Cell cycle arrest

This example visualises protein protein interactions involved in the “cell cycle arrest” pathway.

It also shows how to use iplotx to combine network plots with other matplotlib chart types.

import numpy as np
import pandas as pd
import networkx as nx
import matplotlib.pyplot as plt
import iplotx as ipx


node_data = pd.read_csv("data/cell_cycle_arrest_string_network_coordinates.tsv", sep="\t", index_col=0)
edge_data = pd.read_csv("data/cell_cycle_arrest_string_interactions_short.tsv", sep="\t")

g = nx.Graph()
g.add_edges_from(edge_data.iloc[:, :2].values)

layout = node_data[["x_position", "y_position"]]
node_data["color_ipx"] = [list(map(lambda x: float(x) / 256.0, x[4:-1].split(","))) for x in node_data["color"]]
node_data["label_color"] = ["white" if np.mean(x) < 0.4 else "black" for x in node_data["color_ipx"]]


fig = plt.figure(figsize=(9, 6))
ax = plt.subplot2grid((2, 3), (0, 0), colspan=2, rowspan=2)
ax2 = plt.subplot2grid((2, 3), (0, 2))
ax3 = plt.subplot2grid((2, 3), (1, 2))
ipx.network(
    g,
    layout=layout,
    ax=ax,
    vertex_labels=True,
    vertex_marker="r",
    vertex_size="label",
    vertex_facecolor=node_data["color_ipx"].to_dict(),
    vertex_label_color=node_data["label_color"].to_dict(),
    vertex_edgecolor="k",
    vertex_linewidth=0.5,
    edge_alpha=0.2,
    vertex_zorder=3,

)

degrees = np.bincount(list(dict(g.degree()).values()))
bindegs = np.arange(len(degrees))
ax2.bar(bindegs, degrees, color="gray")
ax2.set_xlabel("Node degree")
ax2.set_ylabel("Number of\nproteins")

pagerank = list(nx.pagerank(g).values())
ax3.ecdf(pagerank, color="black", complementary=True)
ax3.axvline(0.1, ls="--", lw=2, color="tomato")
ax3.set_xlabel("Node PageRank")
ax3.set_ylabel("Cumulative")

fig.tight_layout()