Aldonza-et-al._BMC-Biology_Fig-1A

import numpy as np
import csv
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import networkx as nx

# Classify drugs
EGFR = {}
IGFR = {}
RTK = {}
cytoskeleton = {}

with open("datasets/signaling_input.csv", newline="") as csvfile:
	reader = csv.DictReader(csvfile)
	for row in reader:
		if row['Action'] == "targeted":
			if row['Targeted process/pathway'] == "EGFR signaling":
				EGFR[row['Name']] = 0.
			elif row['Targeted process/pathway'] == "IGFR signaling":
				IGFR[row['Name']] = 0.
			elif row['Targeted process/pathway'] == "RTK signaling":
				RTK[row['Name']] = 0.
		if row['Targeted process/pathway'] == "cytoskeleton":
			cytoskeleton[row['Name']] = 0.

all_dicts = {**EGFR, **IGFR, **RTK, **cytoskeleton}
pair_dict = {}

for key1, value1 in all_dicts.items():
	for key2, value2 in all_dicts.items():
		if (key1 != key2) and ((key2, key1) not in pair_dict.keys()):
			pair_dict[(key1,key2)] = 0.

with open("datasets/resistant_sensitive.csv", newline="") as csvfile:
	reader = csv.DictReader(csvfile)
	for row in reader:
		for drug in all_dicts.keys():
			if row[drug] == "R":
				all_dicts[drug] += 1.

		for drug_pair in pair_dict.keys():
			if (row[drug_pair[0]] == "R") and (row[drug_pair[1]] == "R"):
				pair_dict[drug_pair] += 1.

# Fix node positions
all_pos = {}
y_ref = 10
x = 0
y = 10

for category in [EGFR, IGFR, RTK]:
	for key in category.keys():
		all_pos[key] = (x,y)
		y += 30
		if y > 150:
			if y_ref == 10:
				y_ref = 0
			else:
				y_ref = 10
			y = y_ref
			x += 30

y_ref = 60
x = 230
y = 60

for key in cytoskeleton.keys():
	all_pos[key] = (x,y)

	y += 30

	if y > 120:
		if y_ref == 60:
			y_ref = 50
		else:
			y_ref = 60
		y = y_ref

		x += 30

# Perform computation
for key, value in pair_dict.items():
	pair_dict[key] = (value - min(pair_dict.values())) / (max(pair_dict.values()) - min(pair_dict.values()))
	
for key, value in pair_dict.items():
	if pair_dict[key] <= 0.5:
		pair_dict[key] = 0.3
	elif pair_dict[key] > 0.5 and pair_dict[key] <= 0.8:
		pair_dict[key] = 0.8
	elif pair_dict[key] > 0.8:
		pair_dict[key] = 1.5

for_edge_color = {}
for key, value in pair_dict.items():
	if pair_dict[key] == 0.3:
		for_edge_color[key] = "lightgrey"
	elif pair_dict[key] == 0.8:
		for_edge_color[key] = "darkgray"
	elif pair_dict[key] == 1.5:
		for_edge_color[key] = "dimgray"

for_node_color = {}
for key in all_dicts.keys():
	if key in cytoskeleton.keys():
		for_node_color[key] = "orange"
	else:
		for_node_color[key] = "turquoise"

G = nx.Graph()

G.add_nodes_from(list(all_dicts.keys()))

for key in pair_dict.keys():
	G.add_edge(key[0], key[1])

node_size = []
node_color = []
edge_width = []
edge_color = []

for node in G.nodes():
	node_size.append(all_dicts[node])
	node_color.append(for_node_color[node])

for edge in G.edges():
	edge_width.append(pair_dict[edge])
	edge_color.append(for_edge_color[edge])

nx.draw(G, node_size=node_size, node_color=node_color, with_labels=True, width=edge_width, pos=all_pos, edgecolors='black', font_size=8, edge_color=edge_color)
plt.savefig("images/signaling/signaling_image.png", dpi=1200)
plt.close()