Transformations#

This tutorial will show you how to transform and mirror neurons.

Introduction#

As of version 0.5.0, NAVis includes functions that let you transform and mirror spatial data (e.g. neurons). This new functionality splits into high- and low-level functions. In this tutorial, we will start by exploring the higher-level functions that most users will use and then take a sneak peak at the low-level functions.

At the moment, navis supports the following transform types:

• CMTK warp transforms
• Hdf5 deformation fields
• Elastix transforms
• landmark-based thin-plate spline transforms
• affine transforms

flybrains#

Since NAVis brings the utility but does not ship with any transforms, we have to either generate those ourselves or get them elsewhere. Here, we will showcase the flybrains library that provides a number of different transforms directly to NAVis. Setting up and registering your own custom transforms will be discussed further down.

First, you need to get flybrains. Please follow the instructions to install and download the bridging registrations before you continue.

import flybrains

Importing flybrains automatically registers the transforms with NAVis. This in turn allows NAVis to plot a sequence of bridging transformations to map between any connected template spaces.

In addition to those bridging transforms, flybrains also contains mirror registrations (we will cover those later), meta data and meshes for the template brains:

# This is the Janelia "hemibrain" template brain
print(flybrains.JRCFIB2018F)

Out:

Template brain
--------------
Name: JRCFIB2018F
Short Name: JRCFIB2018F
Type: None
Sex:  F
Dimensions: 34432 x 39552 x 41408 voxels
Voxel size:
  x = 8 nanometers
  y = 8 nanometers
  z = 8 nanometers
Bounding box (nanometers):
  x = 0, y = 0, z = 0,
  x = 275456, y = 316416, z = 331264,
Description: Nanometer-calibrated version of Janelia FIB hemibrain dataset.
DOI: https://doi.org/10.1101/2020.01.21.911859

import navis
import matplotlib.pyplot as plt

# This is the hemibrain neuropil surface mesh
fig, ax = navis.plot2d(flybrains.JRCFIB2018F, view=("x", "-z"))
plt.tight_layout()

You can check the registered transforms like so:

navis.transforms.registry.summary()

# !!! note
#     The documentation is built in an environment with a minimal number of transforms registered. If you have installed
#     and imported `flybrains`, you should see a lot more than what is shown above.

Using xform_brain#

For high-level transforming, you will want to use navis.xform_brain. This function takes a source and target argument and tries to find a bridging sequence that gets you to where you want. Let's try it out:

# Load the example hemibrain neurons (JRCFIB2018raw space)
nl = navis.example_neurons()
nl

fig, ax = navis.plot2d([nl, flybrains.JRCFIB2018Fraw], view=("x", "-z"))
plt.tight_layout()

Let's say we want these neurons in JRC2018F template space. Before we do the actual transform it's useful to quickly check above bridging graph to see what we expect to happen:

Have a look at the bridging graph above: first, we know that we are starting in JRCFIB2018Fraw space. From there, it's two simple affine transforms to go from voxels to nanometers and from nanometers to micrometers. Once we are in JRCFIB2018Fum space, we can use a Hdf5 transform generated by the Saalfeld lab to map to JRC2018F. Note that the arrows in the bridging graph indicate the transforms' forward directions but they can all be inversed to traverse the graph.

Let's give this a shot:

xf = navis.xform_brain(nl, source="JRCFIB2018Fraw", target="JRC2018F")

Out:

Transform path: JRCFIB2018Fraw -> JRCFIB2018F -> JRCFIB2018Fum -> JRC2018F

Painless, wasn't it? Let's see if it worked:

Plot the transformed neurons and the JRC2018F template brain

fig, ax = navis.plot2d([xf, flybrains.JRC2018F], color="r", view=("x", "-y"))
plt.tight_layout()

That worked like a charm! I highly recommend you read through the documentation for navis.xform_brain and check out the parameters you can use to fine-tune it.

Using mirror_brain#

Another useful type of transform is mirroring using navis.mirror_brain to e.g. mirror neurons from the left to the right side of a given brain. The way this works is this:

1. Reflect coordinates about the midpoint of the mirror axis (affine transformation)
2. Apply a warping transformation to compensate for e.g. left/right asymmetries

For the first step, we need to know the length of the mirror axis. This is why - similar to having registered transforms - we need to have meta data about the template space (i.e. the bounding box) available to NAVis.

The second step is optional. For example, JRC2018F and JRC2018U are templates generate from averaging multiple fly brains and are therefore already mirror symmetrical, meaning we don't need the additional warping transform. flybrains does include some mirror transforms though: e.g. for FCWB, VNCIS1 or JFRC2!

Since our neurons are already in JRC2018F space, let's try mirroring them:

mirrored = navis.mirror_brain(xf, template="JRC2018F")

fig, ax = navis.plot2d(
    [xf, mirrored, flybrains.JRC2018F], color=["r"] * 5 + ["g"] * 5, view=("x", "-y")
)
plt.tight_layout()

Out:

Plot neurons:   0%|          | 0/10 [00:00<?, ?it/s]

Plot neurons:  10%|#         | 1/10 [00:00<00:04,  1.89it/s]

Plot neurons:  20%|##        | 2/10 [00:01<00:04,  1.65it/s]

Plot neurons:  30%|###       | 3/10 [00:01<00:03,  1.76it/s]

Plot neurons:  40%|####      | 4/10 [00:02<00:03,  1.74it/s]

Plot neurons:  50%|#####     | 5/10 [00:02<00:03,  1.66it/s]

Plot neurons:  60%|######    | 6/10 [00:03<00:02,  1.71it/s]

Plot neurons:  70%|#######   | 7/10 [00:04<00:01,  1.66it/s]

Plot neurons:  80%|########  | 8/10 [00:04<00:01,  1.69it/s]

Plot neurons:  90%|######### | 9/10 [00:05<00:00,  1.68it/s]

Plot neurons: 100%|##########| 10/10 [00:05<00:00,  1.73it/s]

Perfect! As noted above, this only works if the template is registered with NAVis and if it contains info about its bounding box. If you only have the bounding box at hand but no template brain, check out the lower level function navis.transforms.mirror.

