def view_collection():
params = request.args
uc_id = params['uc_id']
uc = (collection for collection in g.user_session.user_collections
if collection["id"] == uc_id).next()
traits = uc["members"]
trait_obs = []
json_version = []
for atrait in traits:
name, dataset_name = atrait.split(':')
if dataset_name == "Temp":
group = name.split("_")[2]
dataset = create_dataset(dataset_name,
dataset_type="Temp",
group_name=group)
trait_ob = trait.GeneralTrait(name=name, dataset=dataset)
else:
dataset = create_dataset(dataset_name)
trait_ob = trait.GeneralTrait(name=name, dataset=dataset)
trait_ob = trait.retrieve_trait_info(trait_ob,
dataset,
get_qtl_info=True)
trait_obs.append(trait_ob)
json_version.append(trait.jsonable(trait_ob))
collection_info = dict(trait_obs=trait_obs, uc=uc)
if "json" in params:
return json.dumps(json_version)
else:
return render_template("collections/view.html", **collection_info)
def view_collection():
params = request.args
print("PARAMS in view collection:", params)
if "uc_id" in params:
uc_id = params['uc_id']
uc = model.UserCollection.query.get(uc_id)
traits = json.loads(uc.members)
else:
user_collections = json.loads(Redis.get(user_manager.AnonUser().key))
this_collection = {}
for collection in user_collections:
if collection['id'] == params['collection_id']:
this_collection = collection
break
#this_collection = user_collections[params['collection_id']]
traits = this_collection['members']
print("in view_collection traits are:", traits)
trait_obs = []
json_version = []
for atrait in traits:
name, dataset_name = atrait.split(':')
if dataset_name == "Temp":
group = name.split("_")[2]
dataset = create_dataset(dataset_name, dataset_type = "Temp", group_name = group)
trait_ob = trait.GeneralTrait(name=name, dataset=dataset)
else:
dataset = create_dataset(dataset_name)
trait_ob = trait.GeneralTrait(name=name, dataset=dataset)
trait_ob = trait.retrieve_trait_info(trait_ob, dataset, get_qtl_info=True)
trait_obs.append(trait_ob)
json_version.append(trait.jsonable(trait_ob))
if "uc_id" in params:
collection_info = dict(trait_obs=trait_obs,
uc = uc)
else:
collection_info = dict(trait_obs=trait_obs,
collection_name=this_collection['name'])
if "json" in params:
print("json_version:", json_version)
return json.dumps(json_version)
else:
return render_template("collections/view.html",
**collection_info
)
def gen_search_result(self):
"""
Get the info displayed in the search result table from the set of results computed in
the "search" function
"""
self.trait_list = []
json_trait_list = []
species = webqtlDatabaseFunction.retrieve_species(
self.dataset.group.name)
# result_set represents the results for each search term; a search of
# "shh grin2b" would have two sets of results, one for each term
logger.debug("self.results is:", pf(self.results))
for index, result in enumerate(self.results):
if not result:
continue
#### Excel file needs to be generated ####
#logger.debug("foo locals are:", locals())
trait_id = result[0]
this_trait = trait.GeneralTrait(dataset=self.dataset,
name=trait_id,
get_qtl_info=True,
get_sample_info=False)
self.trait_list.append(this_trait)
json_trait_list.append(
trait.jsonable_table_row(this_trait, self.dataset.name,
index + 1))
self.json_trait_list = json.dumps(json_trait_list)
def view_collection():
params = request.args
if g.user_session.logged_in and "uc_id" in params:
uc_id = params['uc_id']
uc = (collection for collection in g.user_session.user_collections if collection["id"] == uc_id).next()
traits = uc["members"]
else:
user_collections = json.loads(Redis.get(user_manager.AnonUser().key))
this_collection = {}
for collection in user_collections:
if collection['id'] == params['collection_id']:
this_collection = collection
break
traits = this_collection['members']
trait_obs = []
json_version = []
for atrait in traits:
name, dataset_name = atrait.split(':')
if dataset_name == "Temp":
group = name.split("_")[2]
dataset = create_dataset(dataset_name, dataset_type = "Temp", group_name = group)
trait_ob = trait.GeneralTrait(name=name, dataset=dataset)
else:
dataset = create_dataset(dataset_name)
trait_ob = trait.GeneralTrait(name=name, dataset=dataset)
trait_ob = trait.retrieve_trait_info(trait_ob, dataset, get_qtl_info=True)
trait_obs.append(trait_ob)
json_version.append(trait.jsonable(trait_ob))
if "uc_id" in params:
collection_info = dict(trait_obs=trait_obs,
uc = uc)
else:
collection_info = dict(trait_obs=trait_obs,
collection_name=this_collection['name'])
if "json" in params:
return json.dumps(json_version)
else:
return render_template("collections/view.html",
**collection_info
)
def view_collection():
params = request.args
print("PARAMS in view collection:", params)
if "uc_id" in params:
uc_id = params['uc_id']
uc = model.UserCollection.query.get(uc_id)
traits = json.loads(uc.members)
print("traits are:", traits)
else:
traits = AnonCollection().get_traits()
print("in view_collection traits are:", traits)
trait_obs = []
json_version = []
for atrait in traits:
print("atrait is:", atrait)
name, dataset_name = atrait.split(':')
trait_ob = trait.GeneralTrait(name=name, dataset_name=dataset_name)
trait_ob.retrieve_info(get_qtl_info=True)
trait_ob.get_info()
trait_obs.append(trait_ob)
json_version.append(trait_ob.jsonable())
#json_version.append(dict(name=trait_ob.name,
# description=trait_ob.description_display,
# location=trait_ob.location_repr,
# mean=trait_ob.mean,
# lrs_score=trait_ob.LRS_score_repr,
# lrs_location=trait_ob.LRS_location_repr))
# dis=trait_ob.description))
#json_version.append(trait_ob.__dict__th)
print("trait_obs:", trait_obs)
if "uc_id" in params:
collection_info = dict(trait_obs=trait_obs, uc=uc)
else:
collection_info = dict(trait_obs=trait_obs)
if "json" in params:
print("json_version:", json_version)
return json.dumps(json_version)
else:
return render_template("collections/view.html", **collection_info)
def run_analysis(self, requestform):
print("Starting CTL analysis on dataset")
self.trait_db_list = [trait.strip() for trait in requestform['trait_list'].split(',')]
self.trait_db_list = [x for x in self.trait_db_list if x]
print("strategy:", requestform.get("strategy"))
strategy = requestform.get("strategy")
print("nperm:", requestform.get("nperm"))
nperm = int(requestform.get("nperm"))
print("parametric:", requestform.get("parametric"))
parametric = bool(requestform.get("parametric"))
print("significance:", requestform.get("significance"))
significance = float(requestform.get("significance"))
# Get the name of the .geno file belonging to the first phenotype
datasetname = self.trait_db_list[0].split(":")[1]
dataset = data_set.create_dataset(datasetname)
genofilelocation = locate(dataset.group.name + ".geno", "genotype")
parser = genofile_parser.ConvertGenoFile(genofilelocation)
parser.process_csv()
print(dataset.group)
# Create a genotype matrix
individuals = parser.individuals
markers = []
markernames = []
for marker in parser.markers:
markernames.append(marker["name"])
markers.append(marker["genotypes"])
genotypes = list(itertools.chain(*markers))
print(len(genotypes) / len(individuals), "==", len(parser.markers))
rGeno = r_t(ro.r.matrix(r_unlist(genotypes), nrow=len(markernames), ncol=len(individuals), dimnames = r_list(markernames, individuals), byrow=True))
# Create a phenotype matrix
traits = []
for trait in self.trait_db_list:
print("retrieving data for", trait)
if trait != "":
ts = trait.split(':')
gt = TRAIT.GeneralTrait(name = ts[0], dataset_name = ts[1])
gt = TRAIT.retrieve_sample_data(gt, dataset, individuals)
for ind in individuals:
if ind in gt.data.keys():
traits.append(gt.data[ind].value)
else:
traits.append("-999")
rPheno = r_t(ro.r.matrix(r_as_numeric(r_unlist(traits)), nrow=len(self.trait_db_list), ncol=len(individuals), dimnames = r_list(self.trait_db_list, individuals), byrow=True))
print(rPheno)
# Use a data frame to store the objects
rPheno = r_data_frame(rPheno, check_names = False)
rGeno = r_data_frame(rGeno, check_names = False)
# Debug: Print the genotype and phenotype files to disk
#r_write_table(rGeno, "~/outputGN/geno.csv")
#r_write_table(rPheno, "~/outputGN/pheno.csv")
# Perform the CTL scan
res = self.r_CTLscan(rGeno, rPheno, strategy = strategy, nperm = nperm, parametric = parametric, ncores = 6)
# Get significant interactions
significant = self.r_CTLsignificant(res, significance = significance)
# Create an image for output
self.results = {}
self.results['imgurl1'] = webqtlUtil.genRandStr("CTLline_") + ".png"
self.results['imgloc1'] = GENERATED_IMAGE_DIR + self.results['imgurl1']
