Note
Click here to download the full example code
Manipulate Morphology#
This tutorial will give you an impression of how to process and manipulate your neurons' morphology.
See the API reference for a complete list of available functions.
As you might imagine some manipulations (e.g. smoothing or simplification) will work on all/most neuron types while others will only work on specific types. For example rerooting only makes sense on a navis.TreeNeuron.
The rule of thumb is this: if a function is called e.g. downsample_neuron it should work with multiple, if not all, neuron types while specialized functions will be called e.g. reroot_skeleton. So depending on what data you are working with and what you want to get out of it, you might have to explicitly convert between neuron types. See the respective function's docstring for details!
Rerooting#
navis.TreeNeurons are hierarchical trees and as such typically have a single "root" node (fragmented neurons will have multiple roots). The root is important because it is used as the reference/origin for a bunch of analyses such as Strahler order. Typically, you want the root to be the soma. Because the root is so important, TreeNeuron can be rerooted:
import navis
n = navis.example_neurons(1, kind="skeleton")
print(n.soma)
Out:
4177
.soma returns the node ID of the soma (if there is one) and can be used to reroot
navis.reroot_skeleton(n, n.soma, inplace=True)
Note
The root is implicitly also important for navis.MeshNeuron because we're using their skeleton representations for a couple operations/analyses!
Simplifying#
If you work with large lists of neurons you may want to downsample/simplifiy before e.g. trying to plot them. This is one of the things that - in principle work - with all neuron types. The implementation, however, depends on the neuron type. Lookup the respective function's help (e.g. via the API) for details.
For TreeNeurons downsampling means skipping N nodes (here 10):
sk = navis.example_neurons(n=1, kind="skeleton")
print(sk.n_nodes)
Out:
4465
sk_downsampled = navis.downsample_neuron(sk, downsampling_factor=10, inplace=False)
print(sk_downsampled.n_nodes)
Out:
1304
For MeshNeurons downsampling will reduce the number of faces by a factor of N:
me = navis.example_neurons(n=1, kind="mesh")
print(me.n_faces)
Out:
13054
me_downsampled = navis.downsample_neuron(me, downsampling_factor=10, inplace=False)
print(me_downsampled.n_faces)
Out:
1459
Note
Under the hood downsample_neuron calls navis.simplify_mesh for MeshNeurons. That function then requires one of the supported backends for mesh operations to be installed: Blender 3D, pymeshlab or open3d. If none is available, it will prompt you to install one.
Resampling#
TreeNeurons can also be "resampled" (up or down) to a given resolution (i.e. distance between nodes):
sk = navis.example_neurons(n=1, kind="skeleton")
print(sk.sampling_resolution * sk.units)
Out:
477.4501679731243 nanometer
Note that we can provide a unit ("1 micron") here because our neuron has units set:
sk_resampled = navis.resample_skeleton(sk, resample_to="1 micron", inplace=False)
print(sk_resampled.sampling_resolution * sk_resampled.units)
Out:
1072.2341259298619 nanometer
Let's visualize what we did there:
import matplotlib.pyplot as plt
nodes_original = sk.nodes[["x", "y", "z"]].values
nodes_downsampled = sk_downsampled.nodes[["x", "y", "z"]].values
nodes_resampled = sk_resampled.nodes[["x", "y", "z"]].values
fig, axes = plt.subplots(1, 3, figsize=(18, 5))
_ = navis.plot2d(
nodes_original,
method="2d",
view=("x", "-z"),
scatter_kws=dict(c="blue"),
ax=axes[0],
)
_ = navis.plot2d(
nodes_resampled,
method="2d",
view=("x", "-z"),
scatter_kws=dict(c="red"),
ax=axes[1],
)
_ = navis.plot2d(
nodes_downsampled,
method="2d",
view=("x", "-z"),
scatter_kws=dict(c="green"),
ax=axes[2],
)
for ax, title in zip(axes, ["Original", "Resampled to 1um", "Downsampled 10x"]):
ax.set_title(title, color="k")
ax.invert_yaxis()
ax.set_axis_off()
plt.tight_layout()
Tip
Click on the image to see it in full resolution.
As you can see the resampling increased the node density in the backbone and decreased it in the finer neurites to bring things on par. Downsampling just thinned out the nodes across the board.
Important
Resampling has a caveat you need to be aware of: nodes are not merely moved around to match the desired resolution - they are regenerated from scratch. As consequence, the original node IDs are - with a few exceptions - all gone.
