ageas.Plot()

This notebook demonstrate how to use ageas.Plot() to visualize GRNs or regulons reconstructed with key GRPs extracted with AGEAS.

import ageas

If visualization will be done in script after ageas.Launch() or ageas.Unit(), initialization Plot() object could be as simple as:

# ageas.Test() is a Launch() object using internalized data
# This would take a few minutes to run
test_launch_object = ageas.Test(cpu_mode = True)

# Here we use first network in atlas, network_0 for example
network = test_launch_object.atlas.nets[0]

Otherwise, we can load in networks from full_atlas.js or key_atalas.js output by ageas.Launch().

import ageas.tool.grn as grn
import ageas.tool.json as json

# Loading in networks from atlas file
# Here we use first network in atlas, network_0 for example
network = grn.GRN()
network.load_dict(
    json.decode(path = 'full_atlas.js')['network_0']
)

Then, a plotter object can be initialized as:

plotter = ageas.Plot(network = network,)

And graph of query network can be plotted and saved:

plotter.draw(
    legend_pos = (1.05, 0.3), # adjust legend position
    edge_width_scale = 1.0,   # adjust edge(GRP) width
)
plotter.save(path = 'test_plot.png', format = 'png')

Now, we should be able to get a network graph looks like:

Also, we can visualize GRPs by correlation strength calculated with class 1 samples or class 2 samples.

plotter = ageas.Plot(
    network = network,
    plot_class = 'class2',  # using mef data here. 'class1' will be ipsc data
    hide_bridge = False,    # unhide GRPs linking regulatory sources
)
plotter.draw(
    legend_pos = (1.15, 0.3), # adjust legend position
    edge_width_scale = 1.0,   # adjust edge(GRP) width
)
plotter.save(path = 'test_plot.png', format = 'png')

Output should looks like:

Surely, we can only visualize GRPs related to one specific gene of interest in order to avoid over-crowded graph.

plotter = ageas.Plot(
    network = network,
    hide_bridge = False,    # unhide GRPs linking regulatory sources
    root_gene = 'Klf4',     # Klf4 is our gene of interest
    depth = 1,              # and we are visualizing all GRPs directly linked with Klf4
)
plotter.draw(
    figure_size = 10,           # less GRPs here, smaller size would be better
    legend_pos = (1.3, 0.3),   # adjust legend position
    edge_width_scale = 2.0,     # adjust edge(GRP) width
)
plotter.save(path = 'test_plot_1.png', format = 'png')

And we shoud get something looks like: