def make_network_json_single_value(nodes, clust_order, mat ):
import json
import d3_clustergram
# initialize dict
d3_json = d3_clustergram.ini_d3_json()
# append row dicts to array
for i in range(len(nodes['row'])):
inst_dict = {}
inst_dict['name'] = nodes['row'][i]
inst_dict['sort'] = clust_order['row'].index(i)
d3_json['row_nodes'].append(inst_dict)
# append col dicts to array
for i in range(len(nodes['col'])):
inst_dict = {}
inst_dict['name'] = nodes['col'][i]
inst_dict['sort'] = clust_order['col'].index(i)
d3_json['col_nodes'].append(inst_dict)
# links - generate edge list
for i in range(len(nodes['row'])):
for j in range(len(nodes['col'])):
inst_dict = {}
inst_dict['source'] = i
inst_dict['target'] = j
inst_dict['value'] = mat[i,j]
d3_json['links'].append( inst_dict )
# return the json
return d3_json
def make_network_json_single_value(nodes, clust_order, mat):
import json
import d3_clustergram
# initialize dict
d3_json = d3_clustergram.ini_d3_json()
# append row dicts to array
for i in range(len(nodes['row'])):
inst_dict = {}
inst_dict['name'] = nodes['row'][i]
inst_dict['sort'] = clust_order['row'].index(i)
d3_json['row_nodes'].append(inst_dict)
# append col dicts to array
for i in range(len(nodes['col'])):
inst_dict = {}
inst_dict['name'] = nodes['col'][i]
inst_dict['sort'] = clust_order['col'].index(i)
d3_json['col_nodes'].append(inst_dict)
# links - generate edge list
for i in range(len(nodes['row'])):
for j in range(len(nodes['col'])):
inst_dict = {}
inst_dict['source'] = i
inst_dict['target'] = j
inst_dict['value'] = mat[i, j]
d3_json['links'].append(inst_dict)
# return the json
return d3_json
def d3_clust_single_value(nodes, clust_order, data_mat):
import json
import d3_clustergram
# initialize dict
d3_json = d3_clustergram.ini_d3_json()
# append row dicts to array
for i in range(len(nodes['row'])):
inst_dict = {}
inst_dict['name'] = nodes['row'][i]
inst_dict['clust'] = clust_order['clust']['row'].index(i)
inst_dict['rank'] = clust_order['rank']['row'][i]
# append to row_nodes
d3_json['row_nodes'].append(inst_dict)
# append col dicts to array
for i in range(len(nodes['col'])):
inst_dict = {}
inst_dict['name'] = nodes['col'][i]
inst_dict['clust'] = clust_order['clust']['col'].index(i)
inst_dict['rank'] = clust_order['rank']['col'][i]
# add to d3_json
d3_json['col_nodes'].append(inst_dict)
# links - generate edge list
###############################
# simply transfer the data from mat
for i in range(len(nodes['row'])):
for j in range(len(nodes['col'])):
# only gather links that are non-zero
if data_mat[i,j] > 0:
# create link dict
####################
# initialize dict
inst_dict = {}
# set source and target
inst_dict['source'] = i
inst_dict['target'] = j
# get the value from the original matrix
inst_value = data_mat[i,j]
# save the inst_dict['value']
inst_dict['value'] = inst_value
# append to links
d3_json['links'].append( inst_dict )
return d3_json
def write_json_single_value(nodes, clust_order, mat, full_path, row_class={}, col_class={}, link_hl={} ):
import json
import json_scripts
import d3_clustergram
# initialize dict
d3_json = d3_clustergram.ini_d3_json()
# generate distance cutoffs
all_dist = []
for i in range(11):
all_dist.append(float(i)/10)
#!! generate tmp classes
import random
random.seed(122341)
# append row dicts to array
for i in range(len(nodes['row'])):
inst_dict = {}
inst_dict['name'] = nodes['row'][i]
inst_dict['clust'] = clust_order['clust']['row'].index(i)
# do not need to get index
inst_dict['rank'] = clust_order['rank']['row'][i]
# save group
inst_dict['group'] = []
for inst_dist in all_dist:
inst_dict['group'].append( float(clust_order['group']['row'][inst_dist][i]) )
# save value for bar
inst_dict['value'] = random.random()
# add class information
inst_dict['class'] = row_class[nodes['row'][i]]
# append dictionary
d3_json['row_nodes'].append(inst_dict)
# append col dicts to array
for i in range(len(nodes['col'])):
inst_dict = {}
inst_dict['name'] = nodes['col'][i]
inst_dict['clust'] = clust_order['clust']['col'].index(i)
