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# Usage: python make_graphviz_file3B.py AT1G19850
#
# Make plot: python make_graphviz_file3B.py AT1G65480 | dot -Tpdf -o result.pdf result.gv
# python make_graphviz_file3B.py AT1G65480 | neato -Goverlap=false -Tpdf -o result.pdf result.gv
#
# The plot is saved in result.pdf, and each little grey box contains a tissue name.
# Change 'pdf' to 'svg' to get a vector image. Tissue name is in yellow box. Double circle represents both a regulator and a regulatee.
# Egg represents a regulatee. Oval represent a regulator. Yellow arrow regulating. Red arrow being regulated.
#
# Input file is specified in variable edge_file (result.skeleton.txt). This file is generated by test_network4.py.
# The tissue name is contained in the lines starting with '##', e.g., '##TF skeleton size in shoot: 15735.' contains 'shoot'.
# Edit the variable tissue_colour_dict and tissue_lst in function get_tissue_from_fname() to match with the tissue names.
#
#
# Purpose: Generate result.gv for Graphviz software dot. The single
# parameter AT1G19850 is a TF. result.gv contains all edges from/to the TF
# in each tissue. A tissue is a subgraph. We can
# convert result.gv to a figure using 'dot -Tpdf -o result.pdf
# result.gv'.
#
# Created 6 July 2017, hui, slcu
# Last modified 11 July 2017, hui, slcu
import random
import numpy as np
import sys
from geneid2name import make_gene_name_AGI_map_dict, get_gene_name
NUM_TARGETS_CUTOFF = 5
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: # tf is both a regulator and a regulatee
return 'doublecircle'
if s > 0 and not p: # a regulator
return 'oval' # regulator
if p and s == 0: # a regulatee
return 'egg' # regulatee
return 'point'
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'] # darker colours means more important for that tissue
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: # no successor
return 'azure'
return colours[min(int(10 * 1.0 * d[tissue] / total), len(colours)-1)]
def write_graphviz_file(fname, edge_dict, colour_dict, agi2name_dict, query_tf):
f = open(fname, 'w')
graph_dict = {} # record for each tissue the graph
last_node = {} # record the last node added in each subgraph
for k in edge_dict:
graph_dict[k] = {'head':'', 'nodes':[], 'edges':[]}
for k in edge_dict: # k is tissue
node_added_dict = {} # make sure we don't add the same node twice
edge_added_dict = {} # make sure an edge is not added twice
tissue_node = '%s_node' % (k)
graph_dict[k]['head'] = ''
d = edge_dict[k] # d = {'tf1':[target1, target2, ...]}
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) # shape's boundary colour
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, ll, color, 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 not target in node_added_dict:
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'
last_node[k] = node_target
edge_key = tf + target
if not edge_key in edge_added_dict:
graph_dict[k]['edges'].append(' \"%s\" -> \"%s\" [color=%s];\n' % (node_tf, node_target, 'gold')) # out-going edge
edge_added_dict[edge_key] = 'YES'
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 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, ll, color, colour_dict[k], shape))
node_added_dict[tf] = 'YES'
last_node[k] = node_target
edge_key = tf + target
if not edge_key in edge_added_dict:
graph_dict[k]['edges'].append(' \"%s\" -> \"%s\" [color=%s];\n' % (node_tf, node_target, 'red'))
if graph_dict[k]['nodes'] != []:
node_label = k + '_label_node'
graph_dict[k]['nodes'].append(' \"%s\" [label=\"%s\", shape=box, color=yellow, style=filled, height=0.8, width=1.6];\n' % (node_label, k.upper()))
# write graphviz file
s0 = 'digraph G {\n graph[splines=true, ranksep=2, fontname=Arial];\n node[fontname=Arial];\n'
s0 += ' {rank=sink; ' # move label node to bottom
for k in last_node:
if graph_dict[k]['nodes'] != []:
node_label = k + '_label_node'
s0 += '%s;' % (node_label)
s0 += '}\n'
for k in graph_dict:
s0 += graph_dict[k]['head']
node_label = k + '_label_node'
for x in graph_dict[k]['nodes']:
s0 += x
for x in graph_dict[k]['edges']:
s0 += x
if k in last_node:
s0 += ' \"%s\" -> \"%s\" [arrowhead=none, style=invis];\n' % (last_node[k], node_label)
s0 += '}\n'
f.write(s0)
f.close()
# main
GENE_ID_TO_GENE_NAME = '/home/hui/network/v03/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
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:
print('Need to specifiy a gene ID, e.g., AT1G19850.')
sys.exit()
else:
query_tf = sys.argv[1]
edge_dict, edge_dict_r = get_edge(edge_file)
write_graphviz_file('result.gv', edge_dict, tissue_colour_dict, agi2name_dict, query_tf)
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