data-raw/hemibrain/pnts/processAD1.R
In jefferislab/lhns: Skeleton data for neurons associated with the lateral horn of Drosophila melanogaster
#######
# AD1 #
#######
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("391716661", "5813096129", "424776680", "425121959", "424780893",
"424781115", "360314997", "511617404", "638472166", "574688277",
"5813035242", "5813047019", "573328325", "605732731", "574706585",
"605046401", "5813013202", "769199010", "762274975", "824349873",
"575384259", "705139000", "729867599", "611352588", "451995734",
"573683438", "606461310", "483017681", "5813052205", "5813022459",
"673426956", "544107335", "543321179", "574040939", "638136814",
"451382570", "517842578", "5813067728", "421957539", "455828904",
"513404289", "5901195496", "452336897", "551298257", "425471220",
"512765816", "544107243", "730562988", "5813095395", "456187010", "551989415", "543412198")
y = c("792326206", "327838570")
z = c("420956213", "392307898", "453350926", "390953189", "5901193642", "5813055865",
"485745927", "546472393", "577835762", "546127037", "577844302",
"453026933", "545786473", "639240544", "359210228")
w = c("420615275", "421983723", "359905678", "452651486", "359901157",
"734693858", "545440837", "576812462", "485430376", "760604568",
"514073078", "667132722", "454045389", "483716037", "575806223",
"454706490", "697132415",
"981547981", "328542009", "452323015", "5901193715",
"360932188", "452651021")
u = "697476780"
ad1 = c(x,y,z,w,u)
### 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)
# z.match = unique(hemibrain_lhns[z,"FAFB.match"])
# z.match = z.match[!is.na(z.match)]
# z.match = read.neurons.catmaid.meta(z.match)
# w.match = unique(hemibrain_lhns[w,"FAFB.match"])
# w.match = w.match[!is.na(w.match)]
# w.match = read.neurons.catmaid.meta(w.match)
# z.match = unique(hemibrain_lhns[z,"FAFB.match"])
# z.match = z.match[!is.na(z.match)]
# z.match = read.neurons.catmaid.meta(z.match)
#
# ### Meta info
# mx = neuprint_get_meta(x)
# my = neuprint_get_meta(y)
# mz = neuprint_get_meta(z)
# mw = neuprint_get_meta(w)
# table(mx$cellBodyFiber)
# table(my$cellBodyFiber)
# table(mz$cellBodyFiber)
# table(mw$cellBodyFiber)
# ### CBFs:
# ### ADL02^hLHT1 ADL03^hLHT3 ADL15^hLHT2 ADL21^hLHT4 ADL26^hLHT5
# ADL03 = neuprint_read_neurons("ADL03")
# ADL03 = ADL03[names(ADL03)%in%hemibrain.lhn.bodyids]
# ADL15 = neuprint_read_neurons("ADL15")
# ADL15 = ADL15[names(ADL15)%in%hemibrain.lhn.bodyids]
# ADL02 = neuprint_read_neurons("ADL02")
# ADL02 = ADL02[names(ADL02)%in%hemibrain.lhn.bodyids]
# ad1.hemi = c(ADL03,ADL15,ADL02)
#
# ### Re-define some of these CBFs
# sd = setdiff(ad1, names(ad1.hemi))
# ds = setdiff(names(ad1.hemi),ad1)
# ad1 = unique(ad1, names(ad1.hemi))
### Set-up data.frame
df = subset(namelist, bodyid %in% ad1)
df$cbf.change = FALSE
df$class = "LHN"
df$cell.type = NA
rownames(df) = df$bodyid
### Wrong CBF
# wrong1 = c("705139000","544107243", "730562988")
# df[wrong1,"cbf.change"] = "ADL15"
### Hemilineages:
df[x,"ItoLee_Hemilineage"] = "LHl2_medial"
df[x,"Hartenstein_Hemilineage"] = "DPLal2_medial"
df[z,"ItoLee_Hemilineage"] = "SLPal1"
df[z,"Hartenstein_Hemilineage"] = "DPLal1"
df[w,"ItoLee_Hemilineage"] = "SLPal2_ventral"
