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 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_enr_vect_clust():
import enrichr_functions as enr_fun
from clustergrammer import Network
net = Network()
g2e_post = net.load_json_to_dict('json/g2e_enr_vect.json')
net = enr_fun.make_enr_vect_clust(g2e_post, 0.001, 1)
net.write_json_to_file('viz','json/enr_vect_example.json')
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 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 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 prepare_heatmap(matrix_input, html_file, html_dir, tools_dir, categories, distance, linkage):
# prepare directory and html
os.mkdir(html_dir)
env = Environment(loader=FileSystemLoader(tools_dir + "/templates"))
template = env.get_template("clustergrammer.template")
overview = template.render()
with open(html_file, "w") as outf:
outf.write(overview)
json_output = html_dir + "/mult_view.json"
net = Network()
net.load_file(matrix_input)
if (categories['row']):
net.add_cats('row', categories['row'])
if (categories['col']):
net.add_cats('col', categories['col'])
net.cluster(dist_type=distance, linkage_type=linkage)
net.write_json_to_file('viz', json_output)
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 get_clustergrammer_json(self, outfile):
# Create network
net = Network()
# Load file
net.load_df(self.expression_dataframe)
# Add categories
try:
net.add_cats('col', self.sample_cats)
except:
pass
try:
# calculate clustering using default parameters
net.cluster()
# save visualization JSON to file for use by front end
net.write_json_to_file('viz', outfile)
except:
os.system('touch {outfile}'.format(**locals()))
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 prepare_clustergrammer_data(self,
outfname='clustergrammer_data.json',
G=None):
"""for a distance matrix, output a clustergrammer JSON file
that clustergrammer-js can use
for now it loads the clustergrammer-py module from local dev files
TODO: once changes are pulled into clustergrammer-py, we can use the actual module (pip)
:outfname: filename for the output json
:G: networkx graph (use self.G_sym by default)
"""
G = self.G_sym or self.G
# if Z is None:
# G = self.G_sym or self.G
# Z = self.get_linkage(G)
clustergrammer_py_dev_dir = '../clustergrammer/clustergrammer-py/'
sys.path.insert(0, clustergrammer_py_dev_dir)
from clustergrammer import Network as ClustergrammerNetwork
start = timer()
d = nx.to_numpy_matrix(G)
df = pd.DataFrame(d, index=G.nodes(), columns=G.nodes())
net = ClustergrammerNetwork()
# net.load_file(infname)
# net.load_file(mat)
net.load_df(df)
net.cluster(dist_type='precalculated')
logger.debug("done loading and clustering. took {}".format(
format_timespan(timer() - start)))
logger.debug("writing to {}".format(outfname))
start = timer()
net.write_json_to_file('viz', outfname)
logger.debug("done writing file {}. took {}".format(
outfname, format_timespan(timer() - start)))
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)
ids = delta_f.columns.map(lambda x: x.split('|')[0])
fout = open("%s_heatmap_matrix.txt" % args.d, 'w')
fout.write("\t\t%s\n" % ('\t'.join(tfs)))
cls = []
for i in ids:
if ann_dict.get(i, ['NA'])[0] == 'NA':
cls.append("Cell Line: %s" % ('NA'))
else:
cls.append("Cell Line: %s" % (ann_dict[i][0]))
fout.write("\t\t%s\n" % ('\t'.join(cls)))
ts = []
for i in ids:
if ann_dict.get(i, ['NA', 'NA'])[1] == 'NA':
ts.append("Tissue: %s" % ('NA'))
else:
ts.append("Tissue: %s" % (ann_dict[i][1]))
fout.write("\t\t%s\n" % ('\t'.join(ts)))
for i in range(status.shape[0]):
fout.write('%s\t%s\t%s\n' %
("Gene: %s" % genes[i], "Input Gene: %s" % status[i], '\t'.join(
delta_f.iloc[i, :].map(str))))
fout.close()
net.load_file("%s_heatmap_matrix.txt" % args.d)
net.cluster()
net.write_json_to_file('viz', '%s_mult_view.json' % args.d)
# make network object and load file
from clustergrammer import Network
net = Network()
net.load_file('mult_view.tsv')
# Z-score normalize the rows
#net.normalize(axis='row', norm_type='zscore', keep_orig=True)
# calculate clustering using default parameters
net.cluster()
# save visualization JSON to file for use by front end
net.write_json_to_file('viz', 'mult_view.json')
# needs pandas and sklearn as well
# pip install --user --upgrade clustergrammer pandas sklearn
# net.enrichrgram('KEA_2015')
# 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.set_cat_color('col', 1, 'Category: one', 'blue')
# net.make_clust()
# net.dendro_cats('row', 5)
net.cluster(dist_type='cos',
views=['N_row_sum', 'N_row_var'],
dendro=True,
sim_mat=True,
filter_sim=0.1,
calc_cat_pval=False,
enrichrgram=False,
run_clustering=True)
