Neuron Topology#

This example demonstrates how to plot neurons' topology using various different layouts.

Skeletons in NAVis are hierarchical trees (hence the name TreeNeuron). As such they can be visualized as dendrograms or flat, graph-like plots using navis.plot_flat. This is useful to unravel otherwise complicated, overlapping branching patterns - e.g. when you want to show compartments or synapse positions within the neuron. Let's demo some layouts!

Subway Maps#

First up: the subway layout. This one is nice for side-by-side comparisons of neurons.

import navis
import matplotlib.pyplot as plt

# Load example neurons
nl = navis.example_neurons(n=4, kind="skeleton")

# Convert example neurons from voxels to nanometers
nl_nm = nl.convert_units("nm")

# Reroot to soma
nl_nm[nl_nm.soma != None].reroot(nl_nm[nl_nm.soma != None].soma, inplace=True)

# Generate one axis for each neuron
fig, axes = plt.subplots(3, 1, figsize=(15, 10), sharex=True)

navis.plot_flat(nl_nm[0], layout="subway", connectors=True, ax=axes[0])
navis.plot_flat(nl_nm[1], layout="subway", connectors=True, ax=axes[1])
navis.plot_flat(nl_nm[3], layout="subway", connectors=True, ax=axes[2])

# Clean up the axes
for ax in axes[:-1]:
    ax.set_axis_off()

for sp in ["left", "right", "top"]:
    axes[-1].spines[sp].set_visible(False)
axes[-1].set_yticks([])

_ = axes[-1].set_xlabel("distance [nm]")
plt.tight_layout()

Important

For the other layouts you will need to have pygraphviz and the underlying graphviz library installed. For details on the layout, check out the graphviz docs.

Dendrograms#

dot and twopi are dendrogram layouts. They (should) preserve branch lengths similar to subway:

ax = navis.plot_flat(nl_nm[0], layout="dot", connectors=True, color="k")
plt.tight_layout()

n = nl_nm[0]
ax = navis.plot_flat(n, layout="twopi", connectors=True, color="k")
plt.tight_layout()

You can also highlight specific connectors by their ID (here we just use the first 100):

highlight = n.connectors.connector_id[:100]

ax = navis.plot_flat(
    nl_nm[0],
    layout="dot",
    highlight_connectors=highlight,
    color="k",
    syn_marker_size=2,
)
plt.tight_layout()

Force-Directed Layouts#

neato, fdp and sfdp are force-directed layouts.

Some layouts (like neato & fdp) can be quite expensive to calculate in which case it's worth downsampling your neuron before plotting

ds = navis.downsample_neuron(nl_nm[0], 10, preserve_nodes="connectors")

ax = navis.plot_flat(ds, layout="neato", connectors=True, color="k")
plt.tight_layout()

ax = navis.plot_flat(nl[0], layout="sfdp", connectors=True, color="k")
plt.tight_layout()

Total running time of the script: ( 0 minutes 35.846 seconds)

Download Python source code: plot_03_plotting_dend.py

Download Jupyter notebook: plot_03_plotting_dend.ipynb

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