df[w,"Hartenstein_Hemilineage"] = "DPLal3_ventral"
df[y,"ItoLee_Hemilineage"] = "primary"
df[y,"Hartenstein_Hemilineage"] = "primary"
df[u,"ItoLee_Hemilineage"] = "primary"
df[u,"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_multi3d(a1, a2)
### Easily plot several of your candidate types at once
############
### AD1x ###
############
#####
# b #
#####
# b1 = c("5813095395", "456187010", "551989415", "543412198") # light = c("120726c3", "130726c1", "130731c1", "130910c0","140514c1")
# df[b1,"cell.type"] = "AD1b1"
# b3 = c("5813021223", "392645419", "5813012028") # light = c("120813c1", "120911c0")
# df[b3,"cell.type"] = "AD1b3"
# b5 = c("581652283", "583015749") # light = c("130731c1", "130729c0", "130801c1", "130802c0", "JJ37")
# df[b5,"cell.type"] = "AD1b5"
b1 = c("5813095395", "456187010", "551989415", "543412198")
df[b1,"cell.type"] = "AD1b1"
b2 = c("543321179", "574040939", "483017681", "573683438", "5813022459", "5813052205",
"544107243", "730562988", "544107335", "673426956",
"606461310", "769199010", "824349873", "5813013202", "762274975") # light = c("L1539#1", "120704c1", "120714c4", "JJ126", "JJ130", "JJ136", "JJ84")
df[b2,"cell.type"] = "AD1b2"
b3 = c("451382570", "5813047019","455828904") # light = "L1539#4"
df[b3,"cell.type"] = "AD1b3"
b4 = c("575384259","638136814")
df[b4,"cell.type"] = "AD1b4"
b5 = c("5813067728", "5901195496", "421957539", "517842578", "513404289") # light = c("120813c1")
df[b5,"cell.type"] = "AD1b5"
b6 = c("451995734", "611352588", "452336897", "425471220", "512765816") #light = c("120816c1") # "JJ26" # Change to AD1b1
df[b6,"cell.type"] = "AD1b6"
#####
# c #
#####
c1 = c("705139000","729867599")
df[c1,"cell.type"] = "AD1c1"
c2 = c("424776680", "424780893", "424781115", "425121959", "551298257") # light=c("Gad1-F-600330", "Gad1-F-400233", "120830c2", "JJ25", "JJ31","121129c0", "130711c3", "120913c1", "JJ129", "JJ132", "JJ20", "JJ33")
df[c2,"cell.type"] = "AD1c2"# light = c("L494#1", "L494#2","L475#1", "L475#2","Gad1-F-300262","Gad1-F-500014", "140612c0", "JJ27")
c3 = c("573328325", "605732731", "574706585", "605046401") # light = c("Gad1-F-700076")
df[c3,"cell.type"] = "AD1c3"
#####
# d #
#####
d1 = c("574688277", "5813035242", "511617404", "638472166") # light = c("L2446#1", "L2446#2")
df[d1,"cell.type"] = "AD1d1"
d2 = c("5813096129", "391716661", "360314997")
df[d2,"cell.type"] = "AD1d2"
#############
### AD1y ####
#############
#####
# h #
#####
g1 = "792326206" # light = "VGlut-F-200392"
df[g1,"cell.type"] = "AD1g1"
#####
# m #
#####
k1 = "327838570"
df[k1,"cell.type"] = "AD1k1"
############
### AD1z ###
############
# j1 = c("391971977", "5901195761", "298547259", "328883690", "422388958") # dead
# df[j1,"cell.type"] = "AD1j1"
j1 = "359210228"
df[j1,"cell.type"] = "AD1j1"
#####
# a #
#####
f1 = c("453350926", "392307898", "420956213") # light = c("Cha-F-100223", "VGlut-F-200319","Cha-F-300272","VGlut-F-400267","VGlut-F-600424","VGlut-F-700522", "130524c0","JJ42")
df[f1,"cell.type"] = "AD1f1"
f2 = c("5813055865") # light = c("Cha-F-100223") # Aspis in the EM
df[f2,"cell.type"] = "AD1f2"
f3 = c("453026933", "545786473", "639240544", "390953189", "5901193642") # light = c("VGlut-F-500156")
df[f3,"cell.type"] = "AD1f3"