Smoothing#
Smoothing is one of those things that work on all neurons but the approaches are so vastly different that there are separate functions: navis.smooth_skeleton, navis.smooth_mesh and navis.smooth_voxels:
# smooth_skeleton uses a rolling window along the linear segments
sk = navis.example_neurons(n=1, kind="skeleton")
sk_smoothed = navis.smooth_skeleton(sk, window=5, inplace=False)
# smooth_mesh uses a iterative rounds of Laplacian smoothing
me = navis.example_neurons(n=1, kind="mesh")
me_smoothed = navis.smooth_mesh(me, iterations=5, inplace=False)
Out:
/opt/hostedtoolcache/Python/3.10.15/x64/lib/python3.10/site-packages/jupyter_client/connect.py:22: DeprecationWarning:
Jupyter is migrating its paths to use standard platformdirs
given by the platformdirs library. To remove this warning and
see the appropriate new directories, set the environment variable
`JUPYTER_PLATFORM_DIRS=1` and then run `jupyter --paths`.
The use of platformdirs will be the default in `jupyter_core` v6
/opt/hostedtoolcache/Python/3.10.15/x64/lib/python3.10/site-packages/dash/_jupyter.py:29: DeprecationWarning:
The `ipykernel.comm.Comm` class has been deprecated. Please use the `comm` module instead.For creating comms, use the function `from comm import create_comm`.
Cutting & Pruning#
Cutting and pruning work best if there is a sense of topology which implicitly requires a skeleton. Many functions will also work on MeshNeurons though. That's because the operation is performed on their skeleton and changes are propagated back to the mesh. Fair warning though: this may not be perfect (e.g. the resulting mesh might not be watertight) - should be good enough for a first pass though!
Let's start with something easy: cutting a skeleton in two at a given node.
# Load the neuron
n = navis.example_neurons(1, kind="skeleton")
# Pick a node ID
cut_node_id = n.nodes.node_id.values[333]
distal, proximal = navis.cut_skeleton(n, cut_node_id)
Plot the two fragments:
# Note that we are using method='2d' here because that makes annotating the plot easier
fig, ax = distal.plot2d(color="cyan", method="2d", view=("x", "-z"))
fig, ax = proximal.plot2d(color="green", ax=ax, method="2d", view=("x", "-z"))
# Annotate cut point
cut_coords = distal.nodes.set_index("node_id").loc[distal.root, ["x", "z"]].values[0]
ax.annotate(
"cut point",
xy=(cut_coords[0], -cut_coords[1]),
color="lightgrey",
xytext=(cut_coords[0], -cut_coords[1] - 2000),
va="center",
ha="center",
arrowprops=dict(shrink=0.1, width=2, color="lightgrey"),
)
plt.tight_layout()
If instead of a node ID, you have an x/y/z coordinate where you want to cut: use the .snap method to find the closest node to that location:
node_id, dist = n.snap([14000, 16200, 12000])
print(f"Closest node: {node_id} at distance {dist * n.units:.2f} {n.units.units}")
Out:
Closest node: 334 at distance 565.69 nanometer nanometer
Instead of cutting a neuron in two, we can also just prune bits off:
n_pruned = n.prune_distal_to(cut_node_id, inplace=False)
cut_coords = n.nodes.set_index("node_id").loc[cut_node_id, ["x", "z"]].values
# Plot original neuron in red and with dotted line
fig, ax = n.plot2d(color="red", method="2d", linestyle=(0, (5, 10)), view=("x", "-z"))
# Plot remaining neurites in red
fig, ax = n_pruned.plot2d(color="green", method="2d", ax=ax, view=("x", "-z"), lw=1.2)
# Annotate cut point
ax.annotate(
"cut point",
xy=(cut_coords[0], -cut_coords[1]),
color="lightgrey",
xytext=(cut_coords[0], -cut_coords[1] - 2000),
va="center",
ha="center",
arrowprops=dict(shrink=0.1, width=2, color="lightgrey"),
)
plt.tight_layout()
navis.cut_skeleton also takes multiple cut nodes, in case you want to chop your neuron into multiple pieces.
