Neurons
We start by extracting the first neuron in Cell07PNs, and assigning it to a variable. We can then plot the neuron so that we have an image of what we are talking about.
Neuron internal structure
The str function (short for structure) allows us to take
a look at the internal structure of this neuron object.
## List of 24
## $ CellType : chr "DA1"
## $ NeuronName : chr "EBH11R"
## $ InputFileName: 'AsIs' chr "/GD/projects/PN2/TransformedTraces/DA1/EBH11R.tasc"
## $ CreatedAt : POSIXt[1:1], format: "2006-01-17 15:21:14"
## $ NodeName : Named chr "jefferis.joh.cam.ac.uk"
## ..- attr(*, "names")= chr "nodename"
## $ InputFileStat:'data.frame': 1 obs. of 10 variables:
## $ InputFileMD5 : Named chr "fcacee3f874cbe2c6ad96214e6fee337"
## ..- attr(*, "names")= 'AsIs' chr "/GD/projects/PN2/TransformedTraces/DA1/EBH11R.tasc"
## $ NumPoints : int 180
## $ StartPoint : num 1
## $ BranchPoints : num [1:16] 34 48 51 75 78 95 98 99 108 109 ...
## $ EndPoints : num [1:18] 1 42 59 62 80 85 96 100 102 112 ...
## $ NumSegs : int 33
## $ SegList :List of 33
## $ d :'data.frame': 180 obs. of 7 variables:
## $ OrientInfo :List of 5
## $ SegOrders : num [1:33] 1 2 2 3 4 4 3 4 5 5 ...
## $ MBPoints : int [1:2] 34 48
## $ LHBranchPoint: int 75
## $ SegTypes : num [1:33] 1 3 1 3 3 3 1 2 2 2 ...
## $ AxonSegNos :List of 3
## $ LHSegNos : num [1:26] 8 9 10 11 12 13 14 15 16 17 ...
## $ MBSegNos :List of 2
## $ NumMBBranches: num 2
## $ AxonLHEP : num 72
## - attr(*, "class")= chr [1:2] "neuron" "list"From this you can see that there are a large number of fields inside
the neuron. Each field can be accessed using the $ or
[[ operators
## [1] 180## [1] 180There are a set of core fields (described in ?neuron
documentation); the key ones will be described shortly. However there
are also quite a few user fields in this neuron and you can safely add
any field you like so long as its name does not clash with an existing
field. For example:
The single most important field in a neuron is the $d
data.frame. This contains a block of data closely
reminiscent of the SWC data format for traced neurons:
## 'data.frame': 180 obs. of 7 variables:
## $ PointNo: int 1 2 3 4 5 6 7 8 9 10 ...
## $ Label : num 2 2 2 2 2 2 2 2 2 2 ...
## $ X : num 187 187 188 188 188 ...
## $ Y : num 133 131 130 129 129 ...
## $ Z : num 88.2 90.6 93.1 95 97.5 ...
## $ W : num 1.01 1.27 1.14 1.27 1.27 1.27 1.27 1.27 1.27 1.27 ...
## $ Parent : num -1 1 2 3 4 5 6 7 8 9 ...Each row defines a node in a branched tree with a unique integer
identifier, PointNo, normally but not always sequentially
numbered from 1. The X,Y,Z columns encode the position of
each vertex and the W column encodes the diameter of the
neurite at that position.
Methods for neurons
R’s simple S3
object oriented system allows specialised functions for particular
classes to be defined. These functions tailored to a particular class
are called methods. Methods can be provided for pre-existing
functions supplied with base R as well as new user-written functions.
The system is quite simple. If a function foo is defined as
a generic function then you can define new functions called
foo.bar that will be called when you write
foo(x) and x has class `bar.
We already used one such method without comment,
plot.neuron. There is a base R function called
plot. nat defines a new function called
plot.neuron, which R interprets as a plot
method specialised for neuron objects. When the base R
plot function is called, it looks at the class of its first
object to see whether a specialised method exists. If there is none, it
will use a fallback method called plot.default.
The plot.neuron method interprets the branching
structure of a neuron and draws line segments joining up the connected
nodes at the appropriate 2D positions. You can compare it with what you
would get if you just plotted the XY position of all nodes joined
together by a single line:
You can find out what methods are available for a particular class like so:
## [1] * + - /
## [5] all.equal as.neuron as.ngraph as.seglist
## [9] boundingbox branchpoints dotprops endpoints
## [13] ndigest nvertices plot plot3d
## [17] potential_synapses prune resample rootpoints
## [21] scale subset summary write.vtk
## [25] xform xyzmatrix xyzmatrix<-
## see '?methods' for accessing help and source codeYou can then find the help page for any method in the console with
?plot.neuron. Note that if you write ?plot you
will get the documentation for the basic plot function
supplied with R.
It is also good idea to look at nat’s function reference page which groups available functions into categories that often reflect the class of object they can work on.
In R, operators such as * or + are actually
special functions with two arguments, so one can add methods for these
in the same way. In the following example we use this arithmetic to
shift a neuron by a small amount:
plot(n1, col = 'black', WithNodes = F, main="Shifting neurons")
# shift by 3 microns in x,y,z
plot(n1+3, col = 'red', add = TRUE, WithNodes = F)
# shift by -5 microns in y
plot(n1+c(0,-5,0), col = 'blue', add = TRUE, WithNodes = F)
Another specialised method is subset.neuron which we can
use to extract part of a neuron into a new object. For example, a simple
spatial criterion, X location must be >240, is used to extract the
axon terminal arborisation field in this example
plot(n1, col = 'black', WithAllPoints = T, main="Subsetting a neuron")
# keep only nodes with X position >20
n1.lh=subset(n1, X>240)
# plot the selected part of the neuron in blue
plot(n1.lh, col='blue', lwd=3, add=TRUE)
# add a line illustrating the cut point
abline(v=240, lty=2)
We can also summarise the morphological properties of a neuron using
the summary.neuron method, which allows us to compare
measurements for the original neuron and the axon arbour that we just
cut out above.
## root nodes segments branchpoints endpoints cable.length
## 1 1 180 33 16 18 297.1763## root nodes segments branchpoints endpoints cable.length
## 1 1 111 27 13 15 156.7654See nat’s function reference page for additional functions for working with neurons.