# do not need to get index
inst_dict['rank'] = clust_order['rank']['col'][i]
# save group data for different cutoffs
inst_dict['group'] = []
for inst_dist in all_dist:
inst_dict['group'].append( float(clust_order['group']['col'][inst_dist][i]) )
# save value for bar
inst_dict['value'] = random.random()
# add class information
inst_dict['class'] = col_class[nodes['col'][i]]
# append dictionary
d3_json['col_nodes'].append(inst_dict)
# links - generate edge list
for i in range(len(nodes['row'])):
for j in range(len(nodes['col'])):
if abs(mat[i,j]) > 0:
inst_dict = {}
inst_dict['source'] = i
inst_dict['target'] = j
inst_dict['value'] = mat[i,j]
# initailize with no highlight
inst_dict['highlight'] = 0
# add highlight if necessary
if len(link_hl) > 0:
# check highlight
if nodes['col'][j] in link_hl:
# check if gene is a known target of the transcription factor
if nodes['row'][i] in link_hl[nodes['col'][j]]:
# highlight
inst_dict['highlight'] = 1
d3_json['links'].append( inst_dict )
# write json
##############
# fw = open(full_path, 'w')
# fw.write( json.dumps( d3_json, indent=2) )
# fw.close()
json_scripts.save_to_json(d3_json, full_path, 'noindent')
def d3_clust_single_value(nodes, clust_order, mat, terms_colors):
import json
import d3_clustergram
# initialize dict
d3_json = d3_clustergram.ini_d3_json()
# append row dicts to array
for i in range(len(nodes['row'])):
inst_dict = {}
inst_dict['name'] = nodes['row'][i]
inst_dict['clust'] = clust_order['clust']['row'].index(i)
inst_dict['rank'] = clust_order['rank']['row'][i]
inst_dict['nl_pval'] = clust_order['nl_pval']['row'][i]
# append to row_nodes
d3_json['row_nodes'].append(inst_dict)
# append col dicts to array
for i in range(len(nodes['col'])):
inst_dict = {}
inst_dict['name'] = nodes['col'][i]
inst_dict['clust'] = clust_order['clust']['col'].index(i)
inst_dict['rank'] = clust_order['rank']['col'][i]
inst_dict['pval'] = clust_order['pval']['col'][i]
inst_dict['nl_pval'] = clust_order['nl_pval']['col'][i]
inst_dict['pval_bh'] = clust_order['pval_bh']['col'][i]
inst_dict['color'] = terms_colors[nodes['col'][i]]
# print(nodes['col'][i])
# add to d3_json
d3_json['col_nodes'].append(inst_dict)
# get max and min col and row value for scaling
# the row nl_pval is just the sum of enriched terms the gene appears in
max_row_value = max(clust_order['nl_pval']['row'])
max_col_value = max(clust_order['nl_pval']['col'])
# links - generate edge list
for i in range(len(nodes['row'])):
for j in range(len(nodes['col'])):
# initialize dict
inst_dict = {}
# set source and target
inst_dict['source'] = i
inst_dict['target'] = j
# calculate the inst_value, a combination of col and row attributes
# scale value by largest
row_value = clust_order['nl_pval']['row'][i] / max_row_value
col_value = clust_order['nl_pval']['col'][j] / max_col_value
# take the mean of the two values times the binary mat[i,j]
inst_value = ( row_value + col_value )/ 2 * mat[i,j]
# make minimum inst_value 0.1
if inst_value > 0:
# save the inst_dict['value']
inst_dict['value'] = inst_value + 0.1
# add the color
inst_dict['color'] = terms_colors[nodes['col'][j]]
# only keep non-zero value links
if inst_value > 0:
# append to links
d3_json['links'].append( inst_dict )
return d3_json
def write_json_single_value(nodes, clust_order, LDR, full_path, perts, row_class={}, col_class={}, link_hl={} ):
import json
import json_scripts
import d3_clustergram
print(perts.keys())
#!! special case, encode extra released information for LDR
mat = LDR['mat']
# get release data
rl = LDR['rl']
print('\n\nchecking rl\n\n')
# print(rl['t'])
# initialize dict
d3_json = d3_clustergram.ini_d3_json()
# generate distance cutoffs
all_dist = []
for i in range(11):
all_dist.append(float(i)/10)
#!! generate tmp classes
import random
random.seed(122341)
# append row dicts to array
for i in range(len(nodes['row'])):
inst_dict = {}
inst_dict['name'] = nodes['row'][i]
inst_dict['clust'] = clust_order['clust']['row'].index(i)
# do not need to get index
inst_dict['rank'] = clust_order['rank']['row'][i]
# # save group
# inst_dict['group'] = []
# for inst_dist in all_dist:
# inst_dict['group'].append( float(clust_order['group']['row'][inst_dist][i]) )
# # save value for bar
# inst_dict['value'] = random.random()
# # add class information
# inst_dict['class'] = row_class[nodes['row'][i]]
# append dictionary
d3_json['row_nodes'].append(inst_dict)
# append col dicts to array
for i in range(len(nodes['col'])):
inst_dict = {}
inst_dict['name'] = nodes['col'][i]
inst_dict['clust'] = clust_order['clust']['col'].index(i)
# do not need to get index
inst_dict['rank'] = clust_order['rank']['col'][i]
# # save group data for different cutoffs
# inst_dict['group'] = []
# for inst_dist in all_dist:
# inst_dict['group'].append( float(clust_order['group']['col'][inst_dist][i]) )
# # save value for bar
# inst_dict['value'] = random.random()
# # add class information
# inst_dict['class'] = col_class[nodes['col'][i]]
# append dictionary
d3_json['col_nodes'].append(inst_dict)
# links - generate edge list
for i in range(len(nodes['row'])):
for j in range(len(nodes['col'])):
if abs(mat[i,j]) > 0:
inst_dict = {}
inst_dict['source'] = i
inst_dict['target'] = j
inst_dict['value'] = mat[i,j]
# !! custom change for LDRgram
inst_dict['value_up'] = rl['t'][i,j]
inst_dict['value_dn'] = -rl['f'][i,j]
# print('\tas: '+nodes['row'][i])
# print('\tcl: '+nodes['col'][j])
# add perturbation information
inst_tuple = ( nodes['row'][i], nodes['col'][j] )
# print( perts[inst_tuple] )
# add to dictionary
inst_dict['perts'] = perts[inst_tuple]
d3_json['links'].append( inst_dict )
# write json
##############
json_scripts.save_to_json(d3_json, full_path, 'indent')
def write_json_single_value(nodes,
clust_order,
LDR,
full_path,
perts,
row_class={},
col_class={},
link_hl={}):
import json
import json_scripts
import d3_clustergram
print(perts.keys())
#!! special case, encode extra released information for LDR
mat = LDR['mat']
# get release data
rl = LDR['rl']
print('\n\nchecking rl\n\n')
# print(rl['t'])
# initialize dict
d3_json = d3_clustergram.ini_d3_json()
# generate distance cutoffs
all_dist = []
for i in range(11):
all_dist.append(float(i) / 10)
#!! generate tmp classes
import random
random.seed(122341)
# append row dicts to array
for i in range(len(nodes['row'])):
inst_dict = {}
inst_dict['name'] = nodes['row'][i]
inst_dict['clust'] = clust_order['clust']['row'].index(i)
# do not need to get index
inst_dict['rank'] = clust_order['rank']['row'][i]
# # save group
# inst_dict['group'] = []
# for inst_dist in all_dist:
# inst_dict['group'].append( float(clust_order['group']['row'][inst_dist][i]) )
# # save value for bar
# inst_dict['value'] = random.random()
# # add class information
# inst_dict['class'] = row_class[nodes['row'][i]]
# append dictionary
d3_json['row_nodes'].append(inst_dict)
# append col dicts to array
for i in range(len(nodes['col'])):
inst_dict = {}
inst_dict['name'] = nodes['col'][i]
inst_dict['clust'] = clust_order['clust']['col'].index(i)
# do not need to get index
inst_dict['rank'] = clust_order['rank']['col'][i]
# # save group data for different cutoffs
# inst_dict['group'] = []
# for inst_dist in all_dist:
# inst_dict['group'].append( float(clust_order['group']['col'][inst_dist][i]) )
# # save value for bar
# inst_dict['value'] = random.random()
# # add class information
# inst_dict['class'] = col_class[nodes['col'][i]]
# append dictionary
d3_json['col_nodes'].append(inst_dict)
# links - generate edge list
for i in range(len(nodes['row'])):
for j in range(len(nodes['col'])):
if abs(mat[i, j]) > 0:
inst_dict = {}
inst_dict['source'] = i
inst_dict['target'] = j
inst_dict['value'] = mat[i, j]
# !! custom change for LDRgram
inst_dict['value_up'] = rl['t'][i, j]
inst_dict['value_dn'] = -rl['f'][i, j]
# print('\tas: '+nodes['row'][i])
# print('\tcl: '+nodes['col'][j])
# add perturbation information
inst_tuple = (nodes['row'][i], nodes['col'][j])
# print( perts[inst_tuple] )
# add to dictionary
inst_dict['perts'] = perts[inst_tuple]
d3_json['links'].append(inst_dict)
# write json
##############
json_scripts.save_to_json(d3_json, full_path, 'indent')