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Summarise the connectivity of banc neurons

Source: R/partners.R
banc_partner_summary.Rd

Returns synaptically connected partners for specified neurons. Understanding synaptic partnerships is crucial for analyzing neural circuits in the Brain And Nerve Cord (BANC) connectome, revealing how distributed control architecture coordinates behaviour across brain and ventral nerve cord regions.

banc_partners returns details of each unitary synaptic connection (including its xyz location).

Usage

banc_partner_summary(
  rootids,
  partners = c("outputs", "inputs"),
  synapse_table = c("synapses_250226", "synapses_v1"),
  threshold = 0,
  remove_autapses = TRUE,
  cleft.threshold = 0,
  datastack_name = NULL,
  ...
)

banc_partners(
  rootids,
  partners = c("input", "output"),
  synapse_table = c("synapses_250226", "synapses_v1"),
  ...
)

Arguments

rootids

Character vector specifying one or more BANC rootids. As a convenience this argument is passed to banc_ids allowing you to pass in data.frames, BANC URLs or simple ids.

partners

Character vector, either "outputs" or "inputs" to specify the direction of synaptic connections to retrieve.

synapse_table

Character, the name of the synapse CAVE table you wish to use. Defaults to the latest.

threshold

Integer threshold for minimum number of synapses (default 0).

remove_autapses

Logical, whether to remove self-connections (default TRUE).

cleft.threshold

Numeric threshold for cleft filtering (default 0).

datastack_name

An optional CAVE datastack_name. If unset a sensible default is chosen.

...

Additional arguments passed to flywire_partner_summary

Value

a data.frame

Details

note that the rootids you pass in must be up to date. See example.

See also

flywire_partner_summary, banc_latestid

Examples

if (FALSE) { # \dontrun{
# Basic connectivity analysis
sample_id=banc_latestid("720575941478275714")
head(banc_partner_summary(sample_id))
head(banc_partner_summary(sample_id, partners='inputs'))

# Research application: Analyze descending neuron control circuits
library(dplyr)

# Get DNa02 descending neurons that control walking behavior
dna02_annotations <- banc_codex_annotations() %>%
  filter(cell_type == "DNa02")
dna02_id <- dna02_annotations$pt_root_id[1]

# Find their downstream targets in the VNC
dna02_outputs <- banc_partner_summary(dna02_id, partners='outputs') %>%
  slice_max(weight, n = 10)

# Visualize the circuit in neuroglancer
banc_partner_summary(sample_id, partners='inputs') %>%
  slice_max(weight, n = 20) %>%
  banc_scene(open=TRUE)
} # }
if (FALSE) { # \dontrun{
# plot input and output synapses of a neuron
nclear3d()
fpi=banc_partners(banc_latestid("720575941478275714"), partners='in')
points3d(banc_raw2nm(fpi$post_pt_position), col='cyan')
fpo=banc_partners(banc_latestid("720575941478275714"), partners='out')
points3d(banc_raw2nm(fpo$pre_pt_position), col='red')
} # }