The goal of bancr is to support analysis of the Brain And Nerve Cord data set (BANC), especially auto-segmentation data. Those data are made available by the BANC project led by Wei-Chung Allen Lee (Harvard) and collaborators including Zetta.ai and the FlyWire team at Princeton. Anyone can request access to the data here. Learn more on the BANC wiki.
To access banc resources, you must have permissions to access the banc autosegmentation dataset and have confirmed your acceptance of the banc proofreading and data ownership guidelines. At this point you should have a linked Google account that will be authorised (see below) for access to banc online resources.
Broadly speaking the bancr package is largely a wrapper over the fafbseg package setting up necessary default paths etc. It is based on another wrapper for a separate project, fancr.
If you have access, you can view BANC data in this helpful neuroglancer scene.
The BANC project uses CAVE tables to store many sorts of annotation information. You can see the available CAVE tables for BANC here.
You can install the development version of bancr from github:
remotes::install_github('flyconnectome/bancr')To do anything useful with the bancr package, you need authorisation to access banc resources. To prove your authorisation for programmatic access you must generate and store a token in your web browser after logging in to an approved Google account. This should be streamlined by running the following command in R (which will also set you up for Pythonic access via cloudvolume).
# set up token - will open your browser to generate a new token
banc_set_token()
# if you already have one do
# banc_set_token("<my token>")To check that everything is set up properly, try:
# diagnose issues
dr_banc()
# confirm functionality, should return FALSE
banc_islatest("720575941562355975")Some functions rely on underlying Python code by
Philipp Schlegel,
called using the reticulate package.
You can install full set of recommended libraries including fafbseg-py:
fafbseg::simple_python("full")
Note that this package is designed to play nicely with fafbseg, which has
been used mainly for the FAFB-FlyWire project, but could be used to work with
data from many neuroglancer/CAVE based projects.
If you get an error related to not finding cloud-volume or the cloud-volume version, the solution may be to update cloud-volume, as so:
fafbseg::simple_python('none', pkgs='cloud-volume~=8.32.1')Use with_banc() to wrap many additional fafbseg::flywire_* functions
for use with the BANC. Alternatively choose_banc() to set all
flywire_* functions from fafbseg to target the BANC. Not all functions
will work.
You can just repeat the install instructions, but this ensures that all dependencies are updated:
remotes::install_github('flyconnectome/bancr')If you need to update a specific Python library dependent, you can do:
fafbseg::simple_python(pkgs='fafbseg')First we need to load the package, and direct ourselves to the BANC data set.
library(bancr)
# choose_banc()Next, let us query a BANC CAVE table in order to get the neurons users have annotated as 'ascending' neurons, i.e. neurons that have their cell bodies and dendrites in the ventral nerve cord, and their axons in the brain.
banc.neck.connective.neurons <- banc_neck_connective_neurons()
head(banc.neck.connective.neurons)After considering these neurons, I have decided I would like to
plot two of them. They are both members of the same cell type. They are
identified by a 16-bit root_id.
an1.left <- "720575941566983162"
an1.right <- "720575941562355975"
This ID changes each time a neuron is edited, so while the BANC is an active project they are unstable. Likely by the time you read this, they have changed a little, although they describe the same cells.
Therefore, let us make sure we have the most up to date IDs.
an1.left <- banc_latestid(an1.left)
an1.right <- banc_latestid(an1.right)
an1.ids <- c(an1.left, an1.right)
an1.ids
Sometimes a more stable way to track a neuron (as long as it has a cell body
within the BANC volume) is to consider its nucleus_id.
We can get a table of nucleus ids from CAVE and find ours. The root_id
column in these CAVE tables automatically update.
banc.nuclei <- banc_nuclei()
banc.nuclei.an1 <- banc.nuclei[as.character(banc.nuclei$pt_root_id) %in% an1.ids,]
banc.nuclei.an1.ids <- as.character(banc.nuclei.an1$id)
banc.nuclei.an1
Great. Next, we want to read the mesh objects of our neurons.
an1.left.mesh <- banc_read_neuron_meshes(an1.left)
an1.right.mesh <- banc_read_neuron_meshes(an1.right)These neurons will be in 'BANC coordinates', in nanometers. They are read as
mesh3d objects which describe triangular meshes.
But we can also get proxy 'L2' skeletons from the segmentation graph for each neuron.
These functions depend on Philipp Schlegel's fafbseg-py library.
You can install this using fafbseg::simple_python. See above.
an1.left.skel <- banc_read_l2skel(an1.left)
an1.right.skel <- banc_read_l2skel(an1.right)We can also get mesh3d objects for our nuclei.
an1.left.nucleus <- banc_read_nuclei_mesh(banc.nuclei.an1.ids[1])
an1.right.nucleus <- banc_read_nuclei_mesh(banc.nuclei.an1.ids[2])We can plot our neurons in 3D using the rgl package.
