def make_viz_from_df(df, filename):
from clustergrammer import Network
net = Network()
net.df_to_dat(df)
net.swap_nan_for_zero()
# zscore first to get the columns distributions to be similar
net.normalize(axis='col', norm_type='zscore', keep_orig=True)
# filter the rows to keep the perts with the largest normalizes values
net.filter_N_top('row', 2000)
num_coluns = net.dat['mat'].shape[1]
if num_coluns < 50:
# views = ['N_row_sum', 'N_row_var']
views = ['N_row_sum']
net.make_clust(dist_type='cos', views=views)
filename = 'json/' + filename.split('/')[1].replace('.gct',
'') + '.json'
net.write_json_to_file('viz', filename)
def make_phos_homepage_viz():
from clustergrammer import Network
net = Network()
filename = 'lung_cellline_3_1_16/lung_cellline_phospho/' + \
'lung_cellline_TMT_phospho_combined_ratios.tsv'
net.load_file(filename)
# quantile normalize to normalize cell lines
net.normalize(axis='col', norm_type='qn')
# only keep most differentially regulated PTMs
net.filter_N_top('row', 250, 'sum')
# take zscore of rows
net.normalize(axis='row', norm_type='zscore', keep_orig=True)
net.swap_nan_for_zero()
# threshold filter PTMs
net.filter_threshold('row', threshold=1.75, num_occur=3)
views = ['N_row_sum', 'N_row_var']
net.make_clust(dist_type='cos',
views=views,
dendro=True,
sim_mat=True,
calc_cat_pval=True)
net.write_json_to_file('viz', 'json/homepage_phos.json', 'indent')
def clust_vect(db, viz_doc, vect_post):
from clustergrammer import Network
try:
net = Network()
net.load_vect_post_to_net(vect_post)
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')
dat_id = upload_dat(db, net)
update_viz = net.viz
update_dat = dat_id
except:
print('error clustering')
update_viz = 'error'
update_dat = 'error'
viz_doc['viz'] = update_viz
viz_doc['dat'] = update_dat
return viz_doc
def make_viz_json(inst_df, name):
from clustergrammer import Network
net = Network()
filename = 'json/'+name
load_df = {}
load_df['mat'] = inst_df
net.df_to_dat(load_df)
net.swap_nan_for_zero()
net.make_clust(views=[])
net.write_json_to_file('viz', filename, 'no-indent')
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}, {'$set': found_viz} )
client.close()
def main(buff, inst_filename, mongo_address, viz_id):
import numpy as np
import flask
from bson.objectid import ObjectId
from pymongo import MongoClient
from flask import request
from clustergrammer import Network
import StringIO
client = MongoClient(mongo_address)
db = client.clustergrammer
viz_id = ObjectId(viz_id)
found_viz = db.networks.find_one({'_id': viz_id})
try:
net = Network()
net.load_tsv_to_net(buff)
net.swap_nan_for_zero()
views = ['N_row_sum', 'N_row_var']
net.make_clust(dist_type='cosine', dendro=True, views=views, \
linkage_type='average')
export_dat = {}
export_dat['name'] = inst_filename
export_dat['dat'] = net.export_net_json('dat')
export_dat['source'] = 'user_upload'
dat_id = db.network_data.insert(export_dat)
update_viz = net.viz
update_dat = dat_id
except:
print('\n-----------------------')
print('error in clustering')
print('-----------------------\n')
update_viz = 'error'
update_dat = 'error'
found_viz['viz'] = update_viz
found_viz['dat'] = update_dat
db.networks.update_one({'_id': viz_id}, {'$set': found_viz})
client.close()
def cluster():
from clustergrammer import Network
net = Network()
vect_post = net.load_json_to_dict('fake_vect_post.json')
net.load_vect_post_to_net(vect_post)
net.swap_nan_for_zero()
# net.N_top_views()
net.make_clust(dist_type='cos',views=['N_row_sum','N_row_var'], dendro=True)
net.write_json_to_file('viz','json/large_vect_post_example.json','indent')
def clustergrammer_load():
# import network class from Network.py
from clustergrammer import Network
net = Network()
net.pandas_load_file('mat_cats.tsv')
net.make_clust(dist_type='cos', views=['N_row_sum', 'N_row_var'])
net.write_json_to_file('viz', 'json/mult_cats.json', 'indent')
print('\n**********************')
print(net.dat['node_info']['row'].keys())
print('\n\n')
def clustergrammer_load():
# import network class from Network.py
from clustergrammer import Network
net = Network()
net.pandas_load_file('mat_cats.tsv')
net.make_clust(dist_type='cos',views=['N_row_sum','N_row_var'])
net.write_json_to_file('viz','json/mult_cats.json','indent')
print('\n**********************')
print(net.dat['node_info']['row'].keys())
print('\n\n')
def build_layout():
df = load_data.load_gsva_compare_cluster('hallmark')
# TODO THIS NEEDS TO BE CLEANED UP!!!!!!
cat_to_true = defaultdict(lambda: [])
for clust in df.index:
if 'PJ030' in clust:
cat_to_true['LGG'].append(clust)
elif 'PJ' in clust:
cat_to_true['GBM'].append(clust)
elif 'LX' in clust:
cat_to_true['LUAD'].append(clust)
elif 'GSE146026' in clust:
cat_to_true['OV'].append(clust)
elif 'GSE72056' in clust:
cat_to_true['SKCM'].append(clust)
elif 'GSE103322' in clust:
cat_to_true['HNSC'].append(clust)
elif 'GSE111672' in clust:
cat_to_true['PAAD'].append(clust)
cats = [{
'title': 'Cancer Type',
'cats': {k: v
for k, v in cat_to_true.items()}
}]
net = Network()
net.load_df(df)
net.add_cats('row', cats)
net.make_clust()
layout = dcc.Tab(label='Cluster Comparison',
children=[
dbc.Container(
fluid=True,
children=[
html.Link(rel='stylesheet',
href='./static/custom.css'),
dash_clustergrammer.DashClustergrammer(
id='cgram-component',
label='',
network_data=net.viz)
])
])
return layout
def cluster():
from clustergrammer import Network
net = Network()
vect_post = net.load_json_to_dict('fake_vect_post.json')
net.load_vect_post_to_net(vect_post)
net.swap_nan_for_zero()
# net.N_top_views()
net.make_clust(dist_type='cos',
views=['N_row_sum', 'N_row_var'],
dendro=True)
net.write_json_to_file('viz', 'json/large_vect_post_example.json',
'indent')
def make_exp_homepage_viz():
from clustergrammer import Network
net = Network()
net.load_file('CCLE_gene_expression/CCLE_NSCLC_all_genes.txt')
# threshold filter expression
net.filter_threshold('row', threshold=3.0, num_occur=4)
views = ['N_row_sum', 'N_row_var']
net.make_clust(dist_type='cos',
views=views,
dendro=True,
sim_mat=True,
calc_cat_pval=False)
net.write_json_to_file('viz', 'json/homepage_exp.json', 'indent')
def main():
import time
start_time = time.time()
import pandas as pd
import StringIO
# import network class from Network.py
from clustergrammer import Network
net = Network()
# load data to dataframe
# net.load_tsv_to_net('txt/example_tsv_network.txt')
# net.load_tsv_to_net('txt/mat_1mb.txt')
# choose file
################
# file_buffer = open('txt/col_categories.txt')
file_buffer = open('txt/example_tsv_network.txt' )
buff = StringIO.StringIO( file_buffer.read() )
net.pandas_load_tsv_to_net(buff)
# filter rows
views = ['filter_row_sum','N_row_sum']
# distance metric
dist_type = 'cosine'
# linkage type
linkage_type = 'average'
net.make_clust(dist_type=dist_type, views=views, calc_col_cats=True,\
linkage_type=linkage_type)
net.write_json_to_file('viz', 'json/mult_view.json', 'no-indent')
elapsed_time = time.time() - start_time
print('\n\n\nelapsed time: '+str(elapsed_time))
def main():
import time
start_time = time.time()
import pandas as pd
import StringIO
# import network class from Network.py
from clustergrammer import Network
net = Network()
# load data to dataframe
# net.load_tsv_to_net('txt/example_tsv_network.txt')
# net.load_tsv_to_net('txt/mat_1mb.txt')
# choose file
################
# file_buffer = open('txt/col_categories.txt')
file_buffer = open('txt/example_tsv_network.txt')
buff = StringIO.StringIO(file_buffer.read())
net.pandas_load_tsv_to_net(buff)
# filter rows
views = ['filter_row_sum', 'N_row_sum']
# distance metric
dist_type = 'cosine'
# linkage type
linkage_type = 'average'
net.make_clust(dist_type=dist_type, views=views, calc_col_cats=True,\
linkage_type=linkage_type)
net.write_json_to_file('viz', 'json/mult_view.json', 'no-indent')
elapsed_time = time.time() - start_time
print('\n\n\nelapsed time: ' + str(elapsed_time))
def make_json_from_tsv(name):
'''
make a clustergrammer json from a tsv file
'''
from clustergrammer import Network
print('\n' + name)
net = Network()
filename = 'txt/'+ name + '.txt'
net.load_file(filename)
df = net.dat_to_df()
net.swap_nan_for_zero()