f4 = c("546472393", "577835762", "546127037", "577844302") # light = c("Gad1-F-200033","Cha-F-700185")
df[f4,"cell.type"] = "AD1f4"
f5 = "485745927"
df[f5,"cell.type"] = "AD1f5"
#############
### AD1w ###
#############
#####
# i #
#####
i2 = c("981547981", "328542009", "452323015", "5901193715") # maybe
df[i2,"cell.type"] = "AD1i2"
i1 = c("359901157", "452651486", "359905678") # light = c("JJ50") # maybe
df[i1,"cell.type"] = "AD1i1"
#####
# e #
#####
# e1 = c("485430434", "855414220", "887148641", "641278400", "949534412", "610916994") #light = c("Gad1-F-300229","Cha-F-200360","fru-F-300009","Gad1-F-200365","L1614#1","L1827#3")
# df[e1,"cell.type"] = "aSP-g1"
#
# e2 = c("5813115796", "421992069", "485775679", "485430336", "329919036") # light = c("Gad1-F-700032", "fru-F-200156")
# df[e2,"cell.type"] = "aSP-g2"
#####
# a #
#####
a1 = c("454045389", "483716037", "575806223") # light = c("130123c1","130523c0", "130524c0","Cha-F-000258","L1740#3", "L1740#7", "L1740#8", "L1740#11","L1740#2","L1739#1","JJ113", "JJ114", "JJ63", "JJ67")
df[a1,"cell.type"] = "AD1a1"
a2 = c("545440837", "576812462", "734693858",
"760604568", "514073078", "485430376", "667132722") # light = c("Cha-F-400246","L1721#1", "L1721#3","Gad1-F-400257","140402c0", "121206c0", "140403c1","L1721#2","L1812#1","L1812#2")
df[a2,"cell.type"] = "AD1a2"
a3 = c("454706490", "697132415") # light = c("120925c1", "121127c1", "121122c1", "121128c3", "L1740#2")
df[a3,"cell.type"] = "AD1a3"
a4 = c("360932188", "452651021", "420615275", "421983723")
df[a4,"cell.type"] = "AD1a4"
########
# AD1z #
########
h1 = "697476780"
df[h1,"cell.type"] = "AD1h1"
########
# save #
########
# Organise cell types
df = process_types(df = df, hemibrain_lhns = hemibrain_lhns)
df[e1,"pnt"] = "LHAD1"
df[e2,"pnt"] = "LHAD1"
# Fix connectivity types
a= c("544107335", "673426956", "606461310", "769199010", "824349873",
"5813013202")
b = c("573683438", "483017681", "5813022459")
c= c("543321179", "574040939", "5813052205", "762274975")
d = c("730562988","544107243")
df[a,"connectivity.type"] = "LHAD1b2_a"
df[b,"connectivity.type"] = "LHAD1b2_b"
df[c,"connectivity.type"] = "LHAD1b2_c"
df[d,"connectivity.type"] = "LHAD1b2_d"
# Summarise results
state_results(df)
# Write .csv
write.csv(df, file = "data-raw/hemibrain/pnts/csv/AD1_celltyping.csv", row.names = FALSE)
# Process
if(process){
# Make 2D Images
take_pictures(df)
# Update googlesheet
write_lhns(df = df[c(a,b,c),], column = c("class", "pnt", "cell.type", "connectivity.type", "ItoLee_Hemilineage", "Hartenstein_Hemilineage"))
}
# a8 = c("329206628", "393340402", "480978811", "360954739", "421300612",
# "390590590", "297519736", "359227113", "359240144", "359926923",
# "5813055866", "5813010259","5813055907", "359219722") # light = "Gad1-F-400155"
# df[a8,"cell.type"] = "AD1a8"
# a7 = c("422669150", "453355233", "451663172", "452677002", "947820593") #light = c("Cha-F-400246","L1721#1", "L1721#3")
# df[a7,"cell.type"] = "AD1a7"
# i5 = c("359219503", "328524517", "5813085995")
# df[i5,"cell.type"] = "AD1i5"
# i4 = c("451960272")
# df[i4,"cell.type"] = "AD1i4"
# i3 = c("453350952", "454019960")
# df[i3,"cell.type"] = "AD1i3"
# a6 = c("515437073", "545441096","453376390")
# df[a6,"cell.type"] = "AD1a6"
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#######
