# Usage: python make_graphviz_file3C.py AT1G19850
#
# Make plot: python make_graphviz_file3C.py AT1G65480 | sfdp -Goverlap=false -Tpdf -o result.flower.pdf result.flower.gv
# unflatten -f -l 3 result.flower.gv | dot -Tsvg -o result.flower.svg
#
# Purpose: Generate result.txt for Graphviz software dot. The single
# parameter AT1G19850 is a TF.
# The query neighbours of the TF's neighbours are also shown.
#
# Created 10 July 2017, hui, slcu
import random
import numpy as np
import sys
from geneid2name import make_gene_name_AGI_map_dict, get_gene_name
PERCENT = 1
NUM_TARGETS_CUTOFF = 5
def get_tf_tissue(fname):
d = {}
f = open(fname)
lines = f.readlines()
f.close()
head = lines[0].strip()
head_lst = head.split('\t')
head_lst = head_lst[1:] # remove TF
for line in lines[1:]:
line = line.strip()
lst = line.split('\t')
lst2 = lst[1:]
lst3 = [int(x) for x in lst2]
lst4 = np.array(lst3)
median_val = np.median(lst4)
tissue = []
for i in range(len(lst2)):
if int(lst2[i]) >= max(median_val, 1) or int(lst2[i]) >= NUM_TARGETS_CUTOFF:
tissue.append(head_lst[i])
tf = (lst[0].split())[0]
d[tf] = tissue # tf is assigned with a list of tissues, the tissue with node degree greater than median are selected.
return d
def get_tissue_from_fname(fname):
tissue_lst = [
'seedling',
'meristem',
'flower',
'aerial',
'shoot',
'seed',
'leaf',
'root',
'stem']
for x in tissue_lst:
if x in fname:
return x
return 'unknown'
def get_edge(fname):
''' Return d = {'flower':{'tf':[target1,target2, ...]}, 'seed':{}} '''
d = {}
d2 = {} # the actual correlation coefficient, absolute value
f = open(fname)
for line in f:
line = line.strip()
if not line.startswith('#'):
lst = line.split('\t')
target = (lst[0].split('_'))[0]
tf = (lst[1].split('_'))[0]
if not tf in d[tissue]:
d[tissue][tf] = [target]
else:
d[tissue][tf].append(target)
strength = abs(float(lst[2]))
if not tf in d2[tissue]:
d2[tissue][tf] = {target:strength}
else:
d2[tissue][tf][target] = strength
else:
tissue = get_tissue_from_fname(line)
d[tissue] = {}
d2[tissue] = {}
f.close()
return d, d2
def in_same_tissue(source, target, node_dict):
return node_dict[source] == node_dict[target]
def make_label(a, b):
if b == '.':
return a
else:
lst = b.split(';')
return a + '_' + lst[0]
def has_predecessor(tf, d):
for k in d:
if tf in d[k] and k != tf:
return True
return False
def get_num_successors(tf, d):
if not tf in d:
return 0
return len(d[tf])
def get_shape(tf, d):
''' d = {'tf':[target1, target2]} '''
p = has_predecessor(tf, d)
s = get_num_successors(tf, d)
if s > 0 and p:
return 'doublecircle'
if s > 0:
return 'ellipse' # regulator
if p:
return 'egg' # regulatee
def get_color(tf, edge_dict, tissue):
#colours = ['darkolivegreen1', 'darkolivegreen2', 'darkolivegreen3', 'darkolivegreen4', 'gold', 'gold1', 'gold2', 'gold3', 'gold4', 'darkgoldenrod', 'darkgoldenrod4']
#colours = ['snow', 'snow1', 'snow2', 'snow3', 'snow4', 'gold', 'gold1', 'gold2', 'gold3', 'gold4']
colours = ['springgreen', 'springgreen1', 'springgreen2', 'springgreen3', 'springgreen4', 'gold', 'gold1', 'gold2', 'gold3', 'gold4']
d = {}
total = 0
for k in edge_dict:
n = get_num_successors(tf, edge_dict[k])
d[k] = n
total += n
#print('%s %d' % (k, n))
if total == 0:
return 'azure'
return colours[min(int(10 * 1.0 * d[tissue] / total), len(colours)-1)]
def make_more_string(n, tissue, edge_dict, colour_dict, agi2name_dict, query_tf):
result = ''
d = edge_dict[tissue]
if n in d:
for target in d[n]:
ll = make_label(target, get_gene_name(target, agi2name_dict))
shape = get_shape(target, d)
color = get_color(target, edge_dict, tissue)
node_target = target + '_' + tissue + '.2'
if target != query_tf:
result += ' \"%s\" [label=\"%s\", fillcolor=%s, color=%s, shape=%s, style=filled];\n' % (node_target, ll, color, colour_dict[tissue], shape)
node_n = n + '_' + tissue