self.results['ctlresult'] = significant
self.results['requestform'] = requestform # Store the user specified parameters for the output page
# Create the lineplot
r_png(self.results['imgloc1'], width=1000, height=600, type='cairo-png')
self.r_lineplot(res, significance = significance)
r_dev_off()
n = 2 # We start from 2, since R starts from 1 :)
for trait in self.trait_db_list:
# Create the QTL like CTL plots
self.results['imgurl' + str(n)] = webqtlUtil.genRandStr("CTL_") + ".png"
self.results['imgloc' + str(n)] = GENERATED_IMAGE_DIR + self.results['imgurl' + str(n)]
r_png(self.results['imgloc' + str(n)], width=1000, height=600, type='cairo-png')
self.r_plotCTLobject(res, (n-1), significance = significance, main='Phenotype ' + trait)
r_dev_off()
n = n + 1
# Flush any output from R
sys.stdout.flush()
# Create the interactive graph for cytoscape visualization (Nodes and Edges)
print(type(significant))
if not type(significant) == ri.RNULLType:
for x in range(len(significant[0])):
print(significant[0][x], significant[1][x], significant[2][x]) # Debug to console
tsS = significant[0][x].split(':') # Source
tsT = significant[2][x].split(':') # Target
gtS = TRAIT.GeneralTrait(name = tsS[0], dataset_name = tsS[1]) # Retrieve Source info from the DB
gtT = TRAIT.GeneralTrait(name = tsT[0], dataset_name = tsT[1]) # Retrieve Target info from the DB
self.addNode(gtS)
self.addNode(gtT)
self.addEdge(gtS, gtT, significant, x)
significant[0][x] = gtS.symbol + " (" + gtS.name + ")" # Update the trait name for the displayed table
significant[2][x] = gtT.symbol + " (" + gtT.name + ")" # Update the trait name for the displayed table
self.elements = json.dumps(self.nodes_list + self.edges_list)
def gen_search_result(self):
"""
Get the info displayed in the search result table from the set of results computed in
the "search" function
"""
trait_list = []
json_trait_list = []
species = webqtlDatabaseFunction.retrieve_species(
self.dataset.group.name)
# result_set represents the results for each search term; a search of
# "shh grin2b" would have two sets of results, one for each term
logger.debug("self.results is:", pf(self.results))
for index, result in enumerate(self.results):
if not result:
continue
#### Excel file needs to be generated ####
trait_dict = {}
trait_id = result[0]
trait_dict['index'] = index + 1
this_trait = trait.GeneralTrait(dataset=self.dataset,
name=trait_id,
get_qtl_info=True,
get_sample_info=False)
trait_dict['name'] = this_trait.name
if this_trait.dataset.type == "Publish":
trait_dict['display_name'] = this_trait.display_name
else:
trait_dict['display_name'] = this_trait.name
trait_dict['dataset'] = this_trait.dataset.name
trait_dict['hmac'] = hmac.data_hmac('{}:{}'.format(
this_trait.name, this_trait.dataset.name))
if this_trait.dataset.type == "ProbeSet":
trait_dict['symbol'] = this_trait.symbol
trait_dict[
'description'] = this_trait.description_display.decode(
'utf-8', 'replace')
trait_dict['location'] = this_trait.location_repr
trait_dict['mean'] = "N/A"
trait_dict['additive'] = "N/A"
if this_trait.mean != "" and this_trait.mean != None:
trait_dict['mean'] = '%.3f' % this_trait.mean
trait_dict['lrs_score'] = this_trait.LRS_score_repr
trait_dict['lrs_location'] = this_trait.LRS_location_repr
if this_trait.additive != "":
trait_dict['additive'] = '%.3f' % this_trait.additive
elif this_trait.dataset.type == "Geno":
trait_dict['location'] = this_trait.location_repr
elif this_trait.dataset.type == "Publish":
trait_dict['description'] = this_trait.description_display
trait_dict['authors'] = this_trait.authors
trait_dict['pubmed_id'] = "N/A"
if this_trait.pubmed_id:
trait_dict['pubmed_id'] = this_trait.pubmed_id
trait_dict['pubmed_link'] = this_trait.pubmed_link
trait_dict['pubmed_text'] = this_trait.pubmed_text
trait_dict['mean'] = "N/A"
if this_trait.mean != "" and this_trait.mean != None:
trait_dict['mean'] = '%.3f' % this_trait.mean
trait_dict['lrs_score'] = this_trait.LRS_score_repr
trait_dict['lrs_location'] = this_trait.LRS_location_repr
trait_dict['additive'] = "N/A"
if this_trait.additive != "":
trait_dict['additive'] = '%.3f' % this_trait.additive
trait_list.append(trait_dict)
#json_trait_list.append(trait.jsonable_table_row(this_trait, self.dataset.name, index + 1))
self.trait_list = json.dumps(trait_list)