# AD1 #
#######
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("391716661", "5813096129", "424776680", "425121959", "424780893",
"424781115", "360314997", "511617404", "638472166", "574688277",
"5813035242", "5813047019", "573328325", "605732731", "574706585",
"605046401", "5813013202", "769199010", "762274975", "824349873",
"575384259", "705139000", "729867599", "611352588", "451995734",
"573683438", "606461310", "483017681", "5813052205", "5813022459",
"673426956", "544107335", "543321179", "574040939", "638136814",
"451382570", "517842578", "5813067728", "421957539", "455828904",
"513404289", "5901195496", "452336897", "551298257", "425471220",
"512765816", "544107243", "730562988", "5813095395", "456187010", "551989415", "543412198")
y = c("792326206", "327838570")
z = c("420956213", "392307898", "453350926", "390953189", "5901193642", "5813055865",
"485745927", "546472393", "577835762", "546127037", "577844302",
"453026933", "545786473", "639240544", "359210228")
w = c("420615275", "421983723", "359905678", "452651486", "359901157",
"734693858", "545440837", "576812462", "485430376", "760604568",
"514073078", "667132722", "454045389", "483716037", "575806223",
"454706490", "697132415",
"981547981", "328542009", "452323015", "5901193715",
"360932188", "452651021")
u = "697476780"
ad1 = c(x,y,z,w,u)
### 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)
# z.match = unique(hemibrain_lhns[z,"FAFB.match"])
# z.match = z.match[!is.na(z.match)]
# z.match = read.neurons.catmaid.meta(z.match)
# w.match = unique(hemibrain_lhns[w,"FAFB.match"])
# w.match = w.match[!is.na(w.match)]
# w.match = read.neurons.catmaid.meta(w.match)
# z.match = unique(hemibrain_lhns[z,"FAFB.match"])
# z.match = z.match[!is.na(z.match)]
# z.match = read.neurons.catmaid.meta(z.match)
#
# ### Meta info
# mx = neuprint_get_meta(x)
# my = neuprint_get_meta(y)
# mz = neuprint_get_meta(z)
# mw = neuprint_get_meta(w)
# table(mx$cellBodyFiber)
# table(my$cellBodyFiber)
# table(mz$cellBodyFiber)
# table(mw$cellBodyFiber)
# ### CBFs:
# ### ADL02^hLHT1 ADL03^hLHT3 ADL15^hLHT2 ADL21^hLHT4 ADL26^hLHT5
# ADL03 = neuprint_read_neurons("ADL03")
# ADL03 = ADL03[names(ADL03)%in%hemibrain.lhn.bodyids]
# ADL15 = neuprint_read_neurons("ADL15")
# ADL15 = ADL15[names(ADL15)%in%hemibrain.lhn.bodyids]
# ADL02 = neuprint_read_neurons("ADL02")
# ADL02 = ADL02[names(ADL02)%in%hemibrain.lhn.bodyids]
# ad1.hemi = c(ADL03,ADL15,ADL02)
#
# ### Re-define some of these CBFs
# sd = setdiff(ad1, names(ad1.hemi))
# ds = setdiff(names(ad1.hemi),ad1)
# ad1 = unique(ad1, names(ad1.hemi))
### Set-up data.frame
df = subset(namelist, bodyid %in% ad1)
df$cbf.change = FALSE
df$class = "LHN"
df$cell.type = NA
rownames(df) = df$bodyid
### Wrong CBF
# wrong1 = c("705139000","544107243", "730562988")
# df[wrong1,"cbf.change"] = "ADL15"
### Hemilineages:
df[x,"ItoLee_Hemilineage"] = "LHl2_medial"
df[x,"Hartenstein_Hemilineage"] = "DPLal2_medial"
df[z,"ItoLee_Hemilineage"] = "SLPal1"
df[z,"Hartenstein_Hemilineage"] = "DPLal1"
df[w,"ItoLee_Hemilineage"] = "SLPal2_ventral"
df[w,"Hartenstein_Hemilineage"] = "DPLal3_ventral"
df[y,"ItoLee_Hemilineage"] = "primary"