# zscore first to get the columns distributions to be similar
net.normalize(axis='col', norm_type='zscore', keep_orig=True)
# filter the rows to keep the perts with the largest normalizes values
net.filter_N_top('row', 1000)
num_rows = net.dat['mat'].shape[0]
num_cols = net.dat['mat'].shape[1]
print('num_rows ' + str(num_rows))
print('num_cols ' + str(num_cols))
if num_cols < 50 or num_rows < 1000:
views = ['N_row_sum']
net.make_clust(dist_type='cos', views=views)
export_filename = 'json/' + name + '.json'
net.write_json_to_file('viz', export_filename)
else:
print('did not cluster, too many columns ')
def make_viz_from_df(df, filename):
from clustergrammer import Network
net = Network()
net.df_to_dat(df)
net.swap_nan_for_zero()
# zscore first to get the columns distributions to be similar
net.normalize(axis='col', norm_type='zscore', keep_orig=True)
# filter the rows to keep the perts with the largest normalizes values
net.filter_N_top('row', 2000)
num_coluns = net.dat['mat'].shape[1]
if num_coluns < 50:
# views = ['N_row_sum', 'N_row_var']
views = ['N_row_sum']
net.make_clust(dist_type='cos', views=views)
filename = 'json/' + filename.split('/')[1].replace('.gct','') + '.json'
net.write_json_to_file('viz', filename)
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'))
import time
start_time = time.time()
from clustergrammer import Network
net = Network()
net.load_file('txt/rc_two_cats.txt')
# net.load_file('txt/tmp.txt')
views = ['N_row_sum', 'N_row_var']
net.make_clust(dist_type='cos', views=views, dendro=True, sim_mat=True)
net.write_json_to_file('viz', 'json/mult_view.json')
net.write_json_to_file('sim_row', 'json/mult_view_sim_row.json')
net.write_json_to_file('sim_col', 'json/mult_view_sim_col.json')
elapsed_time = time.time() - start_time
print('\n\nelapsed time')
print(elapsed_time)
# Remove names for clustergrammer
gene_attribute_matrix.index.name = ""
gene_attribute_matrix.columns.name = ""
# Write to file
# 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 clust_from_response(response_list):
from clustergrammer import Network
import scipy
import json
import pandas as pd
import math
from copy import deepcopy
# print('----------------------')
# print('enrichr_clust_from_response')
# print('----------------------')
ini_enr = transfer_to_enr_dict(response_list)
enr = []
scores = {}
score_types = ['combined_score', 'pval', 'zscore']
for score_type in score_types:
scores[score_type] = pd.Series()
for inst_enr in ini_enr:
if inst_enr['combined_score'] > 0:
# make series of enriched terms with scores
for score_type in score_types:
# collect the scores of the enriched terms
if score_type == 'combined_score':
scores[score_type][inst_enr['name']] = inst_enr[score_type]
if score_type == 'pval':
scores[score_type][inst_enr['name']] = -math.log(
inst_enr[score_type])
if score_type == 'zscore':
scores[score_type][
inst_enr['name']] = -inst_enr[score_type]
# keep enrichement values
enr.append(inst_enr)
# sort and normalize the scores
for score_type in score_types:
scores[score_type] = scores[score_type] / scores[score_type].max()
scores[score_type].sort_values(ascending=False)
number_of_enriched_terms = len(scores['combined_score'])
enr_score_types = ['combined_score', 'pval', 'zscore']
if number_of_enriched_terms < 10:
num_dict = {'ten': 10}
elif number_of_enriched_terms < 20:
num_dict = {'ten': 10, 'twenty': 20}
else:
num_dict = {'ten': 10, 'twenty': 20, 'thirty': 30}
# gather lists of top scores
top_terms = {}
for enr_type in enr_score_types:
top_terms[enr_type] = {}
for num_terms in list(num_dict.keys()):
inst_num = num_dict[num_terms]
top_terms[enr_type][num_terms] = scores[enr_type].index.tolist(
)[:inst_num]
