| Title: | Tools for Microscopy Imaging |
|---|---|
| Description: | Tools for 3D imaging, mostly for biology/microscopy. Read and write TIFF stacks. Functions for segmentation, filtering and analyzing 3D point patterns. |
| Authors: | Volker Schmid [aut, cre], Priyanka Kukreja [ctb], Fabian Scheipl [ctb] |
| Maintainer: | Volker Schmid <[email protected]> |
| License: | GPL-3 |
| Version: | 1.1.8 |
| Built: | 2025-02-22 04:10:08 UTC |
| Source: | https://github.com/bioimaginggroup/bioimagetools |
Binary segmentation in 3d
bwlabel3d(img)bwlabel3d(img)
img |
A 3d array. x is considered as a binary image, whose pixels of value 0 are considered as background ones and other pixels as foreground ones. |
A grayscale 3d array, containing the labeled version of x.
Fabian Scheipl, Volker Schmid
Computes intensity-weighted centers of objects and their mass (sum of intensities) and size.
cmoments3d(mask, ref)cmoments3d(mask, ref)
mask |
a labeled stack as returned from bwlabel3d |
ref |
the original image stack |
a matrix with the moments of the objects in the stack
Volker Schmid
Permutation Test for cross-type nearest neighbor distances
cnnTest( dist, n1, n2, w = rep(1, n1 + n2), B = 999, alternative = "less", returnSample = TRUE, parallel = FALSE, ... )cnnTest( dist, n1, n2, w = rep(1, n1 + n2), B = 999, alternative = "less", returnSample = TRUE, parallel = FALSE, ... )
dist |
a distance matrix, the upper n1 x n1 part contains distances between objects of type 1 the lower n2 x n2 part contains distances between objects of type 2 |
n1 |
numbers of objects of type 1 |
n2 |
numbers of objects of type 2 |
w |
(optional) weights of the objects (length n1+n2) |
B |
number of permutations to generate |
alternative |
alternative hypothesis ("less" to test H0:Colocalization ) |
returnSample |
return sampled null distribution |
parallel |
Logical. Should we use parallel computing? |
... |
additional arguments for mclapply |
a list with the p.value, the observed weighted mean of the cNN-distances, alternative and (if returnSample) the simulated null dist
Fabian Scheipl
Compute cross-type nearest neighbor distances
crossNN(dist, n1, n2, w = rep(1, n1 + n2))crossNN(dist, n1, n2, w = rep(1, n1 + n2))
dist |
a distance matrix, the upper n1 x n1 part contains distances between objects of type 1 the lower n2 x n2 part contains distances between objects of type 2 |
n1 |
numbers of objects of type 1 |
n2 |
numbers of objects of type 2 |
w |
optional weights of the objects (length n1+n2), defaults to equal weights |
a (n1+n2) x 2 matrix with the cross-type nearest neighbor distances and weights given as the sum of the weights of the involved objects
Fabian Scheipl
A function to compute the distance from spots to borders of classes
distance2border( points, img.classes, x.microns, y.microns, z.microns, class1, class2 = NULL, mask = array(TRUE, dim(img.classes)), voxel = FALSE, hist = FALSE, main = "Minimal distance to border", xlab = "Distance in Microns", xlim = c(-0.3, 0.3), n = 20, stats = TRUE, file = NULL, silent = FALSE, parallel = FALSE )distance2border( points, img.classes, x.microns, y.microns, z.microns, class1, class2 = NULL, mask = array(TRUE, dim(img.classes)), voxel = FALSE, hist = FALSE, main = "Minimal distance to border", xlab = "Distance in Microns", xlim = c(-0.3, 0.3), n = 20, stats = TRUE, file = NULL, silent = FALSE, parallel = FALSE )
points |
Data frame containing the coordinates of points in microns as X-, Y-, and Z-variables. |
img.classes |
3D array (or image) of classes for each voxel. |
x.microns |
Size of image in x-direction in microns. |
y.microns |
Size of image in y-direction in microns. |
z.microns |
Size of image in z-direction in microns. |
class1 |
