def add_signature(request):
"""The aim is to retrieve a list of differential expressed genes for certain
criteria (e.g. fold_change, p-value, tissue).
"""
form = SignatureForm(request.POST or None, request.FILES or None)
if request.POST:
if not "file" in request.POST:
file = request.FILES['file']
file.name = file.name.replace('.txt', '')
data = file.read().replace('\r', '').split('\n')
elif "profile" not in request:
msg = "No file or profiles selected. Please provide either a signature "\
"file to upload or select profiles to derive a signature."
messages.add_message(request, messages.ERROR, ugettext(msg))
return redirect('/expressions/signature/add/')
# Inferre descriptive informations from the filename:
if file.name.startswith('name='):
info = dict([item.split('=') for item in file.name.split(';')])
if 'tissue' in info:
tissues = info['tissue'].replace('-', '@').replace(
', ', '@').replace(' and ', '@').split(
'@') # @ is unlikely to be used as filename.
else:
tissues = request.POST.getlist('tissues')
if 'diet' in request.POST and request.POST['diet']:
regimen = Regimen.objects.get(pk=request.POST['diet'])
elif "diet" in info:
regimen = Regimen.objects.get(shortcut__exact=info['diet'])
# Species from form:
try:
species = Species.objects.get(pk=request.POST['species'])
except ValueError as e:
msg = "Species not found in Denigma db. %s. Please select a species." % e
messages.add_message(request, messages.ERROR, ugettext(msg))
return redirect('/expressions/signature/add/')
# Create signature:
signature = Signature(name=request.POST['name'] or info['name'],
diet=regimen,
species=species) #,
signature.save()
# Adding tissues:
for tissue in tissues:
try:
tissue = Tissue.objects.get(
pk=tissue) #if it is selected from form
except:
print "Did not found tissue by pk."
try:
tissue = Tissue.objects.get(
name__iexact=tissue
) # If it is inferred from file name.
except Tissue.DoesNotExist as e:
messages.add_message(
request, messages.ERROR,
ugettext("%s: %s" % (str(e)[:-1], tissue)))
return redirect('/expressions/signature/add/')
signature.tissues.add(tissue)
print "Tissues:", signature.tissues.all()
header = {}
for index, column in enumerate(data[0].split('\t')):
if "DR" in column: column = "exp"
elif "AL" in column: column = "ctr"
header[column.lower().replace('gene symbol', 'symbol')\
.replace('gene_symbol', 'symbol')\
.replace(' ', '_')\
.replace('platform_cloneid', 'seq_id')\
.replace('ensembl_gene', 'seq_id')] = index # WTF is this?
#num_lines = len(data); counter = 0
print len(data[1:])
for line in data[1:]:
#print(line)
#print(header)
try:
#print("Trying")
# For effect size
ctr_values = []
exp_values = []
#counter += 1
if not line: continue
columns = line.split('\t')
if len(columns) < len(header): continue #break #
seq_id = columns[header['seq_id']]
symbol = columns[header['symbol']]
if symbol == "None": symbol = None
ctr = float(columns[header['ctr']])
exp = float(columns[header['exp']])
if "ratio" in header:
ratio = float(columns[header['ratio']])
if ratio < 1:
fold_change = -(1 / ratio)
else:
fold_change = ratio
else:
ratio = float(
columns[header['fold_change']]) # 2**exp/2**ctr
if ratio < 1:
fold_change = -(1 / ratio)
else:
fold_change = ratio
# Calculating effect size:
for k, v in header.items():
if k.startswith('ctr') and k != 'ctr':
ctr_values.append(float(columns[v]))
elif k.startswith('exp') and k != 'exp':
exp_values.append(float(columns[v]))
# if exp_values and exp_values != ctr_values:
# #print exp_values
# es = effect_size(exp_values, ctr_values)
# else:
es = None
# if 'pvalue' in header:
# pvalue = columns[header['p_value']]
# else:
if exp_values != ctr_values:
pvalue = t_two_sample(ctr_values, exp_values)[1]
else:
pvalue = 1
transcript = Transcript(seq_id=seq_id,
symbol=symbol,
ratio=ratio,
fold_change=fold_change,
pvalue=pvalue,
effect_size=es)
transcript.save()
#print(transcript.id, transcript.symbol, transcript.ratio)
expression = Expression.objects.create(signature=signature,
transcript=transcript,
exp=exp,
ctr=ctr,
ratio=ratio,
fold_change=fold_change,
pvalue=pvalue,
effect_size=es)
#print expression
except ValueError as e:
print e, symbol, seq_id, fold_change, pvalue, ctr, exp
#break
#print "Counter=%s; Number of lines:%s" % (counter, num_lines)
#if counter == num_lines:
msg = "Successfully integrated signature: %s" % signature.name
msg_type = messages.SUCCESS
#else:
# msg = "File upload failed."
# msg_type = messages.ERROR
messages.add_message(request, msg_type, ugettext(msg))
redirect('/expressions/signatures/')
ctx = {'form': form, 'action': 'Add'}
return render_to_response('expressions/signature_form.html',
ctx,
context_instance=RequestContext(request))
def create_signatures(request):
"""Generates signatures from profiles."""
# Sort profiles according to tissues
# compare DR vs. AL.
profiles = Profile.objects.all()
#print len(profiles)
signatures = {}
for profile in profiles:
tissues = ' '.join([tissue.name for tissue in profile.tissue.all()])
print tissues, profile.diet.shortcut
if tissues not in signatures:
signatures[tissues] = [None, None]
if profile.diet.shortcut == 'DR':
signatures[tissues][0] = profile
else:
signatures[tissues][1] = profile
print signatures
for tissues, profiles in signatures.items():
print tissues, profiles
signature = Signature(name=tissues,
species=profiles[0].species,
diet=profiles[0].diet)
signature.save()
for tissue in profiles[0].tissue.all():
signature.tissues.add(tissue)
for profile in profiles:
#background = []
profile.transcripts = {}
probes = Probe.objects.filter(profile=profile)
for probe in probes:
if not probe.name.startswith('RANDOM'):
transcript_name = probe.name.split('P')[0]
if transcript_name not in profile.transcripts:
profile.transcripts[transcript_name] = [
probe.expression
]
else:
profile.transcripts[transcript_name].append(
probe.expression)
#else: # For background subtraction.
#background.append(probe.expression)
for transcript_name, exp_expression in profiles[0].transcripts.items():
# If expression too low of e.g. 1/3 of probes, exclude probe.
# RMA (background subtraction, quantile normalization, and median polishing)
# Benjamini p-value
exp = sum(exp_expression) / len(exp_expression)
ctr_expression = profiles[1].transcripts[transcript_name]
ctr = sum(ctr_expression) / len(ctr_expression)
ratio = exp / ctr
if ratio < 1: fold_change = -(1 / ratio)
else: fold_change = ratio
if len(exp_expression) == 1 or len(ctr_expression) == 1:
es = pvalue = None
else:
es = effect_size(exp_expression, ctr_expression)
pvalue = t_two_sample(exp_expression,
ctr_expression)[1] # Calculate p-value.
transcript = Transcript(seq_id=transcript_name,
ratio=ratio,
fold_change=fold_change,
effect_size=es,
pvalue=pvalue)
transcript.save()
expression = Expression.objects.create(signature=signature,
transcript=transcript,
exp=exp,
ctr=ctr,
ratio=ratio,
fold_change=fold_change,
effect_size=es,
pvalue=pvalue)
print('Done')
return redirect('/expressions/signatures/')
def recalculate(self):
self.pvalue = t_two_sample(self.exp, self.ctr)
self.exp = mean(self.exp)
self.ctr = mean(self.ctr)
self.ratio = self.exp / self.ctr
def add_signature(request):
"""The aim is to retrieve a list of differential expressed genes for certain
criteria (e.g. fold_change, p-value, tissue).
"""
form = SignatureForm(request.POST or None, request.FILES or None)
if request.POST:
if not "file" in request.POST:
file = request.FILES['file']
file.name = file.name.replace('.txt', '')
data = file.read().replace('\r', '').split('\n')
elif "profile" not in request:
msg = "No file or profiles selected. Please provide either a signature "\
"file to upload or select profiles to derive a signature."
messages.add_message(request, messages.ERROR, ugettext(msg))
return redirect('/expressions/signature/add/')
# Inferre descriptive informations from the filename:
if file.name.startswith('name='):
info = dict([item.split('=') for item in file.name.split(';')])
if 'tissue' in info:
tissues = info['tissue'].replace('-', '@').replace(', ', '@').replace(' and ', '@').split('@') # @ is unlikely to be used as filename.
else:
tissues = request.POST.getlist('tissues')
if 'diet' in request.POST and request.POST['diet']:
regimen = Regimen.objects.get(pk=request.POST['diet'])
elif "diet" in info:
regimen = Regimen.objects.get(shortcut__exact=info['diet'])
# Species from form:
try:
species = Species.objects.get(pk=request.POST['species'])
except ValueError as e:
msg = "Species not found in Denigma db. %s. Please select a species." % e
messages.add_message(request, messages.ERROR, ugettext(msg))
return redirect('/expressions/signature/add/')
# Create signature:
signature = Signature(name=request.POST['name'] or info['name'], diet=regimen, species=species)#,
signature.save()
# Adding tissues:
for tissue in tissues:
try:
tissue = Tissue.objects.get(pk=tissue) #if it is selected from form
except:
print "Did not found tissue by pk."
try:
tissue = Tissue.objects.get(name__iexact=tissue) # If it is inferred from file name.
except Tissue.DoesNotExist as e:
messages.add_message(request, messages.ERROR, ugettext("%s: %s" % (str(e)[:-1], tissue)))
return redirect('/expressions/signature/add/')
signature.tissues.add(tissue)
print "Tissues:", signature.tissues.all()
header = {}
for index, column in enumerate(data[0].split('\t')):
if "DR" in column: column = "exp"
elif "AL" in column: column = "ctr"
header[column.lower().replace('gene symbol', 'symbol')\
.replace('gene_symbol', 'symbol')\
.replace(' ', '_')\
.replace('platform_cloneid', 'seq_id')\
.replace('ensembl_gene', 'seq_id')] = index # WTF is this?
#num_lines = len(data); counter = 0
print len(data[1:])
for line in data[1:]:
#print(line)
#print(header)
try:
#print("Trying")
# For effect size
ctr_values = []
exp_values = []
#counter += 1
if not line: continue
columns = line.split('\t')
if len(columns) < len(header): continue #break #
seq_id = columns[header['seq_id']]
symbol = columns[header['symbol']]
if symbol == "None": symbol = None
ctr = float(columns[header['ctr']])
exp = float(columns[header['exp']])
if "ratio" in header:
ratio = float(columns[header['ratio']])
if ratio < 1:
fold_change = -(1/ratio)
else:
fold_change = ratio
else:
ratio = float(columns[header['fold_change']]) # 2**exp/2**ctr
if ratio < 1:
fold_change = -(1/ratio)
else:
fold_change = ratio
# Calculating effect size:
for k,v in header.items():
if k.startswith('ctr') and k != 'ctr':
ctr_values.append(float(columns[v]))
elif k.startswith('exp') and k != 'exp':
exp_values.append(float(columns[v]))
# if exp_values and exp_values != ctr_values:
# #print exp_values
# es = effect_size(exp_values, ctr_values)
# else:
es = None
# if 'pvalue' in header:
# pvalue = columns[header['p_value']]
# else:
if exp_values != ctr_values:
pvalue = t_two_sample(ctr_values, exp_values)[1]
else: pvalue = 1
transcript = Transcript(seq_id=seq_id, symbol=symbol, ratio=ratio, fold_change=fold_change, pvalue=pvalue, effect_size=es)
transcript.save()
#print(transcript.id, transcript.symbol, transcript.ratio)
expression = Expression.objects.create(
signature=signature,
transcript=transcript,
exp=exp, ctr=ctr,
ratio=ratio,
fold_change=fold_change,
pvalue=pvalue,
effect_size=es)
#print expression
except ValueError as e:
print e, symbol, seq_id, fold_change, pvalue, ctr, exp
#break
#print "Counter=%s; Number of lines:%s" % (counter, num_lines)
#if counter == num_lines:
msg = "Successfully integrated signature: %s" % signature.name
msg_type = messages.SUCCESS
#else:
# msg = "File upload failed."
# msg_type = messages.ERROR
messages.add_message(request, msg_type, ugettext(msg))
redirect('/expressions/signatures/')
ctx = {'form': form, 'action': 'Add'}
return render_to_response('expressions/signature_form.html', ctx,
context_instance=RequestContext(request))
def create_signatures(request):
"""Generates signatures from profiles."""
# Sort profiles according to tissues
# compare DR vs. AL.
profiles = Profile.objects.all()
#print len(profiles)
signatures = {}
for profile in profiles:
tissues = ' '.join([tissue.name for tissue in profile.tissue.all()])
print tissues, profile.diet.shortcut
if tissues not in signatures:
signatures[tissues] = [None, None]
if profile.diet.shortcut == 'DR':
signatures[tissues][0]= profile
else:
signatures[tissues][1] = profile
print signatures
for tissues, profiles in signatures.items():
print tissues, profiles
signature = Signature(name=tissues, species=profiles[0].species, diet=profiles[0].diet)
signature.save()
for tissue in profiles[0].tissue.all():
signature.tissues.add(tissue)
for profile in profiles:
#background = []
profile.transcripts = {}
probes = Probe.objects.filter(profile=profile)
for probe in probes:
if not probe.name.startswith('RANDOM'):
transcript_name = probe.name.split('P')[0]
if transcript_name not in profile.transcripts:
profile.transcripts[transcript_name] = [probe.expression]
else:
profile.transcripts[transcript_name].append(probe.expression)
#else: # For background subtraction.
#background.append(probe.expression)
for transcript_name, exp_expression in profiles[0].transcripts.items():
# If expression too low of e.g. 1/3 of probes, exclude probe.
# RMA (background subtraction, quantile normalization, and median polishing)
# Benjamini p-value
exp = sum(exp_expression)/len(exp_expression)
ctr_expression = profiles[1].transcripts[transcript_name]
ctr = sum(ctr_expression)/len(ctr_expression)
ratio = exp/ctr
if ratio < 1: fold_change = -(1/ratio)
else: fold_change = ratio
if len(exp_expression) == 1 or len(ctr_expression) == 1:
es = pvalue = None
else:
es = effect_size(exp_expression, ctr_expression)
pvalue = t_two_sample(exp_expression, ctr_expression)[1] # Calculate p-value.
transcript = Transcript(seq_id=transcript_name,
ratio=ratio,
fold_change=fold_change,
effect_size=es,
pvalue=pvalue)
transcript.save()
expression = Expression.objects.create(signature=signature, transcript=transcript,
exp=exp, ctr=ctr, ratio=ratio, fold_change=fold_change,
effect_size=es, pvalue=pvalue)
print('Done')
return redirect('/expressions/signatures/')
def recalculate(self):
self.pvalue = t_two_sample(self.exp, self.ctr)
self.exp = mean(self.exp)
self.ctr = mean(self.ctr)
self.ratio = self.exp/self.ctr