First, we can plot the BANC volume mesh which shows all the brain tissue.
nopen3d()
banc_view()
plot3d(banc.surf, col = "lightgrey", alpha = 0.1)We can also see the synaptic neuropil inside of it.
plot3d(banc_neuropil.surf, col = "lightgrey", alpha = 0.25)And now our neurons, their skeletons and their nuclei.
# Plot neuron meshes
plot3d(an1.left.mesh, col = "coral", alpha = 0.75)
plot3d(an1.right.mesh, col = "chartreuse", alpha = 0.75)
# Plot neuron skeletons
plot3d(an1.left.skel, col = "darkred", alpha = 1)
plot3d(an1.right.skel, col = "darkgreen", alpha = 1)
# Plot nuclei meshes
plot3d(an1.left.nucleus, col = "black", alpha = 1, add = TRUE)
plot3d(an1.right.nucleus, col = "black", alpha = 1, add = TRUE)
We can also make a 2D image of multiple views using ggplot2.
# Simplify meshes to make plotting faster
banc_neuropil <- Rvcg::vcgQEdecim(as.mesh3d(banc_neuropil.surf), percent = 0.1)
banc_brain_neuropil <- Rvcg::vcgQEdecim(as.mesh3d(banc_brain_neuropil.surf), percent = 0.1)
banc_vnc_neuropil <- Rvcg::vcgQEdecim(as.mesh3d(banc_vnc_neuropil.surf), percent = 0.1)
an1.left.mesh.simp <- Rvcg::vcgQEdecim(an1.left.mesh[[1]], percent = 0.1)
an1.right.mesh.simp <- Rvcg::vcgQEdecim(an1.right.mesh[[1]], percent = 0.1)
# Plot! Saves as a PNG file
banc_neuron_comparison_plot(neuron1 = an1.left.mesh.simp,
neuron2 = an1.right.mesh.simp,
neuron1.info = "AN1_right",
neuron2.info = "AN1_left",
banc_neuropil = banc_neuropil,
banc_brain_neuropil = banc_brain_neuropil,
banc_vnc_neuropil = banc_vnc_neuropil,
filename = "banc_an_comparison_ggplot2.png?raw=true")
# Tip: You may need to hit 'zoom' on the RStudio plot pane, to see finer meshes,
# when filename = NULL.
Using a bridge to the symmetric 'template brain' (see below), we can 'mirror' neurons in BANC even though it is an asymmetric space.
Here we can see the normal (grey) and mirrored mesh (green). At the moment, this works less well in the VNC than the brain.
an1.left.skel.m <- banc_mirror(an1.left.skel, method = "tpsreg")
an1.right.skel.m <- banc_mirror(an1.right.skel, , method = "tpsreg")And now plot the mirrored skeletons, and the non-mirrored meshes for comparison:
# Set up 3D plot
nopen3d()
banc_view()
plot3d(banc_neuropil.surf, col = "lightgrey", alpha = 0.1)
# Plot native neuron meshes
plot3d(an1.left.mesh, col = "coral", alpha = 0.5)
plot3d(an1.right.mesh, col = "chartreuse", alpha = 0.5)
# Plot mirrored neuron skeletons
plot3d(an1.left.skel.m, col = "darkred", alpha = 1)
plot3d(an1.right.skel.m, col = "darkgreen", alpha = 1)
We can also change the view to see, for example, the brain more clearly.
banc_front_view()
Or the ventral nerve cord.
banc_vnc_view()
Jasper Phelps
has made a BANC-to-JRC2018F and JRC2018F-to-BANC transform using
the software Elastix.
Therefore, we can use this transform to move data
transformed first into JRC2018F, into the BANC. Or data out of the BANC,
into JRC2018 and then any other template brain to which JRC2018F can be
bridged.
We can either use the Elastix transform directly if you have Elastix installed
on your machine (implemented as method="elastix").
This can be a bit of a journey, so I have also implemented
a thin plate spine registration
that is based on the Elastix transform,
made and applied using the R package Morpho (implemented as method="tpsreg")
. The end result of the two methods can be very slightly different.
Firstly, let's just take the brain part of the ANs we have, as JRC2018F only
include the brain.
# Show only the portion in the brain
an1.mesh.simp <- neuronlist(an1.left.mesh.simp, an1.right.mesh.simp)
an1.mesh.simp.brain <- banc_decapitate(an1.mesh.simp, invert = TRUE)
# Convert to JRC2018F space
an1.mesh.simp.brain.jrc2018f <- banc_to_JRC2018F(an1.mesh.simp.brain,
method = "tpsreg",
banc.units = "nm")
# Plot in JRC2018 space
nopen3d()
plot3d(JRC2018F.surf, col = "lightgrey", alpha = 0.1)
plot3d(an1.mesh.simp.brain.jrc2018f, col = c("turquoise", "navy"), alpha = 0.75, add = TRUE)
We can now read a neuron from FAFB-FlyWire. I already know the ID of the comparable FAFB-FlyWire neurons to fetch.