Which class is the reference class. If is.null(class2), the function computes the distance of points to the border of class (in img.classes). |
class2 |
Which class is the second reference class. If not is.null(class2), the function computes the distance of points from the border between classes class1 and class2. Default: class2=NULL. |
mask |
Array of mask. Needs to have same dimension as img.classes. Only voxels with mask[i,j,k]==TRUE are used. Default: array(TRUE,dim(img.classes)) |
voxel |
Logical. If TRUE, points coordinates are given as voxels rather than in microns. |
hist |
Automatically plot histogram using hist() function. Default: FALSE. |
main |
If (hist) title of histogram. Default: "Minimal distance to border". |
xlab |
If (hist) description of x axis. Default: "Distance in Microns". |
xlim |
If (hist) vector of range of x axis (in microns). Default: c(-.3,.3) |
n |
If (hist) number of bins used in hist(). Default: 20. |
stats |
If (hist) write statistics into plot. Default: TRUE. |
file |
If (hist) the file name of the produced png. If NULL, the histogram is plotted to the standard device. Default: NULL. |
silent |
if TRUE, function remains silent during running time |
parallel |
Logical. Can we use parallel computing? |
This function computes the distances from points to the border of a class or the border between two classes. For the latter, only points in these two classes are used.
The function returns a vector with distances. Negative values correspond to points lying in class1.
Warning: So far no consistency check for arguments is done. E.g., distance2border(randompoints,img.classes=array(1,c(100,100,2)),3,3,1,class1=2) will fail with some cryptic error message (because class1 > max(img.classes)).
## Not run: #simulate random data randompoints<-data.frame("X"=runif(100,0,3),"Y"=runif(100,0,3),"Z"=runif(100,0,.5)) # coordinates in microns! plot(randompoints$X,randompoints$Y,xlim=c(0,3),ylim=c(0,3),pch=19) # points in a circle circlepoints<-read.table(system.file("extdata","kreispunkte.table", package="bioimagetools"),header=TRUE) plot(circlepoints$X,circlepoints$Y,xlim=c(0,3),ylim=c(0,3),pch=19) # a circle like image img<-readTIF(system.file("extdata","kringel.tif",package="bioimagetools")) img<-array(img,dim(img)) # save as array for easier handling img(img, z=1) #and a mask mask<-readTIF(system.file("extdata","amask.tif",package="bioimagetools")) img(mask, z=1, col="greyinverted") xy.microns <- 3 # size in x and y direction (microns) z.microns <- 0.5 # size in z direction (microns) # distance from points to class d1<-distance2border(randompoints, img, xy.microns, xy.microns, z.microns, class1=1,hist=TRUE) d2<-distance2border(circlepoints, img, xy.microns, xy.microns, z.microns, class1=1,hist=FALSE) plot(density(d2),type="l") lines(c(0,0),c(0,10),lty=3) lines(density(d1),col="blue") # use mask, should give some small changes d3<-distance2border(circlepoints, img, xy.microns, xy.microns, z.microns, class1=1,mask=mask,hist=FALSE) plot(density(d2),type="l") lines(c(0,0),c(0,10),lty=3) lines(density(d3),col="blue") # distance from border between classes anotherimg<-img+mask image(seq(0,3,length=300),seq(0,3,length=300),anotherimg[,,1]) points(circlepoints,pch=19) d4<-distance2border(circlepoints, anotherimg, xy.microns, xy.microns, z.microns, class1=1,class2=2) plot(density(d4),lwd=2) # this should give the same answer d5<-distance2border(circlepoints, anotherimg, xy.microns, xy.microns, z.microns, class1=2,class2=1) lines(density(-d5),lty=3,col="blue",lwd=1.5) ## End(Not run)## Not run: #simulate random data randompoints<-data.frame("X"=runif(100,0,3),"Y"=runif(100,0,3),"Z"=runif(100,0,.5)) # coordinates in microns! plot(randompoints$X,randompoints$Y,xlim=c(0,3),ylim=c(0,3),pch=19) # points in a circle circlepoints<-read.table(system.file("extdata","kreispunkte.table", package="bioimagetools"),header=TRUE) plot(circlepoints$X,circlepoints$Y,xlim=c(0,3),ylim=c(0,3),pch=19) # a circle like image img<-readTIF(system.file("extdata","kringel.tif",package="bioimagetools")) img<-array(img,dim(img)) # save as array for easier handling img(img, z=1) #and a mask mask<-readTIF(system.file("extdata","amask.tif",package="bioimagetools")) img(mask, z=1, col="greyinverted") xy.microns <- 3 # size in x and y direction (microns) z.microns <- 0.5 # size in z direction (microns) # distance from points to class d1<-distance2border(randompoints, img, xy.microns, xy.microns, z.microns, class1=1,hist=TRUE) d2<-distance2border(circlepoints, img, xy.microns, xy.microns, z.microns, class1=1,hist=FALSE) plot(density(d2),type="l") lines(c(0,0),c(0,10),lty=3) lines(density(d1),col="blue") # use mask, should give some small changes d3<-distance2border(circlepoints, img, xy.microns, xy.microns, z.microns, class1=1,mask=mask,hist=FALSE) plot(density(d2),type="l") lines(c(0,0),c(0,10),lty=3) lines(density(d3),col="blue") # distance from border between classes anotherimg<-img+mask image(seq(0,3,length=300),seq(0,3,length=300),anotherimg[,,1]) points(circlepoints,pch=19) d4<-distance2border(circlepoints, anotherimg, xy.microns, xy.microns, z.microns, class1=1,class2=2) plot(density(d4),lwd=2) # this should give the same answer d5<-distance2border(circlepoints, anotherimg, xy.microns, xy.microns, z.microns, class1=2,class2=1) lines(density(-d5),lty=3,col="blue",lwd=1.5) ## End(Not run)
A filter is applied to a 3D array representing an image. So far only variance filters are supported.
filterImage3d(img, filter = "var", window, z.scale = 1, silent = FALSE)filterImage3d(img, filter = "var", window, z.scale = 1, silent = FALSE)
img |
is a 3d array representing an image. |
filter |
is the filter to be applied. Options: var: Variance filter. |
window |
half size of window; i.e. window=1 uses a window of 3 voxels in each direction. |
z.scale |
ratio of voxel dimension in x/y direction and z direction. |
silent |
Logical. If FALSE, information on progress will be printed. |
Multi-dimensional array of filtered image data.
Choose a folder interactively by choosing a file in that folder.
folder.choose()folder.choose()
A character vector of length one giving the folder path.
Display an image stack
img( x, z = NULL, ch = NULL, mask = NULL, col = "grey", low = NULL, up = NULL, ... )img( x, z = NULL, ch = NULL, mask = NULL, col = "grey", low = NULL, up = NULL, ... )
x |
Image, 2D or 3D Matrix |
z |
slice to show, default: NULL, expects x to be 2d or 2d+channels |
ch |
channel. Default: NULL, either only one channel, rgb or channel will be assumed from col |
mask |
mask for image, voxel outside the mask will be transparent (default: NULL, no mask) |
col |
Color, either a character ("grey" or "gray", "greyinvert" or "grayinvert", "red" ("r"), "green" ("g") or "blue" ("b"), rgb" for 3D matrices), a vector of character with hex rgb values or a function. |
low |
minimal value of shown intensity. Default: NULL: use min(x, na.rm=TRUE). |
up |
maximal value of shown intensity. Default: NULL: use max(x, na.rm=TRUE). |
... |
other parameters for graphics::image |
no return
Computing the intensity of a 3d point pattern using kernel smoothing.
intensity3D(X, Y, Z, bw = NULL, psz = 25, kernel = "Square")intensity3D(X, Y, Z, bw = NULL, psz = 25, kernel = "Square")
X |
X coordinate |
Y |
Y coordinate |
Z |
Z coordinate |
bw |
bandwidth |
psz |
pointsize used for discretization (large: fast, but not precise) |
kernel |
"Square" or "Uniform" |
3d Array
Calculates an estimate of the cross-type K-function for a multitype point pattern.
