data-raw/hemibrain/pnts/processCENT.R

In jefferislab/lhns: Skeleton data for neurons associated with the lateral horn of Drosophila melanogaster

#######
# PV5 #
#######
if(!exists("process")){
  source("data-raw/hemibrain/startupHemibrain.R")
  process = TRUE
}

# First read all LHNs in the related cell body fibres
### Use plot3d(), nlscan() and find.neuron() to choose IDs.

# Groups
x = c("511271574",
         "360284300",
         "579912201",
         "5813068669",
         "517506265",
         "330268940",
         "328861282",
         "487144598",
         "327499164",
         "5813020988",
      "639585968",
      "329897255",
      "329225149",
      "608922563",
      "328533761")

### Get FAFB assigned hemilineage information
# x.match = unique(hemibrain_lhns[x,"FAFB.match"])
# x.match = x.match[!is.na(x.match)]
# x.match = read.neurons.catmaid.meta(x.match)
#
# ### Meta info
# mx = neuprint_get_meta(x)
# table(mx$cellBodyFiber)

### Set-up data.frame
df = subset(namelist, bodyid %in% x)
df$cbf.change = FALSE
df$class = "CENT"
df$cell.type = NA
rownames(df) = df$bodyid

### Hemilineages:
df[x,"ItoLee_Hemilineage"] = "primary"
df[x,"Hartenstein_Hemilineage"] = "primary"

##############################
# Make and review cell types #
##############################
# hemibrain_type_plot(bodyids = a, meta = lh.meta, someneuronlist = db);plot3d(most.lhns.hemibrain[light],col="black")
### Plot your candidate type alongside the equivalent Ito groupings
# hemibrain_milti3d(a1, a2)
### Easily plot several of your candidate types at once

############
### Cent ###
############

al = "608922563"
df[al,"cell.type"] = "AL-MBDL1"

c7 = c("328533761")
df[c7,"cell.type"] = "CENT7"

c8 = c("511271574",
       "360284300")
df[c8,"cell.type"] = "CENT8"

c5 = c("579912201")
df[c5,"cell.type"] = "CENT5"

c6 = c("5813068669")
df[c6,"cell.type"] = "CENT6"

c4 = c("517506265")
df[c4,"cell.type"] = "CENT4"

c9 = c("330268940")
df[c9,"cell.type"] = "CENT9"

c1 = c("328861282")
df[c1,"cell.type"] = "CENT1"

c3 = c("487144598")
df[c3,"cell.type"] = "CENT3"

c2 = c("327499164")
df[c2,"cell.type"] = "CENT2"

c10 = c("329225149", "329897255")
df[c10,"cell.type"] = "CENT10"

c11 = "639585968"
df[c11,"cell.type"] = "CENT11"

lhmb1 = c("5813020988")
df[lhmb1,"cell.type"] = "LHMB1"

########
# save #
########

# Organise cell types
df = process_types(df = df, hemibrain_lhns = hemibrain_lhns)
df$pnt = #pnt_cbf[match(df$cbf,pnt_cbf$cbf),"pnt"]
df[lhmb1,"pnt"] = "LHPD2"
df[c11,"pnt"] = "LHAD5"
df[c7,"pnt"] = "LHPV1"
df[al,"pnt"] = ""
df$published = TRUE

# Summarise results
state_results(df)

# Write .csv
write.csv(df, file = "data-raw/hemibrain/pnts/csv/CENT_celltyping.csv", row.names = FALSE)

# Process
if(process){
  # Update googlesheet
  write_lhns(df = df, column = c("class", "pnt", "cell.type", "ItoLee_Hemilineage", "Hartenstein_Hemilineage"))

  # Make 2D Images
  take_pictures(df)
}