Below are brief descriptions of the libraries/packages. For details, I defer to their respective (excellent) documentations.
In R, the natverse is your one-stop-shop for all things
neuron: it’s a collection of various R packages that are built on top of
the neuroanatomy toolbox,
nat. Of particular relevance for
natis a general purpose library for working with morphological neuron data. In this workshop, we make heavy use of
nat’s plotting capabilities but its capabilities extend far beyond that. If you want to run any morphological analysis, I highly recommend you have a look at the “Articles” in nat’s doc.
hemibrainrprovide an interface with neuprint and the Janelia hemibrain dataset (link). The former lets you run queries against neuprint’s neo4j database while the latter contains meta data and various convenience functions to work with the hemibrain dataset.
rcatmaidprovides an interface with CATMAID servers such as those the VFB uses to host published from the FAFB or larval fruit fly dataset.
rcatmaidis built on top of
natand you can use
natfunctions with neurons pulled via
In Python, we find packages analogous to those in R:
nat’s serpentine sibling: a general purpose neuron library for visualization and analysis of neuronal morphologies. It also features interfaces e.g. with Blender 3D and the
python-neuprintis a Python library to interface with neuprint maintained by Janelia. Note that
naviswraps this library and adds some convenience functions. See this tutorial.
pymaidlets you interface with CATMAID servers. Critically, it’s built on top of
navisand you can natively use
pymaidneurons. Note that due to a name clash the library is called
There are a few more packages/functions that you might hear of over the course of the workshop.
NBLAST is an algorithm that computes morphological similarity between neurons (Costa et al., 2016). This has proven incredibly useful to find similar neurons across datasets but also to cluster neurons into cell types.
You will note that neurons pulled from VFB are typically in the same
template space which makes co-visualization of neurons from different
datasets a breeze. If you want to transform spatial data between
template brains, e.g. from FAFB (“FAFB14”) to hemibrain (“JRCFIB2018F”), you
should look for
nat.jrcbrains in R and
navis-flybrains in Python.
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