationDatabase(original_denominator_gene_list,
input_count)
run_status = 0
### Analyzed ontologies
if len(available_ontologies) > 0:
print ' Analyzing input ID list with available ontologies'
for ontology_dir in available_ontologies:
ontology_type = getResourceType(ontology_dir)
permuted_z_scores = {}
original_ontology_z_score_data = {}
#print ontology_type, resources_to_analyze
if resources_to_analyze == ontology_type or resources_to_analyze == 'all':
ontology_annotations = importOntologyAnnotations(
species_code, ontology_type)
if ontology_annotations != None: ### Occurs when the files are named or formatted correctly
status, ontology_to_mod_genes = performOntologyORA(
ontology_dir)
run_status += status
### Analyzed gene-sets
if len(available_genesets) > 0:
print ' Analyzing input ID list with available gene-sets'
for geneset_dir in available_genesets:
geneset_type = getResourceType(geneset_dir)
permuted_z_scores = {}
original_mapp_z_score_data = {}
if resources_to_analyze == geneset_type or resources_to_analyze == 'all':
status, mapp_to_mod_genes = performGeneSetORA(geneset_dir)
run_status += status
if len(custom_sets_folder) > 0:
### Hence - Analyze User Supplied GeneSets
permuted_z_scores = {}
original_mapp_z_score_data = {}
run_status += performGeneSetORA('UserSuppliedAssociations')[0]
permute_inputs = []
permute_mapp_inputs = []
ontology_input_gene_count = []
mapp_input_gene_count = []
if run_status == 0:
### Returns the number of successfully analyzed gene-set databases
program_type, database_dir = unique.whatProgramIsThis()
print_out = "Warning!!! Either the MOD you have selected: " + mod + "\nis missing the appropriate relationshipfiles necessary to run GO-Elite\nor you have selected an invalid resource to analyze. Either replace\nthe missing MOD files in " + database_dir + '/' + species_code + ' sub-directories or\nselect a different MOD at run-time.'
ForceCriticalError(print_out)
end_time = time.time()
time_diff = formatTime(start_time, end_time)
print 'ORA analyses finished in %s seconds' % time_diff
return ontology_to_mod_genes, mapp_to_mod_genes ###Return the MOD genes associated with each GO term and MAPP