neuron {nat}

neuron: class to represent traced neurons

Description

neuron makes a neuron object from appropriate variables.

is.neuron will check if an object looks like a neuron.

as.neuron will convert a suitable object to a neuron

as.neuron.data.frame expects a block of SWC format data

as.neuron.ngraph converts a graph (typically an ngraph object) to a neuron

as.neuron.igraph will convert an ngraph compatible igraph object into a neuron.

as.neuron.default will add class "neuron" to a neuron-like object.

Usage

neuron(
  d,
  NumPoints = nrow(d),
  StartPoint,
  BranchPoints = integer(),
  EndPoints,
  SegList,
  SubTrees = NULL,
  InputFileName = NULL,
  NeuronName = NULL,
  ...,
  MD5 = TRUE
)

is.neuron(x, Strict = FALSE)

as.neuron(x, ...)

## S3 method for class 'data.frame'
as.neuron(x, ...)

## S3 method for class 'ngraph'
as.neuron(x, vertexData = NULL, origin = NULL, Verbose = FALSE, ...)

## S3 method for class 'igraph'
as.neuron(x, ...)

## Default S3 method:
as.neuron(x, ...)

Arguments

d

matrix of vertices and associated data in SWC format

NumPoints

Number of points in master subtree

StartPoint, BranchPoints, EndPoints

Nodes of the neuron

SegList

List where each element contains the vertex indices for a single segments of the neuron, starting at root.

SubTrees

List of SegLists where a neuron has multiple unconnected trees (e.g. because the soma is not part of the graph, or because the neuronal arbour has been cut.)

InputFileName

Character vector with path to input file

NeuronName

Character vector containing name of neuron or a function with one argument (the full path) which returns the name. The default (NULL) sets NeuronName to the file name without the file extension.

...

Additional fields to be included in neuron. Note that if these include CreatedAt, NodeName, InputFileStat or InputFileMD5, they will override fields of that name that are calculated automatically.

MD5

Logical indicating whether to calculate MD5 hash of input

x

A neuron or other object to test/convert

Strict

Whether to check class of neuron or use a more relaxed definition based on object being a list with a SegList component.

vertexData

A dataframe with SWC fields especially X,Y,Z,W,PointNo, Parent.

origin

Root vertex, matched against labels (aka PointNo) when available (see details)

Verbose

Whether to be verbose (default: FALSE)

Details

neuron objects consist of a list containing multiple fields describing the 3D location and connectivity of points in a traced neuron. The critical fields of a neuron, n, are n$d which contains a dataframe in SWC format and n$SegList which contains a representation of the neuron's topology used for most internal calculations. For historical reasons, n$SegList is limited to a single fully-connected tree. If the tree contains multiple unconnected subtrees, then these are stored in n$SubTrees and nTrees will be >1; the "master" subtree (typically the one with the most points) will then be stored in n$SegList and n$NumPoints will refer to the number of points in that subtree, not the whole neuron.

StartPoint, BranchPoints, EndPoints are indices matching the rows of the vertices in d not arbitrary point numbers typically encoded in d$PointNo.

Columns will be ordered c('PointNo','Label','X','Y','Z','W','Parent')

Uses a depth first search on the tree to reorder using the given origin.

When the graph contains multiple subgraphs, only one will be chosen as the master tree and used to construct the SegList of the resultant neuron. However all subgraphs will be listed in the SubTrees element of the neuron and nTrees will be set appropriately.

When the graph vertices have a label attribute derived from PointNo, the origin is assumed to be specified with respect to the vertex labels rather than the raw vertex ids.

Value

A list with elements: (NumPoints,StartPoint,BranchPoints,EndPoints,nTrees,NumSegs,SegList, [SubTrees]) NB SubTrees will only be present when nTrees>1.

See Also

neuronlist

dfs, as.seglist

Other neuron: ngraph(), plot.neuron(), potential_synapses(), prune(), resample(), rootpoints(), spine(), subset.neuron()

Examples

## See help for functions listed in See Also for more detailed examples
## Basic properties
# a sample neuron 
n = Cell07PNs[[1]]
# inspect its internal structure
str(n)
# summary of 3D points
summary(xyzmatrix(n))
# identify 3d location of endpoints
xyzmatrix(n)[endpoints(n),]

## Other methods
# plot
plot(n)
# all methods for neuron objects
methods(class = 'neuron')

## Neurons as graphs 
# convert to graph and find longest paths by number of nodes
ng=as.ngraph(n)
hist(igraph::distances(ng))
# ... or in distances  microns
ngw=as.ngraph(n, weights=TRUE)
hist(igraph::distances(ngw))

# converting back and forth between neurons and graphs
g=as.ngraph(Cell07PNs[[1]])
gstem=igraph::induced_subgraph(g, 1:10)
# this is fine
plot(gstem)
plot(as.neuron(gstem))

# but if you had an undirected graph 
ug=igraph::as.undirected(gstem)
# you get a warning because there is no explicit origin for the graph
as.neuron(ug)

# If you need finer control of the conversion process 
gstem2=as.ngraph(ug, root = 10)
plot(gstem2)
plot(as.neuron(gstem2))