We need to load a new R package first.
if (!requireNamespace("remotes")) install.packages("remotes")
remotes::install_github('natverse/nat.jrcbrains')
library(nat.jrcbrains)
## You may need to download the relevant registration, if you have not already:
# download_saalfeldlab_registrations()And then get known FAFB-FlyWire neurons.
## if you previously ran choose_banc()
## now run:
# choose_segmentation("flywire31")
# Which directs you towards the active FAFB-FlyWire segmentation
# Define the IDs we wish to fetch
# these are from the 783 materialisation (i.e.) published version
fw.an1.ids <- c("720575940626768442", "720575940636821616")
# Get neuron meshes
fw.an1.meshes <- read_cloudvolume_meshes(fw.an1.ids)
# Convert to JRC2018F
fw.an1.meshes.jrc2018f <- xform_brain(fw.an1.meshes, sample = "FAFB14",
reference = "JRC2018F")
# Add to plot
plot3d(fw.an1.meshes.jrc2018f, col = c("red","orange"), alpha = 1, add = TRUE)
We can do the same with the Hemibrain.
We need to load a new R package first.
if (!requireNamespace("remotes")) install.packages("remotes")
remotes::install_github('flyconnectome/hemibrainr')
library(hemibrainr)And now we can get the equivalent Hemibrain neuron.
# Read hemibrain neuron
hb.an1 <- "706176085"
# Read mesh, divide by 1000 to reach microns
hb.an1.mesh <- hemibrain_neuron_meshes(hb.an1)
# Transforms to JRC2018F, divide by 1000 to reach microns for JRCFIB2018F
hb.an1.mesh.jrc2018f <- xform_brain(hb.an1.mesh/1000, sample = "JRCFIB2018F", reference = "JRC2018F")
# Add to plot
plot3d(hb.an1.mesh.jrc2018f , col = c("chartreuse"), alpha = 1, add = TRUE)
Now we see all related neurons from three data sets in one space. Awesome!
We can also see the difference between the Elastix registration and the Morpho
based on.
You will first need to download and install Elastix. To do so, you can follow
the instructions here. Remember that the Elastix binaries must be on your
PATH and your system must be able to see its libraries. On MacOSX it is tricky
to just install and use the Elastix binaries directly,
you need instead to compile
ITK then Elastix yourself.
# Transform with Elastix
an1.mesh.simp.brain.jrc2018f.elastix <- banc_to_JRC2018F(an1.mesh.simp.brain,
method = "elastix",
banc.units = "nm")
# Plot in JRC2018 space
nopen3d()
plot3d(JRC2018F.surf, col = "lightgrey", alpha = 0.1)
plot3d(an1.mesh.simp.brain.jrc2018f, col = "blue", alpha = 0.75, add = TRUE)
plot3d(an1.mesh.simp.brain.jrc2018f.elastix, col = "green", alpha = 0.75, add = TRUE)an.upstream <- banc_partner_summary(an1.ids, partners = "input")
an.downstream <- banc_partner_summary(an1.ids, partners = "output")
# Combine the two data frames and add a source column
combined_data <- bind_rows(
mutate(an.upstream, source = "Upstream"),
mutate(an.downstream, source = "Downstream")
) %>%
dplyr::filter(weight>2)
# Create the histogram
ggplot(combined_data, aes(x = weight, fill = source)) +
geom_histogram(binwidth = 1, color = "black", alpha = 0.7, position = "identity") +
scale_fill_manual(values = c("Upstream" = "skyblue", "Downstream" = "orange")) +
labs(title = "histogram of weights: upstream vs downstream",
x = "Weight",
y = "Frequency",
fill = "Source") +
theme_minimal() +
theme(
plot.title = element_text(hjust = 0.5, size = 16, face = "bold"),
axis.title = element_text(size = 12),
axis.text = element_text(size = 10),
legend.position = "top"
)BANC data needs to be acknowledged in accordance to the BANC community guidelines and in agreement with the BANC consortium. If you use this package, you should cite the eventual BANC paper as well (TBD) as our natverse paper (Bates et al. 2020) and the R package as follows:
citation(package = "bancr")Bates A, Jefferis G (2024). bancr: R Client Access to the Brain And Nerve Cord (BANC) Dataset. R package version 0.1.0, https://github.com/flyconnectome/bancr.
The BANC data set was collected at Harvard Medical School in the laboratory of Wei-Chung Allen Lee, by Minsu Kim and Jasper Phelps. The segmentation and synapse prediction was built by Zetta.ai. The neuron reconstruction effort has been hosted and supported by FlyWire. This R package was initialised using the fancr package developed by Greg Jefferis at the MRC Laboratory of Molecular Biology, Cambridge. Alex Bates worked on this R package while in the laboratory of Rachel Wilson at Harvard Medical School.
Bates, Alexander Shakeel, James D. Manton, Sridhar R. Jagannathan, Marta Costa, Philipp Schlegel, Torsten Rohlfing, and Gregory SXE Jefferis. 2020. The Natverse, a Versatile Toolbox for Combining and Analysing Neuroanatomical Data. eLife 9 (April). https://doi.org/10.7554/eLife.53350.