def get_clusters(analysis, data_types):
cluster_json = {}
for data_type in data_types:
analysis_data = get_last_analysis_data(analysis, data_type)
data_df, design_df = get_dataframes(analysis_data, PKS[data_type],
SAMPLE_COL)
if data_type == GENOMICS:
inference = WebOmicsInference(data_df, design_df, data_type)
df = inference.standardize_df(inference.data_df)
elif data_type == PROTEOMICS or data_type == METABOLOMICS:
inference = WebOmicsInference(data_df,
design_df,
data_type,
min_value=5000)
df = inference.standardize_df(inference.data_df, log=True)
if not df.empty:
net = Network()
net.load_df(df)
net.cluster()
data_type_label = {
GENOMICS: 'gene',
PROTEOMICS: 'protein',
METABOLOMICS: 'compound'
}
label = data_type_label[data_type]
json_data = net.export_net_json()
cluster_json[label] = json_data
return cluster_json
def exp_heatmap_json(request):
import pandas as pd
from clustergrammer import Network
columns = [i.name for i in Exp._meta.get_fields()]
#exps = Exp.objects.all().using("expDb").values_list("gene_id", "control_0", "control_1", "control_2", "treated_0", "treated_1", "treated_2")
exps = Exp.objects.all().using("expDb").values()
df = pd.DataFrame(list(exps), columns=columns)
df.index = df.gene_id
df = df.loc[:, df.columns[1:]]
net = Network()
net.load_df(df)
# Z-score normalize the rows
net.normalize(axis='row', norm_type='zscore', keep_orig=True)
# filter for the top 100 columns based on their absolute value sum
net.filter_N_top('col', 100, 'sum')
# cluster using default parameters
net.cluster()
# save visualization JSON to file for use by front end
data = net.export_net_json('viz')
data = json.loads(data)
#print(data)
response = {
'data': data,
}
return JsonResponse(response, content_type='application/json')
def main(buff, inst_filename, mongo_address, viz_id):
import numpy as np
import flask
from bson.objectid import ObjectId
from pymongo import MongoClient
from flask import request
from clustergrammer import Network
import StringIO
client = MongoClient(mongo_address)
db = client.clustergrammer
viz_id = ObjectId(viz_id)
found_viz = db.networks.find_one({'_id': viz_id})
try:
net = Network()
net.load_tsv_to_net(buff)
net.swap_nan_for_zero()
views = ['N_row_sum', 'N_row_var']
net.make_clust(dist_type='cosine', dendro=True, views=views, \
linkage_type='average')
export_dat = {}
export_dat['name'] = inst_filename
export_dat['dat'] = net.export_net_json('dat')
export_dat['source'] = 'user_upload'
dat_id = db.network_data.insert(export_dat)
update_viz = net.viz
update_dat = dat_id
except:
print('\n-----------------------')
print('error in clustering')
print('-----------------------\n')
update_viz = 'error'
update_dat = 'error'
found_viz['viz'] = update_viz
found_viz['dat'] = update_dat
db.networks.update_one({'_id': viz_id}, {'$set': found_viz})
client.close()
from clustergrammer import Network
net = Network()
# load matrix tsv file
net.load_stdin()
# optional filtering and normalization
##########################################
# net.filter_sum('row', threshold=20)
# net.normalize(axis='col', norm_type='zscore', keep_orig=True)
# net.filter_N_top('row', 250, rank_type='sum')
# net.filter_threshold('row', threshold=3.0, num_occur=4)
# net.swap_nan_for_zero()
net.make_clust(dist_type='cos',
views=['N_row_sum', 'N_row_var'],
dendro=True,
sim_mat=False,
filter_sim=0.1,
calc_cat_pval=False)
# output jsons for front-end visualizations
print(net.export_net_json('viz', 'no-indent'))
# fp = StringIO()
# gene_attribute_matrix.to_csv(fp, sep='\t')
gene_attribute_matrix.to_csv('tmp.txt', sep='\t')
# Custergrammer
from clustergrammer import Network
net = Network()
# net.load_tsv_to_net(fp, name) # StringIO
net.load_file('tmp.txt')
net.swap_nan_for_zero()
# Generate
net.make_clust(dist_type='cos',
views=['N_row_sum', 'N_row_var'],
dendro=True,
sim_mat=True,
filter_sim=0.1,
calc_cat_pval=False)
# Insert into database
cur.execute(
'insert into `datasets` (`Name`, `prot_att`, `att_att`, `prot_prot`) values (?, ?, ?, ?)',
(name, net.export_net_json('viz', indent='no-indent'),
net.export_net_json('sim_col', indent='no-indent'),
net.export_net_json('sim_row', indent='no-indent')))
con.commit()
except Exception as e:
print "Couldn't process %s (%s)" % (name, e)
continue
print "Processed %s" % (name)
con.close()
def genNetworkFromMatrix(matr):
net = Network()
# net.load_file('txt/example.txt')
net.load_file_as_string(matr)
net.make_clust(run_clustering=False, dendro=False, views=[])
return net.export_net_json('viz', 'no-indent')
