Subset neuronlist returning either new neuronlist or names of chosen neurons

Subset neuronlist returning either new neuronlist or names of chosen neurons

# S3 method for neuronlist
subset(
  x,
  subset,
  filterfun,
  rval = c("neuronlist", "names", "data.frame"),
  ...
)

Arguments

x

a neuronlist

subset

An expression that can be evaluated in the context of the dataframe attached to the neuronlist. See details.

filterfun

a function which can be applied to each neuron returning TRUE when that neuron should be included in the return list.

rval

What to return (character vector, default='neuronlist')

...

additional arguments passed to filterfun

Value

A neuronlist, character vector of names or the attached data.frame according to the value of rval

Details

The subset expression should evaluate to one of

• character vector of names

• logical vector

• vector of numeric indices

Any missing names are dropped with a warning. The filterfun expression is wrapped in a try. Neurons returning an error will be dropped with a warning.

You may also be interested in find.neuron, which enables objects in a neuronlist to be subsetted by a 3D selection box. In addition subset.neuron, subset.dotprops methods exist: these are used to remove points from neurons (rather than to remove neurons from neuronlists).

See also

neuronlist, find.neuron, subset.data.frame, subset.neuron, subset.dotprops

Examples

da1pns=subset(Cell07PNs,Glomerulus=='DA1')
with(da1pns,stopifnot(all(Glomerulus=='DA1')))
gammas=subset(kcs20,type=='gamma')
with(gammas,stopifnot(all(type=='gamma')))
# define a function that checks whether a neuron has points in a region in 
# space, specifically the tip of the mushroom body alpha' lobe
aptip<-function(x) {xyz=xyzmatrix(x);any(xyz[,'X']>350 & xyz[,'Y']<40)}
# this should identify the alpha'/beta' kenyon cells only
apbps=subset(kcs20,filterfun=aptip)
# look at which neurons are present in the subsetted neuronlist
head(apbps)
#>                                       gene_name          Name  idid soma_side
#> GadMARCM-F000142_seg002 GadMARCM-F000142_seg002 Gad1-F-300043 10647         L
#> GadMARCM-F000423_seg001 GadMARCM-F000423_seg001 Gad1-F-300107  9541         R
#> ChaMARCM-F000586_seg002 ChaMARCM-F000586_seg002  Cha-F-300150  7113         R
#>                         flipped    Driver Gender        X        Y        Z
#> GadMARCM-F000142_seg002   FALSE Gad1-Gal4      F 349.5917 78.18986 96.69280
#> GadMARCM-F000423_seg001    TRUE Gad1-Gal4      F 401.4795 76.32671 97.92564
#> ChaMARCM-F000586_seg002    TRUE  Cha-Gal4      F 340.1020 79.79322 92.21622
#>                                        exemplar cluster  idx type
#> GadMARCM-F000142_seg002 GadMARCM-F000142_seg002      71 1535 apbp
#> GadMARCM-F000423_seg001 GadMARCM-F000423_seg001      61 1265 apbp
#> ChaMARCM-F000586_seg002 ChaMARCM-F000586_seg002      52  898 apbp
# combine global variables with dataframe columns
odds=rep(c(TRUE,FALSE),10)
stopifnot(all.equal(subset(kcs20,type=='gamma' & odds),
            subset(kcs20,type=='gamma' & rep(c(TRUE,FALSE),10))))
if (FALSE) {
# make a 3D selection function using interactive rgl::select3d() function
s3d=select3d()
# Apply a 3D search function to the first 100 neurons in the neuronlist dataset
subset(dps[1:100],filterfun=function(x) {sum(s3d(xyzmatrix(x)))>0},
  rval='names')
# combine a search by metadata, neuropil location and 3D location
subset(dps, Gender=="M" & rAL>1000, function(x) sum(s3d(x))>0, rval='name')
# The same but specifying indices directly, which can be considerably faster
# when neuronlist is huge and memory is in short supply
subset(dps, names(dps)[1:100],filterfun=function(x) {sum(s3d(xyzmatrix(x)))>0},
  rval='names')
}