# write jsons for front-end visualizations
net.write_json_to_file('viz', '/../../../../../pulmon/json/mult_view.json',
'indent')
net.write_json_to_file('sim_row', '/pulmon/json/mult_view_sim_row.json',
'no-indent')
net.write_json_to_file('sim_col', '/pulmon/json/mult_view_sim_col.json',
'no-indent')
"#CC0744", "#C0B9B2", "#C2FF99", "#001E09", "#00489C", "#6F0062",
"#0CBD66", "#EEC3FF", "#456D75", "#B77B68", "#7A87A1", "#788D66",
"#885578", "#0089A3", "#FF8A9A", "#D157A0", "#BEC459", "#456648",
"#0086ED", "#886F4C", "#34362D", "#B4A8BD", "#00A6AA", "#452C2C",
"#636375", "#A3C8C9", "#FF913F", "#938A81", "#575329", "#00FECF",
"#B05B6F", "#8CD0FF", "#3B9700", "#04F757", "#C8A1A1", "#1E6E00",
"#7900D7", "#A77500", "#6367A9", "#A05837", "#6B002C", "#772600",
"#D790FF", "#9B9700", "#549E79", "#FFF69F", "#201625", "#CB7E98",
"#72418F", "#BC23FF", "#99ADC0", "#3A2465", "#922329", "#5B4534",
"#FDE8DC", "#404E55", "#FAD09F", "#A4E804", "#f58231", "#324E72", "#402334"
]
for i in range(len(color_array3)):
label = 'SC3 label: _' + str(i) + '_'
net.set_cat_color(axis='col',
cat_index=1,
cat_name=label,
inst_color=color_array3[i])
#console.log(color_array[i]);
if use_user_label == '1':
for j in range(len(unique_array)):
userlabel = 'User\'s label: _' + str(unique_array[j]) + '_'
net.set_cat_color(axis='col',
cat_index=2,
cat_name=userlabel,
inst_color=color_array3[63 - j])
net.cluster(dist_type='cos', enrichrgram=True, run_clustering=False)
# write jsons for front-end visualizations
out = wd + 'json/' + outname + '.json'
net.write_json_to_file('viz', out, 'indent')
from clustergrammer import Network
import sys
filename = sys.argv[-1]
net = Network()
print("Python is fun.")
print(filename)
filepath = '/Users/snehalpatil/Documents/GithubProjects/gsesuite-data/heatmap/' + (
filename)
print(filepath)
net.load_file(filepath)
net.cluster()
jsonname = filename.replace(".txt", ".json")
jsonfilepath = '/Users/snehalpatil/Documents/GithubProjects/gsesuite-data/heatmap/' + jsonname
net.write_json_to_file('viz', jsonfilepath)
# make network object and load file
from clustergrammer import Network
net = Network()
b = "cluster.txt"
d = "cluster.json"
net.load_file(b)
# calculate clustering using default parameters
net.cluster()
# save visualization JSON to file for use by front end
net.write_json_to_file('viz', 'cluster.json')
print('loading file...')
net = Network()
# load matrix file
net.load_file(matrix_filename)
print('done')
# cluster using default parameters
print('clustering the matrix...')
net.cluster(dist_type='jaccard', linkage_type='complete')
# net.cluster(run_clustering=False)
print('done')
# save visualization JSON to file for use by front end
print('saving results in json file...')
json_filename = matrix_filename + '.json'
net.write_json_to_file('viz', json_filename)
print('done')
# creating the html page
print('creating the html page...')
network_data = ''
file = open(json_filename, 'rt')
for line in file:
network_data += line
file.close()
print(len(network_data))
load_viz_new_filename = '/home/meheurap/scripts/proteinCluster/load_viz_new.js'
load_viz_new = ''
file = open(load_viz_new_filename, 'rt')
for line in file:
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)
# new_cols = [(x, 'Cat-1: A', 'Cat-2: B', 'Cat-3: C') for x in df.columns]
df.index = new_rows
df.columns = new_cols
net.load_df(df)
net.cluster(dist_type='cos',
views=['N_row_sum', 'N_row_var'],
dendro=True,
sim_mat=False,
filter_sim=0.1,
calc_cat_pval=False,
enrichrgram=True)
# write jsons for front-end visualizations
net.write_json_to_file('viz', 'data/big_data/custom.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.normalize(axis='row', norm_type='zscore')
net.cluster(dist_type='cos',
views=['N_row_sum', 'N_row_var'],
dendro=True,
sim_mat=False,
filter_sim=0.1,
calc_cat_pval=False,
enrichrgram=True)
# write jsons for front-end visualizations
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))
'''
The clustergrammer python module can be installed using pip:
pip install clustergrammer
or by getting the code from the repo:
https://github.com/MaayanLab/clustergrammer-py
'''
import os
from clustergrammer import Network
for filename in os.listdir("tsv"):
name = filename.split(".")[0]
net = Network()
# load matrix tsv file
print name
net.load_file('tsv/' + name + '.tsv')
# optional filtering and normalization
##########################################
net.swap_nan_for_zero()
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)
# write jsons for front-end visualizations
net.write_json_to_file('viz', 'output/' + name + '.json', 'indent')
# make network object and load file
from clustergrammer import Network
net = Network()
net.load_file('mult_view.tsv')