As an (extreme) example, let's cut a neuron at every single branch point:
n = navis.example_neurons(1, kind="skeleton")
branch_points = n.nodes[n.nodes.type == "branch"].node_id.values
cut = navis.cut_skeleton(n, branch_points)
cut.head()
# Plot neuron fragments
fig, ax = navis.plot2d(cut, linewidth=1.5, view=("x", "-z"))
plt.tight_layout()
Out:
Plot neurons: 0%| | 0/600 [00:00<?, ?it/s]
Plot neurons: 25%|##4 | 148/600 [00:00<00:00, 1474.53it/s]
Plot neurons: 49%|####9 | 296/600 [00:00<00:00, 1468.09it/s]
Plot neurons: 74%|#######4 | 445/600 [00:00<00:00, 1474.74it/s]
Plot neurons: 99%|#########8| 593/600 [00:00<00:00, 1472.54it/s]
Let's try something more sophisticated: pruning by Strahler index:
# Load a fresh skeleton
n = navis.example_neurons(1, kind="skeleton")
# Reroot to soma
n = n.reroot(n.soma)
# This will prune off terminal branches (the lowest two Strahler indices)
n_pruned = n.prune_by_strahler(to_prune=[1, 2], inplace=False)
# Plot original neurons in red
fig, ax = n.plot2d(color="red", view=('x', '-z'))
# Plot remaining neurites in green
fig, ax = n_pruned.plot2d(color="green", ax=ax, linewidth=1, view=("x", "-z"))
plt.tight_layout()
We can also turn this around and remove only the higher order branches. Let's use this example to show that we can also do this with MeshNeurons:
Load an example mesh neuron
m = navis.example_neurons(1, kind="mesh")
# This will prune to the just terminal branches
m_pruned = navis.prune_by_strahler(m, to_prune=range(3, 100), inplace=False)
# Plot original neuron in cyan
fig, ax = m.plot2d(color="cyan", figsize=(10, 10), view=("x", "-z"))
# Plot remaining neurites red
fig, ax = m_pruned.plot2d(color="red", ax=ax, view=("x", "-z"))
plt.tight_layout()
Out:
/opt/hostedtoolcache/Python/3.10.15/x64/lib/python3.10/site-packages/skeletor/skeletonize/wave.py:198: DeprecationWarning:
Graph.clusters() is deprecated; use Graph.connected_components() instead
/opt/hostedtoolcache/Python/3.10.15/x64/lib/python3.10/site-packages/skeletor/skeletonize/wave.py:228: DeprecationWarning:
Graph.shortest_paths() is deprecated; use Graph.distances() instead
Alternatively, we can prune terminal branches based on size:
# This will prune all branches smaller than 10 microns
m_pruned = navis.prune_twigs(m, size="10 microns", inplace=False)
# Plot original neuron in red
fig, ax = m.plot2d(color="red", figsize=(10, 10), view=("x", "-z"))
# Plot remaining neurites in cyan
fig, ax = m_pruned.plot2d(
color="cyan", ax=ax, linewidth=0.75, alpha=0.5, view=("x", "-z")
)
plt.tight_layout()
Intersecting with Volumes#
We can also intersect neurons with navis.Volume (and trimesh.Trimesh for that matter). This is useful if you want to e.g. subset a neuron to a certain brain region. Let's see how this works:
Load an example navis.Volume
lh = navis.example_volume("LH")
# Prune by volume
m_lh = navis.in_volume(m, lh, inplace=False)
m_outside_lh = navis.in_volume(m, lh, mode="OUT", inplace=False)
And plot!
Plot pruned branchs neuron in green
fig, ax = navis.plot2d(
[m_lh, m_outside_lh, lh], color=["red", "green"], figsize=(10, 10), view=("x", "-z")
)
plt.tight_layout()
Does this work with all neuron types? There is no simple answer unfortunately. In theory, anything that works on skeletons should also work on meshes, and vice versa. However, navis.Dotprops navis.VoxelNeuron are so fundamentally different that certain operations just don't make sense. For example we can't cut them but we can subset them to a given volume. Check out the I/O API reference docs for an overview of what works with which neuron type.
# Note that [`navis.in_volume`][] also works with arbitrary spatial data (i.e. `(N, 3)` arrays of x/y/z locations):
Get the connectors for one of our above skeletons
cn = sk.connectors
# Add a column that tells us which connectors are in the LH volume
cn["in_lh"] = navis.in_volume(cn[["x", "y", "z"]].values, lh)
cn.head()
Count the number of connectors (pre and post) in- and outside the LH:
cn.groupby(["type", "in_lh"]).size()
Out:
type in_lh
post False 1978
True 106
pre False 325
True 296
dtype: int64
About half the presynapses are in the LH (most of the rest will be in the MB calyx). The large majority of postsynapses are outside the LH in the antennal lobe where this neuron has its dendrites.
That's it for now! Please see the NBLAST tutorial for morphological comparisons using NBLAST and the API reference for a full list of morphology-related functions.
Total running time of the script: ( 0 minutes 17.941 seconds)
Download Python source code: tutorial_morpho_00_manipulate.py
Download Jupyter notebook: tutorial_morpho_00_manipulate.ipynb