In natverse/hemibrainr: Code for Working with Data from Janelia FlyEM's Hemibrain Project and the flywire data sets``

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

This markdown examines some neurons that have 'mixed' predicted transmitters. Prediction error or biological quirk?

LNO1

# Load libraries
library(hemibrainr)
library(fafbseg)

# These are the flywire IDs of LNO1
LNO1.xyz = matrix(c(135467, 51625, 2367, 136541, 50845, 2280), ncol = 3, byrow = TRUE)
LNO1 = unique(flywire_xyz2id(LNO1.xyz,rawcoords = TRUE))

# Let us get all of the flywire neurons with synapses
fw.neurons = flywire_neurons(WithConnectors = TRUE, zip = TRUE)

# Subset these to just the LNO neurons
LNO1.flow = fw.neurons[names(fw.neurons)%in%LNO1]
LNO1.flow = as.neuronlist(LNO1.flow)

# Get meshes
LNO1.meshes = fafbseg::read_cloudvolume_meshes(LNO1)

# Plot together
plot3d_split(LNO1.flow, transmitters = TRUE, soma = FALSE, transmitter.alpha = 1)
plot3d(LNO1.meshes, col = "grey", alpha = 0.1)

# Get synapse data
LNO1.synapses = hemibrain_extract_synapses(LNO1.flow, .parallel = FALSE, OmitFailures = FALSE)
LNO1.synapses.outputs = subset(LNO1.synapses, prepost == 0 & Label!="dendrite")

# Cluster by transmitter
LNO1.select = aggregate(list(count = LNO1.synapses.outputs$prepost),list(pre_id = LNO1.synapses.outputs$pre_id,
                                                    top_nt = LNO1.synapses.outputs$top_nt,
                                                    post_id = LNO1.synapses.outputs$post_id),
                 length)
LNO1.select.strong = subset(LNO1.select, count >= 5)
conn.mat = reshape2::acast(data = LNO1.select.strong,
                     formula = top_nt ~ post_id , # pre ~ post, to use bodyids instead of cells
                     fun.aggregate = sum,
                     value.var = "count", # for raw synapses, change to count
                     fill = 0)
ComplexHeatmap::Heatmap(conn.mat)

Run from scratch

We can do the same thing without using the precomputed data. It takes a little longer, but we can do this like so:

# Load the libraries we need
library(hemibrainr)
library(fafbseg)
choose_segmentation('flywire')

# We need to use some python modules to skeletonise meshes
## Install them with this R code
fafbseg::simple_python("full")

# Get all the flywire meta data
fw.meta = flywire_meta(sql=FALSE)

# These are the flywire IDs of LNO1
LNO1 = c("720575940631591157", "720575940620581780")

# Get meshes
LNO1.meshes = fafbseg::read_cloudvolume_meshes(LNO1)

# Let us get the skeletons 
LNO1.skels = fafbseg::skeletor(LNO1)
### If this crashes, follow these instructions instead: http://natverse.org/fafbseg/articles/articles/installing-cloudvolume-meshparty.html
### Or cheat and just get the skeletons from the drive:  LNO1.skels = subset(flywire_neurons(), root_id %in% LNO1)

# This points to a location on your computer where you have the synapse SQL database 
fafbsynapses = "/Volumes/nnautilus/projects/JanFunke"
## Google drive location is: hemibrain/fafbsynapses/
## Here I am using an external SSD as that is faster

# Now let us attach the synapses with prediction
LNO1.syn = flywire_neurons_add_synapses(LNO1.skels,
                                        cleft.threshold = 50,
                                        transmitters = TRUE,
                                        local = fafbsynapses,
                                        Verbose = TRUE,
                                        OmitFailures = TRUE,
                                        .parallel = FALSE)

# Now split into axon and dendrite
LNO1.flow = hemibrainr::flow_centrality(LNO1.syn, mode = "centrifugal", polypre = TRUE, split = "synapses", .parallel = FALSE, OmitFailures = TRUE)

# Plot together
plot3d_split(LNO1.flow, transmitters = TRUE)
plot3d(LNO1.meshes, col = "grey", alpha = 0.1)

# Get synapse data
LNO1.synapses = hemibrain_extract_synapses(LNO1.flow, .parallel = FALSE, OmitFailures = FALSE)
LNO1.conns = hemibrain_extract_connections(LNO1.flow, .parallel = FALSE, OmitFailures = FALSE, meta = fw.meta)

il3LN1

And now for Istvan's weird antennal lobe LNs

# These are the flywire IDs of il3LN1
il3LN1.xyz = matrix(c(123158, 81767, 2697, 135901, 84928, 2642), ncol = 3, byrow = TRUE)
il3LN1 = unique(flywire_xyz2id(il3LN1.xyz,rawcoords = TRUE))

# Let us get all of the flywire neurons with synapses
fw.neurons = flywire_neurons(WithConnectors = TRUE, zip = TRUE)

# Subset these to just the il3LN neurons
il3LN1.flow = fw.neurons[names(fw.neurons)%in%il3LN1]
il3LN1.flow = as.neuronlist(il3LN1.flow)

# Get meshes
il3LN1.meshes = fafbseg::read_cloudvolume_meshes(il3LN1)

# Plot together
plot3d_split(il3LN1.flow, transmitters = TRUE, soma = FALSE, transmitter.alpha = 1)
plot3d(il3LN1.meshes, col = "grey", alpha = 0.1)

# Get synapse data
il3LN1.synapses = hemibrain_extract_synapses(il3LN1.flow, .parallel = FALSE, OmitFailures = FALSE)
il3LN1.synapses.outputs = subset(il3LN1.synapses, prepost == 0)

Get all of these interesting synapses

interesting = c("720575940631591157", "720575940620581780", "720575940638426064", "720575940609123794")
partners = flywire_partners(interesting, local = fafbsynapses, details = TRUE)
partners = subset(partners, prepost == 0 & pre_id %in% interesting)
ntp = npred = flywire_ntpred(partners)
nts = fafbseg:::ntpredictions_tbl(local = fafbsynapses)
interesting.syns <- partners %>%
      dplyr::inner_join(nts, by = "offset", copy = TRUE) %>%
      dplyr::filter(prepost==0, pre_id %in% interesting) %>%
      dplyr::collect()
interesting.syns$cell_type = "LNO1"
interesting.syns$cell_type[interesting.syns$pre_id%in%il3LN1] = "il3LN1"

natverse/hemibrainr documentation built on Nov. 27, 2024, 9:01 p.m.