Type: Package
Title: NeuroAnatomy Toolbox ('nat') Extension for Handling Template Brains
Version: 1.1
Description: Extends package 'nat' (NeuroAnatomy Toolbox) by providing objects and functions for handling template brains.
License: GPL-3
URL: http://natverse.org/nat.templatebrains/, https://github.com/natverse/nat.templatebrains
Depends: nat (≥ 1.8.6), R (≥ 2.10), rgl
Imports: digest, igraph, memoise, rappdirs, Morpho
Suggests: spelling, git2r (≥ 0.22.1), nabor, testthat (≥ 2.1.0), covr
Encoding: UTF-8
RoxygenNote: 7.2.3
BugReports: https://github.com/natverse/nat.templatebrains/issues
Language: en-GB
NeedsCompilation: no
Packaged: 2023-06-14 05:42:36 UTC; jefferis
Author: Gregory Jefferis ORCID iD [aut, cre], James Manton ORCID iD [aut]
Maintainer: Gregory Jefferis <jefferis@gmail.com>
Repository: CRAN
Date/Publication: 2023-06-14 08:20:05 UTC

NeuroAnatomy Toolbox add-on package for handling template brains

Description

This package provides a class templatebrain that stores key information about reference brains along with helper functions to simplify transformation of data between template brains (a.k.a bridging) and mirroring of data within a template brain.

Extends package 'nat' (NeuroAnatomy Toolbox) by providing objects and functions for handling template brains.

Helper functions

Easy-to-use functions for transforming data from one template brain to another, displaying slices alongside 3D data, etc. are provided.

For transforming between spaces, see especially xform_brain, mirror_brain. The regtemplate functions allow an R object containing neuroanatomical data to be tagged as in a particular template brain space.

plot3d.templatebrain provides a simple way to plot a surface object representing a given template brain.

Registrations

Functions such as xform_brain provided by nat.templatebrains are not very useful unless you tell the package about bridging/mirroring registrations that you have available. There are 4 supported ways to add registrations to the search list that will be considered:

Note that the package implements a cache to avoid rescanning the directories in the registration search list all the time. The functions mentioned above will automatically ensure that the cache is reset whenever a new set of registrations are added to the search list.

Package options

It is strongly recommended that you use the functions mentioned above rather than manipulating the registration search list directly.

Note that registration directories will be searched in the order that they are listed. It is therefore strongly recommended that individual registrations have globally unique names.

Author(s)

Maintainer: Gregory Jefferis jefferis@gmail.com (ORCID)

Authors:

See Also

nat

Useful links:

Examples

## Not run: 
# Plot all known registrations
plot(bridging_graph(), vertex.size=25)

## manually add a new directory containing registrations to the search list
# Don't do this unless essential!
options(nat.templatebrains.regdirs=union(
  getOption('nat.templatebrains.regdirs'), "/my/new/path"))
# NB after mucking around with paths manually you must also manually update
# the cache so that new registrations are actually used.
nat.templatebrains:::reset_cache()

## same, but override any built-in registration by putting the new path
# at the first position in the search list
options(nat.templatebrains.regdirs=union("/my/new/path"),
  getOption('nat.templatebrains.regdirs'))

## End(Not run)

Sample template brain: FlyCircuit Whole Brain

Description

This is a sample template brain for testing purposes which is equivalent to the FCWB template brain defined by the nat.flybrains, which should be considered the canonical version.

Set or list local folders containing registrations for nat.templatebrains

Description

add_reg_folders sets options('nat.templatebrains.regdirs') appropriately so that registrations can be found by e.g. xform_brain.

extra_reg_folders lists extra registration folders present in standard location

Usage

add_reg_folders(dir = extra_reg_folders(), first = TRUE)

extra_reg_folders(full.names = TRUE)

Arguments

dir

Path to one or more folders containing registrations. Default value will scan for registration folders in a standard location. (Please see Details and File layout sections)

first

Whether the new folder should be added to the start (default) or end of the search list.

full.names

Whether to list full path to registration folders

Details

When dir is unset then it will default to the value of extra_reg_folders() i.e. any folders / cloned repositories in the standard location

Value

This function is principally called for its side effect. A path to the location on disk containing added registrations is returned invisibly.

File layout

You must pass a folder containing one or more registrations, not the registration folder itself. So if you have this situation on disk

you should write add_reg_folders("/path/to/myregistrations")

Examples


## Not run: 
  add_reg_folders("myextraregistrations")

## End(Not run)
# adding a non-existent folder will generate an error
tools::assertError(add_reg_folders(tempfile()))

Add reglist object describing a bridging/mirroring registration

Description

By specifying either reference, sample or mirror arguments, you can add a bridging or mirroring registration, respectively, to the list of those in use for xform_brain and mirror_brain.

Usage

add_reglist(
  x,
  reference = NULL,
  sample = NULL,
  mirror = NULL,
  temp = TRUE,
  ...
)

Arguments

x

A single reglist object (which )

reference, sample

The reference and sample brains (in character or templatebrain form) for a bridging registration.

mirror

The reference brain (in character or templatebrain form) for a mirroring registration.

temp

Whether to store the on disk representation in a session-specific temporary folder (that will be removed when R closes). Defaults to TRUE.

...

Additional arguments passed to saveRDS e.g. to control compression when the reglist object is saved to disk.

Value

This function is called for its side effect and has no return value.

See Also

add_reg_folders

Examples

## Not run: 
library(nat.flybrains)
# mirroring registration for a specific template brain object
add_reglist(mirroring, mirror=JFRC2013)
# equivalent but withhout needing to construct the template
add_reglist(mirroring, mirror="JFRC2013")

# add a bridging registration between two brains
add_reglist(bridging, reference=JFRC2, sample=JFRC2013)


## End(Not run)

Find all template brains or those matching a given image volume

Description

all_templatebrains returns a data.frame detailing all templatebrain objects on the search path (including those inside packages).

Usage

all_templatebrains(cached = TRUE, remove.duplicates = FALSE)

guess_templatebrain(
  x,
  rval = c("templatebrain", "name"),
  cached = TRUE,
  mustWork = FALSE
)

Arguments

cached

When TRUE returns precomputed (memoised) results, otherwise rescans searching for all template brains.

remove.duplicates

Whether to remove duplicate template brains (as determined by md5 hash) from the result list

x

A im3d image object, array or matrix compatible with as.templatebrain OR a 2 or 3-vector defining the dimensions of an image or image stack.

rval

Whether to return the templatebrain object itself or just its name.

mustWork

Whether to insist that exactly one template brain is found

Value

For all_templatebrains, a data.frame containing the following columns:

guess_templatebrain returns a templatebrain object when rval='templatebrain' or a character vector when rval='name'.

See Also

templatebrain

Examples

## Not run: 
all_templatebrains()

guess_templatebrain(im3d(dims=c(30,40,50)))
# or
guess_templatebrain(c(30,40,50))
guess_templatebrain('path/to/my/image.nrrd')

if(require('nat.flybrains')){
  guess_templatebrain(im3d(dims=c(1024,512,218)), rval = 'name')
  # get the matching template brain
  tb=guess_templatebrain(im3d(dims=c(1024,512,218)))
  # get its voxel dimensions
  voxdims(tb)

  tb=guess_templatebrain(c(1024,512))
  tb
}

## End(Not run)

Make data.frame with details of all registrations

Description

Make data.frame with details of all registrations

Usage

allreg_dataframe(regdirs = getOption("nat.templatebrains.regdirs"))

Arguments

regdirs

Character vector of directories to search for registrations (see details)

Details

by default regdirs is set to getOption('nat.templatebrains.regdirs')

Value

data.frame with one row for each observed registration and columns

If there are no registrations, there will be a data.frame with 0 rows and these columns.

Examples

## Not run: 
allreg_dataframe()

## End(Not run)

Use image file or other object to initialise template brain

Description

Use image file or other object to initialise template brain

Usage

as.templatebrain(x, ...)

## S3 method for class 'character'
as.templatebrain(x, ...)

## S3 method for class 'im3d'
as.templatebrain(x, regName = NULL, name = regName, ...)

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

Arguments

x

object used to construct the templatebrain, either a character vector with the path to a file or an im3d object.

...

additional named arguments passed to methods and then on to templatebrain that will be added as fields to the templatebrain object.

name, regName

name and short name of the template brain. Will use the filename (minus final extension) by default for both fields.

Details

as.templatebrain can extract the key fields defining an template space from an image file. This is generally a much more convenient approach to defining a templatebrain object than specifying all fields by hand.

Value

A list with class templatebrain

See Also

templatebrain, im3d

Examples

# Make templatebrain object using image info from the template brain NRRD file
nhdr=system.file('images','FCWB.nhdr', package='nat.templatebrains')
as.templatebrain(nhdr, name = "FlyCircuit Whole Brain")

Make or query connected graph of bridging registrations

Description

These functions are designed for expert use. In general it is recommended to use xform_brain.

bridging_graph creates an igraph::graph representing all known template brains (vertices) and the bridging registrations connecting them (edges).

shortest_bridging_seq finds the shortest bridging sequence on a graph of all available bridging registrations, subject to constraints defined by graph connectivity and the reciprocal parameter.

Usage

bridging_graph(
  regdirs = getOption("nat.templatebrains.regdirs"),
  reciprocal = NA
)

shortest_bridging_seq(
  sample,
  reference,
  via = NULL,
  checkboth = TRUE,
  imagedata = FALSE,
  reciprocal = NA,
  ...
)

Arguments

regdirs

Character vector of directories to search for registrations (see details)

reciprocal

Sets the weight of reciprocal edges in the graph (and thereby whether inverse registrations will be considered).

sample

Source template brain (e.g. IS2) that data is currently in. Specified either as character vector or a templatebrain object.

reference

Target template brain (e.g. IS2) that data should be transformed into.

via

(optional) intermediate template brain that the registration sequence must pass through.

checkboth

When TRUE will look for registrations in both directions. See details.

imagedata

Whether x should be treated as image data (presently only supported as a file on disk) or 3D object vertices - see details.

...

additional arguments passed on to bridging_graph

Details

When reciprocal != NA we create a graph where each forward transformation is matched by a corresponding inverse transformation with the specified edge weight. The edge weight for forward transforms will always be 1.0.

By default regdirs is set to getOption('nat.templatebrains.regdirs')

See Also

allreg_dataframe, xform_brain

Examples

## Not run: 
plot(bridging_graph(), vertex.size=25, edge.arrow.size=0.5)
# with reciprocal edges
plot(bridging_graph(reciprocal=3), vertex.size=25)

## End(Not run)
## Not run: 
shortest_bridging_seq(FCWB, IS2)
# or
shortest_bridging_seq('FCWB', 'IS2')

shortest_bridging_seq(sample='FCWB', reference='IS2', via="JFRC2")

## End(Not run)

Find sequence of one or more bridging registrations

Description

This function is primarily intended for developer use (it is used inside xform_brain) but may be useful for end users.

Usage

bridging_sequence(
  sample,
  reference,
  via = NULL,
  imagedata = FALSE,
  checkboth = !imagedata,
  mustWork = FALSE
)

Arguments

sample

Source template brain (e.g. IS2) that data is currently in. Specified either as character vector or a templatebrain object.

reference

Target template brain (e.g. IS2) that data should be transformed into.

via

(optional) intermediate template brain that the registration sequence must pass through.

imagedata

Whether x should be treated as image data (presently only supported as a file on disk) or 3D object vertices - see details.

checkboth

whether to look for registrations in both directions. The default (checkboth=FALSE) will only return registrations in the forward direction (see details).

mustWork

whether to error out if appropriate registrations are not found.

Details

When checkboth=FALSE, only registrations that can be directly used to map image data from sample to reference are returned. When working with 3D points, use checkboth=TRUE. Note that all possible directories will first be scanned for registrations in the preferred direction and then rescanned for the opposite direction if nothing is found.

Registration direction

When mapping points from JFRC2 -> IS2 -> FCWB (i.e. sample=JFRC2, via=IS2, ref=FCWB) the command line passed to CMTK's streamxform should look like: ⁠streamxform -- JFRC2_IS2.list --inverse FCWB_IS2.list⁠ However when mapping image data the command line for CMTK's reformatx should look like: ⁠reformatx -o out.nrrd --floating JFRC2.nrrd FCWB.nrrd FCWB_IS2.list --inverse JFRC2_IS2.list⁠ bridging_sequence produces output like ⁠ list(JFRC2 = structure( "/GD/dev/R/nat.flybrains/inst/extdata/bridgingregistrations/JFRC2_IS2.list", swap = TRUE), IS2 = "/GD/dev/R/nat.flybrains/inst/extdata/bridgingregistrations/FCWB_IS2.list") ⁠ in these circumstances, which xformpoints.cmtkreg turns into "– JFRC2_IS2.list –inverse FCWB_IS2.list".

Examples

## Not run: 
bridging_sequence(sample=JFRC2, ref=FCWB, checkboth = TRUE)
bridging_sequence(sample=JFRC2, via=IS2, ref=FCWB, checkboth = TRUE)

## End(Not run)

Display an image slice in 3D

Description

Display an image slice in 3D

Usage

display_slice(brain, slice, ...)

Arguments

brain

template brain (e.g. IS2) of the slice.

slice

Path to PNG image containing slice to display.

...

extra arguments to pass to persp3d.

Value

This function is principally called for its side effect of adding to the plot. A vector of rgl shape IDs is returned invisibly.

Download and register git repository containing registrations

Description

Note that these extra registrations will be downloaded to a standard location on your hard drive that will be used for one session to the next. See examples and local_reg_dir_for_url.

Usage

download_reg_repo(url, localdir = NULL, ...)

Arguments

url

Location of one or more remote git repositories. Can accept partial github specifications of the form "<user>/<repo>".

localdir

Full path to local checkout location of git repository. When localdir=NULL, the default, a sensible location is chosen using the rappdirs function.

...

additional arguments passed to git2r::clone e.g. credentials for private repo.

Value

This function is principally called for its side effect. A path to the location on disk containing added registrations is returned invisibly.

See Also

add_reg_folders, local_reg_dir_for_url, git2r::clone

update_reg_repos

Examples

## find the root location of all registration directories
local_reg_dir_for_url()
## Not run: 
## Add the two main jefferislab bridging and mirroring registration
# collections for Drosophila brains from github.com.
download_reg_repo("jefferislab/BridgingRegistrations")
download_reg_repo("jefferislab/MirrorRegistrations")

## update all current registration repositories
update_reg_repos()

## End(Not run)

Fit affine or thin plate spline transform to arbitrary transformation

Description

Fit affine or thin plate spline transform to arbitrary transformation

Usage

fit_xform(
  reg,
  samplepts,
  refpts = NULL,
  type = c("affine", "rigid", "similarity", "tps"),
  subsample = FALSE,
  scale = c(1, 1),
  ...
)

Arguments

reg

Any registration compatible with nat::xform, including non-rigid and multi-step registrations. You must either supply this or the refpts argument.

samplepts

A set of points in the sample (floating) space. Can be any object compatible with nat::xyzmatrix.

refpts

An optional set of points in the target (fixed) space matching samplepts. You must either supply this or the reg argument.

type

A character string specifying the type of registration. See computeTransform for details.

subsample

A number of points to subsample from samplepts,refpts. The default value of FALSE means use all provided points to calculate the new transform.

scale

a 2-vector indicating the amount to scale the sample and reference points. You can supply one vector if this is the same. If the transform expects points in microns and returns points in microns then you would need scale=c(1000,1) if you want the input to be in microns and the output to be in nm.

...

