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 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 process_GCT_and_export_tsv():
from clustergrammer import Network
filename = 'gcts/LDS-1003.gct'
print('exporting processed GCT as tsv file')
df = load_file(filename)
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', 200)
net.write_matrix_to_tsv('txt/example_gct_export.txt')
def process_GCT_and_export_tsv():
from clustergrammer import Network
filename = 'gcts/LDS-1003.gct'
print('exporting processed GCT as tsv file')
df = load_file(filename)
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', 200)
net.write_matrix_to_tsv('txt/example_gct_export.txt')
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)
# 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])