Low-level functions#

Adding your own transforms#

Let's assume you want to add your own transforms. There are four different transform types:

• navis.transforms.affine.AffineTransform
• navis.transforms.cmtk.CMTKtransform
• navis.transforms.h5reg.H5transform
• navis.transforms.thinplate.TPStransform

To show you how to use them, we will create a new thin plate spline transform using TPStransform. If you look at the bridging graph again, you might note the "FAFB14" template brain: it stands for "Full Adult Fly Brain" (the 14 is a version number for the alignment). We will use landmarks to generate a mapping between this 14th and the previous 13th iteration.

First we will grab the landmarks from the Saalfeld's lab elm repository:

import pandas as pd

# These landmarks map betweet FAFB (v14 and v13) and a light level template
# We will use only the v13 and v14 landmarks
landmarks_v14 = pd.read_csv(
    "https://github.com/saalfeldlab/elm/raw/master/lm-em-landmarks_v14.csv", header=None
)
landmarks_v13 = pd.read_csv(
    "https://github.com/saalfeldlab/elm/raw/master/lm-em-landmarks_v13.csv", header=None
)

# Name the columns
landmarks_v14.columns = landmarks_v13.columns = [
    "label",
    "use",
    "lm_x",
    "lm_y",
    "lm_z",
    "fafb_x",
    "fafb_y",
    "fafb_z",
]

landmarks_v13.head()

Now we can use those landmarks to generate a thin plate spine transform:

from navis.transforms.thinplate import TPStransform

tr = TPStransform(
    landmarks_source=landmarks_v14[["fafb_x", "fafb_y", "fafb_z"]].values,
    landmarks_target=landmarks_v13[["fafb_x", "fafb_y", "fafb_z"]].values,
)
# note: navis.transforms.MovingLeastSquaresTransform has similar properties

The transform has a method that we can use to transform points but first we need some data in FAFB14 space:

# Transform our neurons into FAFB 14 space
xf_fafb14 = navis.xform_brain(nl, source="JRCFIB2018Fraw", target="FAFB14")

Out:

Transform path: JRCFIB2018Fraw -> JRCFIB2018F -> JRCFIB2018Fum -> JRC2018F -> FAFBum = FAFB14um -> FAFB14

Now let's see if we can use the v14v13 transform:

# Transform the nodes of the first two neurons
pts_v14 = xf_fafb14[:2].nodes[["x", "y", "z"]].values
pts_v13 = tr.xform(pts_v14)

Quick check how the v14 and v13 coordinates compare:

# Original in black, transformed in red
fig, ax = navis.plot2d(pts_v14, scatter_kws=dict(c="k"), view=("x", "-y"))
_ = navis.plot2d(pts_v13, scatter_kws=dict(c="r"), ax=ax, view=("x", "-y"))

So that did... something. To be honest, I'm not sure what to expect for the FAFB 1413 transform but let's assume this is correct and move on.

Next, we will register this new transform with NAVis so that we can use it with higher level functions:

# Register the transform
navis.transforms.registry.register_transform(
    tr, source="FAFB14", target="FAFB13", transform_type="bridging"
)

Now that's done we can use FAFB13 with navis.xform_brain:

# Transform our neurons into FAFB 14 space
xf_fafb13 = navis.xform_brain(xf_fafb14, source="FAFB14", target="FAFB13")

Out:

Transform path: FAFB14 -> FAFB13

fig, ax = navis.plot2d(xf_fafb14, c='k', view=("x", "-y"))
_ = navis.plot2d(xf_fafb13, c='r', ax=ax)

Registering Template Brains#

For completeness, lets also have a quick look at registering additional template brains.

Template brains are represented in navis as navis.transforms.templates.TemplateBrain and there is currently no canonical way of constructing them: you can associate as much or as little data with them as you like. However, for them to be useful they should have a name, a label and a boundingbox property.

Minimally, you could do something like this:

# Construct template brain from base class
my_brain = navis.transforms.templates.TemplateBrain(
    name="My template brain",
    label="my_brain",
    boundingbox=[[0, 100], [0, 100], [0, 100]],
)

# Register with navis
navis.transforms.registry.register_templatebrain(my_brain)

# Now you can use it with mirror_brain:
import numpy as np

pts = np.array([[10, 10, 10]])
pts_mirrored = navis.mirror_brain(pts, template="my_brain")

# Plot the points
fig, ax = plt.subplots()
ax.scatter(pts[:, 0], pts[:, 1], c="k", alpha=1, s=50, label="Original")
ax.scatter(
    pts_mirrored[:, 0], pts_mirrored[:, 1], c="r", alpha=1, s=50, label="Mirrored"
)
ax.legend()

Out:

<matplotlib.legend.Legend object at 0x7fd5c2b1d150>

While this is a working solution, it's not very pretty: for example, my_brain does have the default docstring and no fancy string representation (e.g. for print(my_brain)). I highly recommend you take a look at how flybrains constructs and packages the templates.

Acknowledgments#

Much of the transform module is modelled after functions written by Greg Jefferis for the natverse. Likewise, flybrains is a port of data collected by Greg Jefferis for nat.flybrains and nat.jrcbrains.

Total running time of the script: ( 1 minutes 55.186 seconds)

Download Python source code: plot_01_transforms.py

Download Jupyter notebook: plot_01_transforms.ipynb

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