Additional arguments passed to computeTransform

Value

A homogeneous affine matrix or a nat::tpsreg object n.b. only in development nat (>= 1.10.1)

Examples

## Not run: 
library(nat.flybrains)
reg=shortest_bridging_seq(sample='FCWB', reference="JFRC2")
fit_xform(reg, nat::kcs20, subsample=200, type='affine')
# compute transform with translations in nm not microns
fit_xform(reg, nat::kcs20, subsample=200, type='affine', scale=1000)

## End(Not run)

Fit a single transform to a bridging registration between brains

Description

Fit a single transform to a bridging registration between brains

Usage

fit_xform_brain(
  sample,
  reference,
  via = NULL,
  type = c("affine", "rigid", "similarity", "tps"),
  pts = NULL,
  npts = if (is.null(pts)) 1000 else NULL,
  scale = c(1, 1),
  ...
)

Arguments

sample

Source template brain (e.g. IS2) that data is currently in. Specified either as character vector or a templatebrain object.

reference

Target template brain (e.g. IS2) that data should be transformed into.

via

(optional) intermediate template brain that the registration sequence must pass through.

type

A character string specifying the type of registration. See computeTransform for details.

pts

Optional set of points to use for the fit. If they are not specified they will be randomly sampled (see details).

npts

Number of points to use for fit (defaults to 1000 when no pts argument is specified).

scale

a 2-vector indicating the amount to scale the sample and reference points. You can supply one vector if this is the same. If the transform expects points in microns and returns points in microns then you would need scale=c(1000,1) if you want the input to be in microns and the output to be in nm.

...

Additional arguments passed to fit_xform

Details

If pts are supplied these will be used to construct the fit. This might be useful if you are focussing on a particular brain region and supply relevant neurons. But if you want to work with one registration to use for the whole brain then it's better to use points across the brain.

When points are not provided then fit_xform_brain will first check to see if it can find a neuropil surface model for the sample. If it can, it will sample a number of points that lie inside the surface model. If no surface model can be found, then it will look for a templatebrain object specifying a bounding box around the sample brain. If successful, it will sample npts within that bounding box. If not there will be an error and you will have to supply the points.

Value

A homogeneous affine matrix or a nat::tpsreg object n.b. only in development nat (>= 1.10.1)

See Also

fit_xform

Examples

## Not run: 
library(nat.flybrains)
t1=fit_xform_brain(sample='FCWB', reference="JFRC2", type='affine')
t1
# the same but for points in nm
fit_xform_brain(sample='FCWB', reference="JFRC2", type='affine', scale=1000)
# run the fit based on a particular group of Kenyon cells
t2=fit_xform_brain(sample='FCWB', reference="JFRC2",
  pts=nat::kcs20, npts=300, type='affine')

nclear3d()
mfrow3d(1, 2, sharedMouse = TRUE)
plot3d(JFRC2)
plot3d(xform(FCWB.surf, t1))
next3d()
plot3d(JFRC2)
plot3d(xform(FCWB.surf, t2))
# the whole brain surfaces clearly match better when you fit on the whole brain

## End(Not run)

Standard local checkout location for extra registration directories

Description

Standard local checkout location for extra registration directories

Usage

local_reg_dir_for_url(url = NULL)

Arguments

url

Character vector containing a url. When url=NULL defaults to giving the base path.

Details

When called without any argument returns the root directory that will be inspected for extra registrations. You can put a sub-folder yourself there manually and then call add_reg_folders, but you are much better off in general using download_reg_repo to install from a github repository such as this one of ours:. jefferislab/BridgingRegistrations

Note that this folder will always be the same place on a machine i.e. this defines a consistent, persistent location on disk to store data across sessions. You can modify the location of this folder by editing the R_USER_DATA_DIR environment variable. While this is not recommended on a personal machine, it may be necessary on a server. See rappdirs::user_data_dir for details.

When called with a url, a SHA1 hash will be calculated for the URL and appended to the basepath. This should ensure that locations derived from different URLs do not clash.

Value

path(s) containing registrations on disk.

See Also

download_reg_repo, rappdirs::user_data_dir

Mirror 3D object around a given axis, optionally using a warping registration

Description

Mirror 3D object around a given axis, optionally using a warping registration

Usage

mirror_brain(
  x,
  brain = regtemplate(x),
  mirrorAxis = c("X", "Y", "Z"),
  transform = c("warp", "affine", "flip"),
  ...
)

Arguments

x

the 3D object to be mirrored.

brain

source template brain (e.g. IS2) that data is in.

mirrorAxis

the axis to mirror (default "X").

transform

whether to use warp (default) or affine component of registration, or simply flip about midplane of axis.

...

extra arguments to pass to mirror.

Value

A transformed version of x

See Also

xform_brain, regtemplate

Examples

data(FCWB.demo)
# Simple mirror along the x i.e. medio-lateral axis
kcs20.flip=mirror_brain(kcs20, FCWB.demo, transform='flip')

## Full non-rigid mirroring to account for differences in shape/centering of
## template brain.
## Depends on nat.flybrains package and system CMTK installation
## Not run: 
library(nat.flybrains)
kcs20.right=mirror_brain(kcs20, FCWB, .progress='text')
plot3d(kcs20, col='red')
plot3d(kcs20.right, col='green')
# include surface plot of brain
plot3d(FCWB)

# Compare simple flip with full mirror
# This template brain is highly symmetric so these are almost identical
clear3d()
plot3d(kcs20.flip, col='blue')
plot3d(kcs20.right, col='green')

# Convert to JFRC2 and do the same
kcs20.jfrc2=xform_brain(kcs20, sample = FCWB, reference=JFRC2, .progress='text')
kcs20.jfrc2.right=mirror_brain(kcs20.jfrc2, JFRC2, .progress='text')
kcs20.jfrc2.flip=mirror_brain(kcs20.jfrc2, JFRC2, transform='flip')
clear3d()
# This time there is a bigger difference between the two transformations
plot3d(kcs20.jfrc2.flip, col='blue')
plot3d(kcs20.jfrc2.right, col='green')
# plot mushroom body neuropils as well
plot3d(JFRC2NP.surf, "MB.*_R", alpha=0.3, col='grey')

# Compare Euclidean distance between corresponding points in all neurons
diffs=xyzmatrix(kcs20.jfrc2.flip)-xyzmatrix(kcs20.jfrc2.right)
hist(sqrt(rowSums(diffs^2)), xlab='Distance /microns')

## End(Not run)

Plot 3D surface of a template brain

Description

Plot 3D surface of a template brain

Usage

## S3 method for class 'templatebrain'
plot3d(x, col = "grey", alpha = 0.3, ...)

Arguments

x

the template brain to plot.

col

the color of the surface.

alpha

the alpha value of the surface.

...

extra arguments to pass to plot3d.

Details

This function will work immediately for the standard templatebrain defined in the package documentation. If passed an object called e.g. FCWB it expects to find another object named FCWB.surf containing the surface information. If you follow this naming convention for user-defined refbrains it will work for them as well.

Value

This function is principally called for its side effect of adding to the plot. A vector of rgl shape IDs is returned invisibly.?

Get or set the registration template space in which an object lives

Description

Get or set the registration template space in which an object lives

Usage

regtemplate(x)

regtemplate(x) <- value

Arguments

x

The 3D object whose registration space will be set/returned

value

The registration template brain (either a character vector naming the space or a templatebrain object)

Details

In order to facilitate transformations between objects in defined anatomical spaces these functions allow the registration template for an object to be specified. Most of the time you will not need to use these functions manually since the appropriate space will be set by the function xform_brain and friends.

Value

Either a templatebrain object or the newly tagged object

Examples

## Not run: 
library(nat.flybrains)
kcs3=kcs20[1:3]
regtemplate(kcs3)=FCWB
regtemplate(kcs3)

kcs3m=mirror_brain(kcs3, brain=regtemplate(kcs20))
plot3d(kcs3, col='red')
plot3d(kcs3m, col='green')

## End(Not run)

Construct templatebrain object for an image registration template

Description

templatebrain objects encapsulate key information for the reference brain in an image registration. Usually this will be a standard template brain used for many registrations. It will normally be much more convenient to use as.templatebrain methods to convert an image file or an im3d object into a templatebrain.

Usage

templatebrain(
  name,
  regName = name,
  type = NULL,
  sex = NULL,
  dims = NULL,
  BoundingBox = NULL,
  voxdims = NULL,
  origin = NULL,
  units = NULL,
  description = NULL,
  doi = NULL,
  ...
)

Arguments

name

the full name of the template.

regName

the short name. This will be the stem used to prefix registrations (e.g. JFRC2_someimage.list) for this template brain and likely also the stem of the template brain image (e.g. JFRC2.nrrd).

type

one of c('single brain', 'average'), indicating whether the template brain has been created from just one image, or is the average of multiple images.

sex

the sex of the template brain. For templates with type=='average', the possibility of sex='intersex' exists.

dims

dimensions of the image (number of voxels).

BoundingBox

physical dimensions of the image (see boundingbox).

voxdims

physical spacing between voxels.

origin

the physical location of the first voxel

units

units of physical measurements (e.g. microns).

description

details of the template.

doi

a DOI for the original template brain image.

...

additional named arguments that will be added as fields to the templatebrain object.

Details

A variety of methods are available to work on templatebrain objects. See templatebrain-meths for basic methods. The two main functions that are available for using template brains are xform_brain and mirror_brain.

templatebrain objects are only useful for transformation processes when the BoundingBox is specified to define the physical extent of the volume. We use the definition of the Amira 3D visualisation and analysis software. This corresponds to the node centers option in the NRRD format. The bounding box can be obtained from NRRD or AmiraMesh format files. See boundingbox for details.

Value

A list with class templatebrain.

See Also

as.templatebrain, templatebrain-meths, xform_brain, mirror_brain.

Template brain methods

Description

is.templatebrain tests if object is of class templatebrain

as.character.templatebrain converts template brain to character vector representation (normally used to extract the short name i.e. regName).

print.templatebrain prints templatebrain information in human-readable form

as.im3d converts a template brain to a nat::im3d object; this is probably useful for developers.

origin extracts the space origin of a templatebrain object.

dim extracts the dimensions (in number of pixels) of the image associated with a templatebrain object.

voxdims extracts the dimensions (in calibrated spatial units, e.g. microns) of voxels in the image associated with a templatebrain object.

boundingbox extracts the boundingbox (in calibrated spatial units, e.g. microns) of the image associated with a templatebrain object. See boundingbox for details.

Usage

is.templatebrain(x)

## S3 method for class 'templatebrain'
as.character(x, field = c("regName", "name"), ...)

## S3 method for class 'templatebrain'
print(x, ...)

## S3 method for class 'templatebrain'
as.im3d(x, ...)

## S3 method for class 'templatebrain'
origin(x, ...)

## S3 method for class 'templatebrain'
dim(x, ...)

## S3 method for class 'templatebrain'
voxdims(x, ...)

## S3 method for class 'templatebrain'
boundingbox(x, ...)

Arguments

x

an object (usually a templatebrain).

field

which field to use (defaults to 'regName').

...

additional arguments for methods.

Value

Return values are documented in the generic methods. is.templatebrain returns a logical indicating whether or not the object is a templatebrain.

as.character.templatebrain returns a character vector with the name of the template brain.

dim.templatebrain returns a 3-value integer vector.

See Also

im3d

origin

voxdims

boundingbox

Examples

data(FCWB.demo)
is.templatebrain(FCWB.demo)
origin(FCWB.demo)
dim(FCWB.demo)
voxdims(FCWB.demo)
boundingbox(FCWB.demo)
# print method
FCWB.demo

Update local copy of git repository containing registrations

Description

When x=NULL all repositories listed in options(nat.templatebrains.regdirs) are checked to see if they are git repositories and, if yes, they are pulled to update.

Usage

update_reg_repos(x = NULL)

Arguments

x

Path to local checkout of a registration git repository. See details for meaning of default.

Value

This function is principally called for its side effect, but does return a git2r::repository object containing the path on disk to the location of the git repository with registration.