if random.uniform(0, 1) <= PERCENT:
result += ' \"%s\" -> \"%s\" [color=%s];\n' % (node_n, node_target, 'gold')
for tf in d:
if tf != query_tf:
if n in d[tf]: # n is successor
ll = make_label(tf, get_gene_name(tf, agi2name_dict))
shape = get_shape(tf, d)
color = get_color(tf, edge_dict, tissue)
node_tf = tf + '_' + tissue + '.2'
result += ' \"%s\" [label=\"%s\", fillcolor=%s, color=%s, shape=%s, style=filled];\n' % (node_tf, ll, color, colour_dict[tissue], shape)
node_n = n + '_' + tissue
if random.uniform(0, 1) <= PERCENT:
result += ' \"%s\" -> \"%s\" [color=%s];\n' % (node_tf, node_n, 'red')
return result
def write_graphviz_file(fname, edge_dict, colour_dict, agi2name_dict, query_tf):
f = open(fname, 'w')
graph_dict = {}
more = {}
for k in edge_dict:
graph_dict[k] = {'head':'', 'nodes':[], 'edges':[]}
for k in edge_dict: # k is tissue
neighbours = []
node_added_dict = {}
tissue_node = '%s_node' % (k)
graph_dict[k]['head'] = ''
d = edge_dict[k]
tf_lst = d.keys()
for tf in tf_lst:
node_tf = tf + '_' + k
if tf == query_tf:
ll = make_label(tf, get_gene_name(tf, agi2name_dict))
shape = get_shape(tf, d)
color = get_color(tf, edge_dict, k)
if not tf in node_added_dict:
graph_dict[k]['nodes'].append(' \"%s\" [label=\"%s\", fillcolor=%s, color=%s, shape=%s, style=filled];\n' % (node_tf, 'Query gene: '+ll, 'DeepSkyBlue', colour_dict[k], shape))
node_added_dict[tf] = 'YES'
for target in d[tf]:
ll = make_label(target, get_gene_name(target, agi2name_dict))
node_target = target + '_' + k
shape = get_shape(target, d)
color = get_color(target, edge_dict, k)
if random.uniform(0, 1) <= PERCENT:
if not target in node_added_dict:
neighbours.append(target)
graph_dict[k]['nodes'].append(' \"%s\" [label=\"%s\", fillcolor=%s, color=%s, shape=%s, style=filled];\n' % (node_target, ll, color, colour_dict[k], shape))
node_added_dict[target] = 'YES'
graph_dict[k]['edges'].append(' \"%s\" -> \"%s\" [color=%s];\n' % (node_tf, node_target, 'gold'))
else: # check if tf is a target of another tf
for target in d[tf]:
if target == query_tf:
ll = make_label(tf, get_gene_name(tf, agi2name_dict))
node_tf = tf + '_' + k
shape = get_shape(tf, d)
color = get_color(tf, edge_dict, k)
node_target = target + '_' + k
if random.uniform(0, 1) <= PERCENT:
if not tf in node_added_dict:
neighbours.append(tf)
graph_dict[k]['nodes'].append(' \"%s\" [label=\"%s\", fillcolor=%s, color=%s, shape=%s, style=filled];\n' % (node_tf, ll, color, colour_dict[k], shape))
node_added_dict[tf] = 'YES'
graph_dict[k]['edges'].append(' \"%s\" -> \"%s\" [color=%s];\n' % (node_tf, node_target, 'red'))
neighbours = list(set(neighbours))
more[k] = ''
for n in neighbours:
more[k] += make_more_string(n, k, edge_dict, colour_dict, agi2name_dict, query_tf)
for k in graph_dict:
if graph_dict[k]['nodes'] != []:
f = open(fname + '.' + k + '.gv', 'w')
s0 = 'digraph G {\n graph[splines=true, ranksep=3, fontname=Arial];\n node[fontname=Arial];\n'
s0 += graph_dict[k]['head']
for x in graph_dict[k]['nodes']:
s0 += x
for x in graph_dict[k]['edges']:
s0 += x
if k in more:
s0 += more[k]
s0 += '}\n'
f.write(s0)
f.close()
# main
GENE_ID_TO_GENE_NAME = '../Data/information/AGI-to-gene-names_v2.txt'
agi2name_dict = make_gene_name_AGI_map_dict(GENE_ID_TO_GENE_NAME)
edge_file = 'result.skeleton.txt' # prepared by test_network4.py
node_file = 'result.out.txt' # prepared by test_network4.py
tissue_colour_dict = {
'seedling':'greenyellow',
'meristem':'skyblue4',
'flower':'lightpink',
'aerial':'cyan',
'shoot':'forestgreen',
'seed':'black',
'leaf':'green',
'root':'gold',
'stem':'orange4'}
if len(sys.argv) < 2:
sys.exit()
else:
query_tf = sys.argv[1]
#tf_tissue_dict = get_tf_tissue(node_file )
edge_dict, edge_dict_r = get_edge(edge_file)
write_graphviz_file('result', edge_dict, tissue_colour_dict, agi2name_dict, query_tf)