# Z-score normalize the rows
#net.normalize(axis='row', norm_type='zscore', keep_orig=True)
# calculate clustering using default parameters
net.cluster()
# save visualization JSON to file for use by front end
net.write_json_to_file('viz', 'mult_view.json')
# needs pandas and sklearn as well
# pip install --user --upgrade clustergrammer pandas sklearn
# make network object and load DataFrame, df
import sys
import pandas as pd
from clustergrammer import Network
df = pd.read_csv(sys.argv[1], header=True, index_col=0, sep='\t')
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
net.write_json_to_file('viz', sys.argv[2])
from clustergrammer import Network
net = Network()
# choose tsv file
####################
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))
# net.load_file('txt/tuple_cats.txt')
# net.load_file('txt/example_tsv.txt')
# net.enrichrgram('KEA_2015')
# 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.set_cat_color('col', 1, 'Category: one', 'blue')
# net.make_clust()
# net.dendro_cats('row', 5)
net.cluster(dist_type='cos',
views=['N_row_sum', 'N_row_var'],
dendro=True,
sim_mat=True,
filter_sim=0.1,
calc_cat_pval=False,
enrichrgram=True)
# write jsons for front-end visualizations
#net.write_json_to_file('viz', 'json/mult_view.json', 'indent')
net.write_json_to_file('viz', 'json/pooja.json', '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')
#from sys import argv
from clustergrammer import Network
net = Network()
net.load_file('mat.txt')
#argv[1]
# calculate clustering using default parameters
net.cluster()
# save visualization JSON to file for use by front end
net.write_json_to_file('viz', 'kbio_mhci_view.json')
net2 = Network()
net2.load_file('mat2.txt')
#argv[1]
# calculate clustering using default parameters
net2.cluster()
# save visualization JSON to file for use by front end
net2.write_json_to_file('viz', 'kbio_mhci_view_summary.json')
# net.load_file('txt/tuple_cats.txt')
# net.load_file('txt/example_tsv.txt')
# net.enrichrgram('KEA_2015')
# 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.set_cat_color('col', 1, 'Category: one', 'blue')
# net.make_clust()
# net.dendro_cats('row', 5)
net.cluster(dist_type='cos',
views=['N_row_sum', 'N_row_var'],
dendro=True,
sim_mat=True,
filter_sim=0.1,
calc_cat_pval=False,
enrichrgram=False,
run_clustering=True)
# write jsons for front-end visualizations
net.write_json_to_file('viz', 'json/out.json', 'indent')
net.write_json_to_file('sim_row', 'json/out.json', 'no-indent')
net.write_json_to_file('sim_col', 'json/out.json', 'no-indent')
import time
start_time = time.time()
# import network class from Network.py
from clustergrammer import Network
net = Network()
net.load_tsv_to_net('txt/example_tsv.txt')
net.make_filtered_views(dist_type='cos',views=['N_row_sum','pct_row_sum'])
net.write_json_to_file('viz', 'json/mult_view.json', 'indent')
# your code
elapsed_time = time.time() - start_time
print('\n\n\nelapsed time')
print(elapsed_time)
'''
Python 2.7
The clustergrammer python module can be installed using pip:
pip install clustergrammer
or by getting the code from the repo:
https://github.com/MaayanLab/clustergrammer-py
'''
from clustergrammer import Network
net = Network()
# load matrix tsv file
net.load_file('txt/heatmap_features.txt')
net.set_cat_color('row', 1, 'Feature Type: Interactivity', 'yellow')
net.set_cat_color('row', 1, 'Feature Type: Sharing', 'blue')
net.set_cat_color('row', 1, 'Feature Type: Usability', 'orange')
net.set_cat_color('row', 1, 'Feature Type: Biology-Specific', 'red')
net.cluster(dist_type='cos',
views=[],
dendro=True,
filter_sim=0.1,
calc_cat_pval=False,
enrichrgram=False)
# write jsons for front-end visualizations
net.write_json_to_file('viz', 'json/mult_view.json', 'indent')
# ],
# "bb":[
# "p1",
# "p2",
# "p3",
# "p4"
# ],
# "cc":[
# "p1",
# "p2",
# "p4"
# ],
# "dd":[
# "p2"
# ],
# "ee":[
# "p4"
# ]
# }
# }
# ])
# calculate clustering using default parameters
net.cluster()
# save visualization JSON to file for use by front end
net.write_json_to_file('viz', 'json/new_matrix.json')
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)
# import network class from Network.py
from clustergrammer import Network
# get instance of Network
net = Network()
print(net.__doc__)
print('make tsv clustergram')
# load network from tsv file
##############################
net.load_tsv_to_net('txt/example_tsv_network.txt')
inst_filt = 0.001
inst_meet = 1
net.filter_network_thresh(inst_filt,inst_meet)
# cluster
#############
net.cluster_row_and_col('cos')
# export data visualization to file
######################################
net.write_json_to_file('viz', 'json/default_example.json', 'indent')