# gather the terms that should be kept - they are at the top of the score list
keep_terms = []
for inst_enr_score in top_terms:
for tmp_num in list(num_dict.keys()):
keep_terms.extend(top_terms[inst_enr_score][tmp_num])
keep_terms = list(set(keep_terms))
# keep enriched terms that are at the top 10 based on at least one score
keep_enr = []
for inst_enr in enr:
if inst_enr['name'] in keep_terms:
keep_enr.append(inst_enr)
# fill in full matrix
#######################
# genes
row_node_names = []
# enriched terms
col_node_names = []
# gather information from the list of enriched terms
for inst_enr in keep_enr:
col_node_names.append(inst_enr['name'])
row_node_names.extend(inst_enr['int_genes'])
row_node_names = sorted(list(set(row_node_names)))
net = Network()
net.dat['nodes']['row'] = row_node_names
net.dat['nodes']['col'] = col_node_names
net.dat['mat'] = scipy.zeros([len(row_node_names), len(col_node_names)])
for inst_enr in keep_enr:
inst_term = inst_enr['name']
col_index = col_node_names.index(inst_term)
# use combined score for full matrix - will not be seen in viz
tmp_score = scores['combined_score'][inst_term]
net.dat['node_info']['col']['value'].append(tmp_score)
for inst_gene in inst_enr['int_genes']:
row_index = row_node_names.index(inst_gene)
# save association
net.dat['mat'][row_index, col_index] = 1
# cluster full matrix
#############################
# do not make multiple views
views = ['']
if len(net.dat['nodes']['row']) > 1:
net.make_clust(dist_type='jaccard', views=views, dendro=False)
else:
net.make_clust(dist_type='jaccard',
views=views,
dendro=False,
run_clustering=False)
# get dataframe from full matrix
df = net.dat_to_df()
for score_type in score_types:
for num_terms in num_dict:
inst_df = deepcopy(df)
inst_net = deepcopy(Network())
inst_df['mat'] = inst_df['mat'][top_terms[score_type][num_terms]]
# load back into net
inst_net.df_to_dat(inst_df)
# make views
if len(net.dat['nodes']['row']) > 1:
inst_net.make_clust(dist_type='jaccard',
views=['N_row_sum'],
dendro=False)
else:
inst_net.make_clust(dist_type='jaccard',
views=['N_row_sum'],
dendro=False,
run_clustering=False)
inst_views = inst_net.viz['views']
# add score_type to views
for inst_view in inst_views:
inst_view['N_col_sum'] = num_dict[num_terms]
inst_view['enr_score_type'] = score_type
# add values to col_nodes and order according to rank
for inst_col in inst_view['nodes']['col_nodes']:
inst_col['rank'] = len(
top_terms[score_type]
[num_terms]) - top_terms[score_type][num_terms].index(
inst_col['name'])
inst_name = inst_col['name']
inst_col['value'] = scores[score_type][inst_name]
# add views to main network
net.viz['views'].extend(inst_views)
return net
net.load_file('txt/rc_two_cats.txt')
# net.load_file('txt/example_tsv.txt')
# net.load_file('txt/col_categories.txt')
# net.load_file('txt/mat_cats.tsv')
# net.load_file('txt/mat_1mb.Txt')
# net.load_file('txt/mnist.txt')
# net.load_file('txt/sim_mat_4_cats.txt')
views = ['N_row_sum','N_row_var']
# # filtering rows and cols by sum
# net.filter_sum('row', threshold=20)
# net.filter_sum('col', threshold=30)
# # keep top rows based on sum
# net.filter_N_top('row', 10, 'sum')
net.make_clust(dist_type='cos',views=views , dendro=True,
sim_mat=True, filter_sim=0.1)
# net.produce_view({'N_row_sum':10,'dist':'euclidean'})
net.write_json_to_file('viz', 'json/mult_view.json', 'no-indent')
net.write_json_to_file('sim_row', 'json/mult_view_sim_row.json', 'no-indent')
net.write_json_to_file('sim_col', 'json/mult_view_sim_col.json', 'no-indent')
elapsed_time = time.time() - start_time
print('\n\nelapsed time')
print(elapsed_time)
def clust_from_response(response_list):
from clustergrammer import Network
import scipy
import json
import pandas as pd
import math
from copy import deepcopy