See Also

download_reg_repo

Transform 3D object between template brains

Description

Transform 3D object between template brains

Usage

xform_brain(
  x,
  sample = regtemplate(x),
  reference,
  via = NULL,
  imagedata = is.character(x),
  checkboth = NULL,
  target = NULL,
  Verbose = interactive(),
  ...
)

Arguments

x

the 3D object to be transformed

sample

Source template brain (e.g. IS2) that data is currently in. Specified either as character vector or a templatebrain object.

reference

Target template brain (e.g. IS2) that data should be transformed into.

via

(optional) intermediate template brain that the registration sequence must pass through.

imagedata

Whether x should be treated as image data (presently only supported as a file on disk) or 3D object vertices - see details.

checkboth

When TRUE will look for registrations in both directions. See details.

target

When transforming image data, this specifies the target space (defaults to reference when imagedata=TRUE). See Details.

Verbose

Whether to show a message with the sequence of template brains

...

extra arguments to pass to xform and then on to xformpoints or xformimage which will eventually hand off to cmtk.reformatx when using CMTK.

Details

NB the sample, reference and via brains can either be templatebrain objects or a character string containing the short name of the template e.g. "IS2".

xform_brain uses the helper function shortest_bridging_seq to find the shortest path between different template brains based on the set of bridging registrations that the natverse has been informed about (see bridging_graph). You can specify a via argument to ensure that the registrations passes through one or more intermediate templates. Note that when multiple brains are passed to via they should be in order from sample to reference. If you are passing multiple templatebrain objects, they must be wrapped in a list.

When transforming image data (imagedata=TRUE), the target argument should normally be specified. This defines the absolute/voxel dimensions of the target space. This can be calculated from a templatebrain object, so by default it will be set to the value of the reference argument. Alternatively an image file on disk can be specified; this is essential if the reference argument does not specify a templatebrain object but instead just names a template space (i.e. is a string).

The significance of the imagedata and checkboth arguments is that CMTK registrations are not directly invertible although they can be numerically inverted in most cases (unless there are regions where folding occurred). For image data, numerical inversion is much slower.

You can control whether you want to allow inverse registrations manually by setting checkboth explicitly. Otherwise when checkboth=NULL the default is to act as if checkboth=TRUE but issue a warning if an inversion must be used.

Value

A transformed version of x

See Also

mirror_brain, shortest_bridging_seq bridging_graph, regtemplate, xform. xformpoints, xformimage, cmtk.reformatx (for transforming image data with CMTK).

Examples

## depends on nat.flybrains package and system CMTK installation
## Not run: 
## reformat neurons
##
library(nat.flybrains)
# Plot Kenyon cells in their original FCWB template brain
nopen3d()
plot3d(kcs20)
plot3d(FCWB)
# Convert to JFCR2 template brain
kcs20.jfrc2=xform_brain(kcs20, sample = FCWB, reference=JFRC2)
# now plot in the new JFRC2 space
nopen3d()
plot3d(kcs20.jfrc2)
plot3d(JFRC2)
# compare with the untransformed neurons
plot3d(kcs20)
# plot with neuropil sub regions for the left mushroom body
clear3d()
plot3d(kcs20.jfrc2)
# nb "MB.*_L" is a regular expression
plot3d(JFRC2NP.surf, "MB.*_L", alpha=0.3)
# compare with originals - bridging registration is no perfect in peduncle
nopen3d()
plot3d(kcs20)
plot3d(FCWBNP.surf, "MB.*_L", alpha=0.3)

# insist on using a specific intermediate template brain
# this would nor be an improvement in this case
kcs20.jfrc2viais2=xform_brain(kcs20, sample = FCWB, via=IS2, reference=JFRC2)


## reformat image examples
# see ?cmtk.reformatx for details of any additional arguments
# note that for image data a target space defining the dimensions of the
# output image must be specified - this happens by default using the
# reference templatebrain object
xform_brain('in.nrrd', sample=FCWB, ref=JFRC2, output='out.nrrd')
# or you can specify an image file explicitly as target
xform_brain('in.nrrd', sample=FCWB, ref=JFRC2, output='out.nrrd',
            target='JFRC2.nrrd')

# use partial volume interpolation for label field
xform_brain('labels.nrrd', sample=FCWB, ref=JFRC2, output='out.nrrd',
            interpolation='pv')

# use binary mask to restrict (and speed up) reformatting
xform_brain('in.nrrd', sample=FCWB, ref=JFRC2, output='out.nrrd', mask='neuropil.nrrd')

## End(Not run)