df[y,"Hartenstein_Hemilineage"] = "primary"
df[u,"ItoLee_Hemilineage"] = "primary"
df[u,"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_multi3d(a1, a2)
### Easily plot several of your candidate types at once
############
### AD1x ###
############
#####
# b #
#####
# b1 = c("5813095395", "456187010", "551989415", "543412198") # light = c("120726c3", "130726c1", "130731c1", "130910c0","140514c1")
# df[b1,"cell.type"] = "AD1b1"
# b3 = c("5813021223", "392645419", "5813012028") # light = c("120813c1", "120911c0")
# df[b3,"cell.type"] = "AD1b3"
# b5 = c("581652283", "583015749") # light = c("130731c1", "130729c0", "130801c1", "130802c0", "JJ37")
# df[b5,"cell.type"] = "AD1b5"
b1 = c("5813095395", "456187010", "551989415", "543412198")
df[b1,"cell.type"] = "AD1b1"
b2 = c("543321179", "574040939", "483017681", "573683438", "5813022459", "5813052205",
"544107243", "730562988", "544107335", "673426956",
"606461310", "769199010", "824349873", "5813013202", "762274975") # light = c("L1539#1", "120704c1", "120714c4", "JJ126", "JJ130", "JJ136", "JJ84")
df[b2,"cell.type"] = "AD1b2"
b3 = c("451382570", "5813047019","455828904") # light = "L1539#4"
df[b3,"cell.type"] = "AD1b3"
b4 = c("575384259","638136814")
df[b4,"cell.type"] = "AD1b4"
b5 = c("5813067728", "5901195496", "421957539", "517842578", "513404289") # light = c("120813c1")
df[b5,"cell.type"] = "AD1b5"
b6 = c("451995734", "611352588", "452336897", "425471220", "512765816") #light = c("120816c1") # "JJ26" # Change to AD1b1
df[b6,"cell.type"] = "AD1b6"
#####
# c #
#####
c1 = c("705139000","729867599")
df[c1,"cell.type"] = "AD1c1"
c2 = c("424776680", "424780893", "424781115", "425121959", "551298257") # light=c("Gad1-F-600330", "Gad1-F-400233", "120830c2", "JJ25", "JJ31","121129c0", "130711c3", "120913c1", "JJ129", "JJ132", "JJ20", "JJ33")
df[c2,"cell.type"] = "AD1c2"# light = c("L494#1", "L494#2","L475#1", "L475#2","Gad1-F-300262","Gad1-F-500014", "140612c0", "JJ27")
c3 = c("573328325", "605732731", "574706585", "605046401") # light = c("Gad1-F-700076")
df[c3,"cell.type"] = "AD1c3"
#####
# d #
#####
d1 = c("574688277", "5813035242", "511617404", "638472166") # light = c("L2446#1", "L2446#2")
df[d1,"cell.type"] = "AD1d1"
d2 = c("5813096129", "391716661", "360314997")
df[d2,"cell.type"] = "AD1d2"
#############
### AD1y ####
#############
#####
# h #
#####
g1 = "792326206" # light = "VGlut-F-200392"
df[g1,"cell.type"] = "AD1g1"
#####
# m #
#####
k1 = "327838570"
df[k1,"cell.type"] = "AD1k1"
############
### AD1z ###
############
# j1 = c("391971977", "5901195761", "298547259", "328883690", "422388958") # dead
# df[j1,"cell.type"] = "AD1j1"
j1 = "359210228"
df[j1,"cell.type"] = "AD1j1"
#####
# a #
#####
f1 = c("453350926", "392307898", "420956213") # light = c("Cha-F-100223", "VGlut-F-200319","Cha-F-300272","VGlut-F-400267","VGlut-F-600424","VGlut-F-700522", "130524c0","JJ42")
df[f1,"cell.type"] = "AD1f1"
f2 = c("5813055865") # light = c("Cha-F-100223") # Aspis in the EM
df[f2,"cell.type"] = "AD1f2"
f3 = c("453026933", "545786473", "639240544", "390953189", "5901193642") # light = c("VGlut-F-500156")
df[f3,"cell.type"] = "AD1f3"
f4 = c("546472393", "577835762", "546127037", "577844302") # light = c("Gad1-F-200033","Cha-F-700185")