print('----------------------')
print('enrichr_clust_from_response')
print('----------------------')
ini_enr = transfer_to_enr_dict( response_list )
enr = []
scores = {}
score_types = ['combined_score','pval','zscore']
for score_type in score_types:
scores[score_type] = pd.Series()
for inst_enr in ini_enr:
if inst_enr['combined_score'] > 0:
# make series of enriched terms with scores
for score_type in score_types:
# collect the scores of the enriched terms
if score_type == 'combined_score':
scores[score_type][inst_enr['name']] = inst_enr[score_type]
if score_type == 'pval':
scores[score_type][inst_enr['name']] = -math.log(inst_enr[score_type])
if score_type == 'zscore':
scores[score_type][inst_enr['name']] = -inst_enr[score_type]
# keep enrichement values
enr.append(inst_enr)
# sort and normalize the scores
for score_type in score_types:
scores[score_type] = scores[score_type]/scores[score_type].max()
scores[score_type].sort(ascending=False)
number_of_enriched_terms = len(scores['combined_score'])
enr_score_types = ['combined_score','pval','zscore']
if number_of_enriched_terms <10:
num_dict = {'ten':10}
elif number_of_enriched_terms <20:
num_dict = {'ten':10, 'twenty':20}
else:
num_dict = {'ten':10, 'twenty':20, 'thirty':30}
# gather lists of top scores
top_terms = {}
for enr_type in enr_score_types:
top_terms[enr_type] = {}
for num_terms in num_dict.keys():
inst_num = num_dict[num_terms]
top_terms[enr_type][num_terms] = scores[enr_type].index.tolist()[: inst_num]
# gather the terms that should be kept - they are at the top of the score list
keep_terms = []
for inst_enr_score in top_terms:
for tmp_num in num_dict.keys():
keep_terms.extend( top_terms[inst_enr_score][tmp_num] )
keep_terms = list(set(keep_terms))
# keep enriched terms that are at the top 10 based on at least one score
keep_enr = []
for inst_enr in enr:
if inst_enr['name'] in keep_terms:
keep_enr.append(inst_enr)
# fill in full matrix
#######################
# genes
row_node_names = []
# enriched terms
col_node_names = []
# gather information from the list of enriched terms
for inst_enr in keep_enr:
col_node_names.append(inst_enr['name'])
row_node_names.extend(inst_enr['int_genes'])
row_node_names = sorted(list(set(row_node_names)))
net = Network()
net.dat['nodes']['row'] = row_node_names
net.dat['nodes']['col'] = col_node_names
net.dat['mat'] = scipy.zeros([len(row_node_names),len(col_node_names)])
for inst_enr in keep_enr:
inst_term = inst_enr['name']
col_index = col_node_names.index(inst_term)
# use combined score for full matrix - will not be seen in viz
tmp_score = scores['combined_score'][inst_term]
net.dat['node_info']['col']['value'].append(tmp_score)
for inst_gene in inst_enr['int_genes']:
row_index = row_node_names.index(inst_gene)
# save association
net.dat['mat'][row_index, col_index] = 1
# cluster full matrix
#############################
# do not make multiple views
views = ['']
if len(net.dat['nodes']['row']) > 1:
net.make_clust(dist_type='jaccard', views=views, dendro=False)
else:
net.make_clust(dist_type='jaccard', views=views, dendro=False, run_clustering=False)
# get dataframe from full matrix
df = net.dat_to_df()
for score_type in score_types:
for num_terms in num_dict:
inst_df = deepcopy(df)
inst_net = deepcopy(Network())
inst_df['mat'] = inst_df['mat'][top_terms[score_type][num_terms]]
# load back into net
inst_net.df_to_dat(inst_df)
# make views
if len(net.dat['nodes']['row']) > 1:
inst_net.make_clust(dist_type='jaccard', views=['N_row_sum'], dendro=False)
else:
inst_net.make_clust(dist_type='jaccard', views=['N_row_sum'], dendro=False, run_clustering = False)
inst_views = inst_net.viz['views']
# add score_type to views
for inst_view in inst_views:
inst_view['N_col_sum'] = num_dict[num_terms]
inst_view['enr_score_type'] = score_type
# add values to col_nodes and order according to rank
for inst_col in inst_view['nodes']['col_nodes']:
inst_col['rank'] = len(top_terms[score_type][num_terms]) - top_terms[score_type][num_terms].index(inst_col['name'])
inst_name = inst_col['name']
inst_col['value'] = scores[score_type][inst_name]
# add views to main network
net.viz['views'].extend(inst_views)
return net
# net.normalize(axis='row', norm_type='qn')
# net.normalize(axis='col', norm_type='zscore', keep_orig=True)
# net.filter_N_top('row', 100, rank_type='var')
# net.filter_N_top('col', 3, rank_type='var')
# net.filter_threShold('col', threshold=2, num_occur=3
# net.filter_threshold('row', threshold=3.0, num_occur=4)
net.swap_nan_for_zero()
# df = net.dat_to_df()
views = ['N_row_sum', 'N_row_var']
net.make_clust(dist_type='cos',views=views , dendro=True,
sim_mat=True, filter_sim=0.1, calc_cat_pval=False)
# run_enrichr=['ChEA_2015'])
# run_enrichr=['ENCODE_TF_ChIP-seq_2014'])
# run_enrichr=['KEA_2015'])
# run_enrichr=['GO_Biological_Process_2015'])
net.write_json_to_file('viz', 'json/mult_view.json', 'no-indent')
net.write_json_to_file('sim_row', 'json/mult_view_sim_row.json', 'no-indent')
net.write_json_to_file('sim_col', 'json/mult_view_sim_col.json', 'no-indent')
# net.write_matrix_to_tsv ('txt/export_tmp.txt')
elapsed_time = time.time() - start_time
print('\n\nelapsed time: '+str(elapsed_time))
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')
# net.load_file('txt/mat_cats.tsv')
# net.load_file('txt/mat_1mb.Txt')
# net.load_file('txt/mnist.txt')
# net.load_file('txt/sim_mat_4_cats.txt')
views = ['N_row_sum', 'N_row_var']
# # filtering rows and cols by sum
# net.filter_sum('row', threshold=20)
# net.filter_sum('col', threshold=30)
# # keep top rows based on sum
# net.filter_N_top('row', 10, 'sum')
net.make_clust(dist_type='cos',
views=views,
dendro=True,
sim_mat=True,
filter_sim=0.1)
# net.produce_view({'N_row_sum':10,'dist':'euclidean'})
net.write_json_to_file('viz', 'json/mult_view.json', 'no-indent')
net.write_json_to_file('sim_row', 'json/mult_view_sim_row.json', 'no-indent')
net.write_json_to_file('sim_col', 'json/mult_view_sim_col.json', 'no-indent')
elapsed_time = time.time() - start_time
print('\n\nelapsed time')
print(elapsed_time)
import time
start_time = time.time()
from clustergrammer import Network
net = Network()
net.load_file('txt/rc_two_cats.txt')
# net.load_file('txt/tmp.txt')
views = ['N_row_sum','N_row_var']
net.make_clust(dist_type='cos',views=views , dendro=True, sim_mat=True)
net.write_json_to_file('viz', 'json/mult_view.json')
net.write_json_to_file('sim_row', 'json/mult_view_sim_row.json')
net.write_json_to_file('sim_col', 'json/mult_view_sim_col.json')
elapsed_time = time.time() - start_time
print('\n\nelapsed time')
print(elapsed_time)
####################
inst_name = 'Tyrosine'
# net.load_file('txt/phos_ratios_all_treat_no_geld_ST.txt')
net.load_file('txt/phos_ratios_all_treat_no_geld_Tyrosine.txt')
net.swap_nan_for_zero()
# net.normalize(axis='row', norm_type='zscore', keep_orig=True)
print(net.dat.keys())
views = ['N_row_sum', 'N_row_var']
net.make_clust(dist_type='cos',
views=views,
dendro=True,
sim_mat=True,
filter_sim=0.1,
calc_cat_pval=False)
# run_enrichr=['KEA_2015'])
# run_enrichr=['ENCODE_TF_ChIP-seq_2014'])
# run_enrichr=['GO_Biological_Process_2015'])
net.write_json_to_file('viz', 'json/' + inst_name + '.json', 'no-indent')
net.write_json_to_file('sim_row', 'json/' + inst_name + '_sim_row.json',
'no-indent')
net.write_json_to_file('sim_col', 'json/' + inst_name + '_sim_col.json',
'no-indent')
elapsed_time = time.time() - start_time
print('\n\nelapsed time: ' + str(elapsed_time))
