Read precomputed NBLASTs from the hemibrainr Google Drive

Source: R/hemibrain_googledrive.R

Read precomputed data available on the hemibrain Google Team Drive. This includes body IDs for all hemibrain neurons ((hemibrain_neuron_bodyids)), all synapses (hemibrain_synapses), neuron-neuron connections (hemibrain_connections) and an edgelist (hemibrain_elist) for all hemibrain neurons, broken down by axon and dendrite assignments. NBLAST matrices for all neurons against all neurons in the data set are also available, including ones broken down by neuron compartment.

hemibrain_nblast(
  nblast = c("hemibrain", "flywire", "hemibrain-flywire", "hemibrain-fafb14",
    "hemibrain-flycircuit", "flywire-mirror", "flywire-spine",
    "hemibrain-primary.neurites", "hemibrain-primary.dendrites", "hemibrain-axons",
    "hemibrain-dendrites", "hemibrain-spines", "hemibrain-tracts", "hemibrain-arbour",
    "hemibrain-simplified"),
  local = FALSE,
  fafbsegpy = TRUE
)

Arguments

nblast

the NBLAST matrix you would like to retrieve, e.g. "arbours" gives you a normalised all by all NBLAST matrix of all branching arbour. dotprops object.
local

FALSE or path. By default (FALSE) data is read from options()$remote_connectome_data), but the user can specify an alternative path.
fafbsegpy

logical. If TRUE the NBLASTs are fetched from Philipp Schlegel's nightly flywire NBLAST pipeline.

Value

a matrix with named rows and columns describing an NBLAST result.

Details

NBLASTs were made in JRCFIB2018F space. Hemibrain neurons first had their 'twigs' pruned:

fib.twigs5 = nlapply(all.neurons.flow, prune_twigs, twig_length=5000, .parallel = TRUE, OmitFailures = TRUE).

Where all.neurons.flow can be called with hemibrain_neurons. Neurons were moved from JRC2018Fraw to JRC2018F by:

all.neurons.flow.microns = hemibrainr:::scale_neurons.neuronlist(fib.twigs5, .parallel = TRUE, OmitFailures = TRUE)

For 'compartment' NBLASTs neurons' axons, dendrites, primary neurite tracts, etc., were extracted. E.g.

all.neurons.pnt = primary_neurite_cable(x = all.neurons.flow.microns, .parallel = TRUE, OmitFailures = TRUE) all.neurons.arbour = arbour_cable(x = all.neurons.flow.microns, .parallel = TRUE, OmitFailures = TRUE) all.neurons.tract = tract_cable(x = all.neurons.flow.microns, .parallel = TRUE, OmitFailures = TRUE)

For the 'simplified' hemibrain neuron NBLAST, the function nat::simplify_neuron was used as so:

all.neurons.simp = nat::nlapply(X = all.neurons.flow.microns, FUN = nat::simplify_neuron, n = 1, invert = FALSE, .parallel = TRUE, OmitFailures = TRUE)

Neurons, simplified neurons and compartments were turned into vector cloud using nat::dotprops as so:

hemibrain.microns.dps =dotprops(all.neurons.flow.microns, k=5, resample=1, .parallel=T, OmitFailures = T)

NBLASTs were run as so:

hemibrain.aba.mean=nat.nblast::nblast_allbyall(hemibrain.twigs5.dps, .progress='text',.parallel=TRUE, normalisation='mean')

NBLASTs were also run against flywire and flycircuit neurons. Flywire neurons were pulled from the flywire project as meshes and skeletonised using fafbseg::skeletor.

The code for this pipeline can be found as a flyconnectome GitHub repository

See also

hemibrain_splitpoints, hemibrain_flow_centrality, hemibrain_metrics, hemibrainr_googledrive_data

Examples

# \donttest{
if (FALSE) {

# A normalised neuron-neuron NBLASST for all hemibrain neurons
hemibrain.nblast = hemibrain_nblast(nblast = "hemibrain")

# A normalised neuron-neuron NBLASST for all hemibrain neurons's axons
hemibrain.axon.nblast = hemibrain_nblast(nblast = "hemibrain-axons")

# And for simplified hemibrain neurons
hemibrain.simp.nblast = hemibrain_nblast(nblast = "hemibrain-simplified")

}# }