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fafbseg-catmaid-legacy-info

Source: vignettes/fafbseg-catmaid-legacy-info.Rmd
fafbseg-catmaid-legacy-info.Rmd

This vignette includes information about interacting with FAFB catmaid and Google autosegmentation services which is now principally of historical interest.

Neuroglancer URLs

You can use the package to generate URLs pointing to a defined location in a neuroglancer dataset. This includes arbitrary locations in the FAFB dataset specified interactively or using CATMAID URLs. For example, we could find the location referenced in this tweet:

library(fafbseg)
# First, optionally set a package option with an example neuroglancer URL for your dataset.
# This URL will define the image layers that are visible. You need to use an
# URL for which you have access. The package ships with a default using segmentation
# fafb_v14:fafb_v14_16nm_v00c_split3xfill2 which is probably what you need.
options(fafbseg.sampleurl="https://<neuroglancerlurl>")

# Now open location specified by CATMAID URL
u='https://fafb.catmaid.virtualflybrain.org/?pid=2&zp=131280&yp=170014.98879622458&xp=426584.81386896875&tool=navigator&sid0=2&s0=-1'
open_fafb_ngl(u)

# or display raw coordinates that you can paste into active Neuroglancer session
open_fafb_ngl(u, coords.only = TRUE)

You can use the same approach for https://flywire.ai URLs with the additional option of transforming the coordinates to get closer to the correct location in the FlyWire assembly of the FAFB data (which is typically a few hundred nm off). See flywire2fafb() for details.

# opens a new browser tab
with_segmentation('flywire31', 
  open_fafb_ngl(u, sample = 'FAFB14', reference = "FlyWire"))

# writes coordinates to clipboard so that you can paste them into an existings
# flywire neuroglancer browser tab
with_segmentation('flywire31', 
  clipr::write_clip(
    open_fafb_ngl(u, coords.only = TRUE, 
      sample = 'FAFB14', reference = "FlyWire")))

CATMAID URLs

You can also go in the opposite direction to convert a neuroglancer location into a CATMAID URL. The neuroglancer location can come either from a URL or the JSON scene specification obtained by clicking on the {} icon.

# round trip using the CATMAID URL we used earlier
ngu = open_fafb_ngl(u, open = FALSE)

# now open using elmr package - installed like so if necessary: 
# natmanager::install(pkgs="natverse/elmr")
elmr::open_fafb(ngl_decode_scene(ngu))

Shiny application

The package now includes a simple Shiny application that translates between the two URL schemes. You can use an online version of the app at

https://jefferislab.shinyapps.io/CATMAID-Neuroglancer-Converter/

or download from GitHub and run locally the latest version of the app:

if (!require("shiny")) install.packages("shiny")
shiny::runGitHub("natverse/fafbseg", subdir = "inst/app/")

Currently the application does not convert FlyWire<->FAFB coordinates, but this would be a relatively simple addition using flywire2fafb().

Analysis of neuroglancer meshes

Currently the package provides functionality to read neuroglancer meshes into R and then compare such meshes with traced neuron objects e.g. from CATMAID. For this you will need to authenticate with an appropriate API. Alternatively see the article Capturing neuroglancer meshes from a browser scene.

You can read in meshes for the FlyWire segmentation of FAFB by doing (roughly)

choose_segmentation(release = 'flywire31')
open_fafb_ngl(c(109459, 41305, 5424)*c(4,4,40))
va6pn=read_cloudvolume_meshes("720575940633169983")
# compare with VFB CATMAID
library(elmr)
va6pn.skel=read.neurons.catmaid("name:VA6.*PN")
# let's take a look at those
plot3d(va6pn, col='grey', lwd=0.5, type='wire')
# only plot first neuron on RHS
plot3d(va6pn.skel[1], lwd=3, col='red')

# can also transform the FAFB14 skeleton from VFB to FlyWire coordinates
va6pn.skel.fw <- xform_brain(va6pn.skel[1], reference = 'FlyWire', sample='FAFB14')
nclear3d()
plot3d(va6pn, col='grey', lwd=0.5, type='wire')
# only plot first neuron on RHS
plot3d(va6pn.skel.fw, lwd=3, col='red')

You can read in meshes for the Google brain segmentation of FAFB by doing (roughly)

choose_segmentation(release = '20190805')
open_fafb_ngl(c(109459, 41305, 5424)*c(4,4,40))
va6pn=read_brainmaps_meshes("710435991")