Visualization¶

RISK provides a flexible and powerful plotting interface via the NetworkPlotter object. This enables generation of publication-ready figures with detailed domain annotations, significance-driven coloring, KDE-based contours, and rich label customization.

Create a Plotter¶

You must first load your NetworkGraph object into a NetworkPlotter.

plotter = risk.load_plotter(
    graph=graph,
    figsize=(15, 15),
    background_color="black",
    background_alpha=1.0,
    pad=0.3,
)

Core Plotting Methods¶

The following methods control different aspects of the visualization:

Method	Purpose
`plot_title()`	Add a title and subtitle overlay in the center of the network
`plot_circle_perimeter()`	Draw a circular boundary around the network layout for visual framing
`plot_contour_perimeter()`	Draw a KDE-based contour around the entire network layout perimeter
`plot_network()`	Render nodes and edges based on statistical significance, using size and color to reflect overrepresentation
`plot_subnetwork()`	Highlight a selected group of nodes in a different style (e.g., metabolic vs. ribosomal)
`plot_contours()`	Draw shaded KDE contours around clustered domains (e.g., GO term regions)
`plot_subcontour()`	Draw a KDE contour around a specific list of nodes or a small subgraph
`plot_labels()`	Automatically generate text labels and arrows for each domain or functional region
`plot_sublabel()`	Manually add a label and arrow for a user-defined node set
`savefig()`	Save the current figure as a SVG, PNG, PDF, etc.
`show()`	Display the current plot in a notebook or interactive window

Many of these methods support additional customization parameters beyond the examples shown below. For a full list, use help(plotter.method_name) in a Python environment or refer to the in-notebook documentation.

Example: Core Visualization Workflow¶

The following sequence illustrates the most commonly used core plotting methods in RISK. These can be combined to create clear, informative figures with minimal code.

Add a Title¶

plotter.plot_title(
    title="Yeast Genetic Interaction Network",
    subtitle="Colored by Overrepresentation Score",
    fontcolor="white",
    fontsize=20,
    subtitle_color="white",
)

Draw a Circle Perimeter¶

plotter.plot_circle_perimeter(
    radius=1.0,
    color="white",
    linewidth=2.0,
    linestyle="solid",
    alpha=1.0,
)

For a more flexible KDE-based layout boundary, see plot_contour_perimeter().

Plot the Annotated Network¶

The plot_network() method draws the full network similarly to networkx.draw, but with enhanced styling and layout control. It is the primary way to visualize domain-level significance on the graph.

While plot_network() can be used as-is, RISK provides helper functions such as get_annotated_node_sizes() and get_annotated_node_colors() to automatically generate node styles based on annotation significance. These outputs plug directly into plot_network().

node_sizes = plotter.get_annotated_node_sizes(
    significant_size=200,
    nonsignificant_size=10,
)

node_colors = plotter.get_annotated_node_colors(
    cmap="gist_rainbow",
    blend_colors=False,
    blend_gamma=2.2,
    min_scale=0.8,
    max_scale=1.0,
    scale_factor=0.5,
    alpha=1.0,
    nonsignificant_color="white",
    nonsignificant_alpha=0.75,
    random_seed=888,
)

plotter.plot_network(
    node_size=node_sizes,
    node_color=node_colors,
    node_shape="o",
    node_edgewidth=1.0,
    edge_width=0.04,
    node_edgecolor="black",
    edge_color="white",
    node_alpha=1.0,
    edge_alpha=1.0,
)

You can also use plotter.get_annotated_contour_colors() and plotter.get_annotated_label_colors() for consistent domain-based color themes across contours and labels.

Highlight a Subnetwork¶

Use plot_subnetwork() to overlay a custom set of nodes with distinct styling—useful for emphasizing known complexes or pathway members.

plotter.plot_subnetwork(
    nodes=["RPL1A", "RPL2B", "RPL3", "RPL5"],
    color="white",
    size=220,
    alpha=0.8,
)

Domain Overlays with KDE Contours¶

You can highlight functionally overrepresented domains using KDE-based contours.

All domains:¶

plotter.plot_contours(
    levels=5,
    bandwidth=0.8,
    grid_size=250,
    color=plotter.get_annotated_contour_colors(...),
    linestyle="solid",
    linewidth=2.0,
    alpha=1.0,
    fill_alpha=0.25,
)

Subsets of interest:¶

plotter.plot_subcontour(
    nodes=["LSM1", "LSM2", "LSM3", "PAT1"],
    color="white",
    levels=5,
    fill_alpha=0.25,
)

Domain Labels and Arrows¶

Label all domains¶

plotter.plot_labels(
    font="Arial",
    fontsize=15,
    fontcase={"title": "lower"},
    fontcolor="white",
    fontalpha=1.0,
    arrow_color=plotter.get_annotated_label_colors(...),
    arrow_style="-",
    arrow_alpha=1.0,
    max_labels=28,
    min_label_lines=3,
    max_label_lines=4,
    min_chars_per_line=3,
    max_chars_per_line=12,
    words_to_omit=["from", "the", "into", "via", "process"],
)

Add a custom label¶

plotter.plot_sublabel(
    nodes=["LSM1", "LSM2", "PAT1"],
    label="LSM1-7-PAT1 Complex",
    radial_position=73,
    scale=1.6,
    offset=0.12,
    fontcolor="white",
    fontsize=15,
)

Export and Display¶

Save your figure¶

plotter.savefig("my_plot.png", dpi=300)

Display the figure in a notebook/script¶

plotter.show()

GO BP PPI Network

Next Step¶

You’re now ready to export your results or continue exploring 7. Advanced Plotting features like background layers, dynamic coloring, and multi-panel layouts.