def enrichment_analysis(request):
start = time.time()
form = EnrichmentAnalysisSearchForm(request.POST or None)
template_params = {'ea_form': form}
template_params.update(GLOBAL_TEMPLATE_PARAMS)
if not form.is_valid():
return render_to_response("search.html", template_params,
context_instance=RequestContext(request))
print form.cleaned_data
raw_user_list = form.cleaned_data['gene_list']
user_list = set(s.strip(',') for s in raw_user_list.split())
if len(user_list) == 0:
return render_to_response("search.html", template_params,
context_instance=RequestContext(request))
user_list = set(_search_genes(user_list))
print "User list is %s" % user_list
all_genes = Gene.objects.all()
results = []
expts, count, row_index = _search(form)
for tf in set(TFS.itervalues()):
print tf
expts_for_loop = copy.deepcopy(expts)
expts_for_loop = expts_for_loop.filter(transcription_factor=tf)
targeted = set(expt.gene for expt in _direct_search(expts_for_loop))
if len(targeted) == 0:
print "Setting enrichment to 1.0 for %s" % tf
enrichment = 1.0
else:
overlap = user_list.intersection(targeted)
if len(overlap) == 0:
print "Setting enrichment to 1.0 for %s" % tf
enrichment = 1.0
else:
enrichment = stats.hypergeom.sf(len(overlap) - 1,
all_genes.count(),
len(user_list),
len(targeted))
results.append({'tf': tf, 'enrichment': enrichment})
results = sorted(results, key=lambda tf: tf['tf'])
print time.time() - start
return HttpResponse(json.dumps(results))
def validate_transcription_factor(value, line):
canonical_value = TFS.get(value.translate(None, '-_. ').lower())
if not canonical_value:
raise DBImportError("Error on line %d: Transcription factor %s"
" is not valid." % (line, value))
return canonical_value