df[f4,"cell.type"] = "AD1f4"
f5 = "485745927"
df[f5,"cell.type"] = "AD1f5"
#############
### AD1w ###
#############
#####
# i #
#####
i2 = c("981547981", "328542009", "452323015", "5901193715") # maybe
df[i2,"cell.type"] = "AD1i2"
i1 = c("359901157", "452651486", "359905678") # light = c("JJ50") # maybe
df[i1,"cell.type"] = "AD1i1"
#####
# e #
#####
# e1 = c("485430434", "855414220", "887148641", "641278400", "949534412", "610916994") #light = c("Gad1-F-300229","Cha-F-200360","fru-F-300009","Gad1-F-200365","L1614#1","L1827#3")
# df[e1,"cell.type"] = "aSP-g1"
#
# e2 = c("5813115796", "421992069", "485775679", "485430336", "329919036") # light = c("Gad1-F-700032", "fru-F-200156")
# df[e2,"cell.type"] = "aSP-g2"
#####
# a #
#####
a1 = c("454045389", "483716037", "575806223") # light = c("130123c1","130523c0", "130524c0","Cha-F-000258","L1740#3", "L1740#7", "L1740#8", "L1740#11","L1740#2","L1739#1","JJ113", "JJ114", "JJ63", "JJ67")
df[a1,"cell.type"] = "AD1a1"
a2 = c("545440837", "576812462", "734693858",
"760604568", "514073078", "485430376", "667132722") # light = c("Cha-F-400246","L1721#1", "L1721#3","Gad1-F-400257","140402c0", "121206c0", "140403c1","L1721#2","L1812#1","L1812#2")
df[a2,"cell.type"] = "AD1a2"
a3 = c("454706490", "697132415") # light = c("120925c1", "121127c1", "121122c1", "121128c3", "L1740#2")
df[a3,"cell.type"] = "AD1a3"
a4 = c("360932188", "452651021", "420615275", "421983723")
df[a4,"cell.type"] = "AD1a4"
########
# AD1z #
########
h1 = "697476780"
df[h1,"cell.type"] = "AD1h1"
########
# save #
########
# Organise cell types
df = process_types(df = df, hemibrain_lhns = hemibrain_lhns)
df[e1,"pnt"] = "LHAD1"
df[e2,"pnt"] = "LHAD1"
# Fix connectivity types
a= c("544107335", "673426956", "606461310", "769199010", "824349873",
"5813013202")
b = c("573683438", "483017681", "5813022459")
c= c("543321179", "574040939", "5813052205", "762274975")
d = c("730562988","544107243")
df[a,"connectivity.type"] = "LHAD1b2_a"
df[b,"connectivity.type"] = "LHAD1b2_b"
df[c,"connectivity.type"] = "LHAD1b2_c"
df[d,"connectivity.type"] = "LHAD1b2_d"
# Summarise results
state_results(df)
# Write .csv
write.csv(df, file = "data-raw/hemibrain/pnts/csv/AD1_celltyping.csv", row.names = FALSE)
# Process
if(process){
# Make 2D Images
take_pictures(df)
# Update googlesheet
write_lhns(df = df[c(a,b,c),], column = c("class", "pnt", "cell.type", "connectivity.type", "ItoLee_Hemilineage", "Hartenstein_Hemilineage"))
}
# a8 = c("329206628", "393340402", "480978811", "360954739", "421300612",
# "390590590", "297519736", "359227113", "359240144", "359926923",
# "5813055866", "5813010259","5813055907", "359219722") # light = "Gad1-F-400155"
# df[a8,"cell.type"] = "AD1a8"
# a7 = c("422669150", "453355233", "451663172", "452677002", "947820593") #light = c("Cha-F-400246","L1721#1", "L1721#3")
# df[a7,"cell.type"] = "AD1a7"
# i5 = c("359219503", "328524517", "5813085995")
# df[i5,"cell.type"] = "AD1i5"
# i4 = c("451960272")
# df[i4,"cell.type"] = "AD1i4"
# i3 = c("453350952", "454019960")
# df[i3,"cell.type"] = "AD1i3"
# a6 = c("515437073", "545441096","453376390")
# df[a6,"cell.type"] = "AD1a6"
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