K.cross.3D( X, Y, Z, X2, Y2, Z2, psz = 25, width = 1, intensity = NULL, intensity2 = NULL, parallel = FALSE, verbose = FALSE )K.cross.3D( X, Y, Z, X2, Y2, Z2, psz = 25, width = 1, intensity = NULL, intensity2 = NULL, parallel = FALSE, verbose = FALSE )
X |
X coordinate of first observed point pattern in microns. |
Y |
Y coordinate |
Z |
Z coordinate |
X2 |
X coordinate of second observed point pattern |
Y2 |
Y coordinate |
Z2 |
Z coordinate |
psz |
pointsize used for discretization. Smaller values are more precise, but need more computation time. |
width |
maximum distance |
intensity |
intensity of first pattern. Only if
. |
intensity2 |
intensity of second pattern |
parallel |
Logical. Can we use parallel computing? |
verbose |
Plot verbose information |
a list of breaks and counts.
Calculates an estimate of the cross-type L-function for a multitype point pattern.
L.cross.3D( X, Y, Z, X2, Y2, Z2, psz = 25, width = 1, intensity = NULL, intensity2 = NULL, parallel = FALSE, verbose = FALSE )L.cross.3D( X, Y, Z, X2, Y2, Z2, psz = 25, width = 1, intensity = NULL, intensity2 = NULL, parallel = FALSE, verbose = FALSE )
X |
X coordinate of first observed point pattern in microns. |
Y |
Y coordinate |
Z |
Z coordinate |
X2 |
X coordinate of second observed point pattern |
Y2 |
Y coordinate |
Z2 |
Z coordinate |
psz |
pointsize used for discretization. Smaller values are more precise, but need more computation time. |
width |
maximum distance |
intensity |
intensity of first pattern. Only if
. |
intensity2 |
intensity of second pattern |
parallel |
Logical. Can we use parallel computing? |
verbose |
Plot verbose information |
a list of breaks and counts.
Mexican hat brush to use with filter2
mexican.hat.brush(n = 7, sigma2 = 1)mexican.hat.brush(n = 7, sigma2 = 1)
n |
size of brush |
sigma2 |
standard deviation |
brush
Nearest neighbor distribution (D curve)
nearest.neighbour.distribution( X, Y, Z, X2 = X, Y2 = Y, Z2 = Z, same = TRUE, psz = 25, main = "Nearest neighbour distribution", file = NULL, return = FALSE )nearest.neighbour.distribution( X, Y, Z, X2 = X, Y2 = Y, Z2 = Z, same = TRUE, psz = 25, main = "Nearest neighbour distribution", file = NULL, return = FALSE )
X |
X coordinates of point pattern 1 |
Y |
Y coordinates of point pattern 1 |
Z |
Z coordinates of point pattern 1 |
X2 |
X coordinates of point pattern 2 |
Y2 |
Y coordinates of point pattern 2 |
Z2 |
Z coordinates of point pattern 2 |
same |
binary, FALSE for cross D curve |
psz |
pointsize for discretization |
main |
Title for graphic |
file |
File name for PNG file. If NULL, plots to standard device. |
return |
Logical. Return histogram? |
histogram of nearest neighbors
p<-read.csv(system.file("extdata","cell.csv",package="bioimagetools")) nearest.neighbour.distribution(p$X,p$Y,p$Z)p<-read.csv(system.file("extdata","cell.csv",package="bioimagetools")) nearest.neighbour.distribution(p$X,p$Y,p$Z)
Title Find distance to next neighbour of a specific class
nearestClassDistance(coord, img, class, voxelsize, step = 0)nearestClassDistance(coord, img, class, voxelsize, step = 0)
coord |
coordinate of relevant voxel |
img |
image array of classes |
class |
class to find |
voxelsize |
vector of length three. size of voxel in X-/Y-/Z-direction |
step |
size of window to start with |
distance to nearest voxel of class "class"
Find all distances to next neighbor of all classes
nearestClassDistances( img, voxelsize = NULL, size = NULL, classes = 7, maxdist = NULL, silent = FALSE, cores = 1 )nearestClassDistances( img, voxelsize = NULL, size = NULL, classes = 7, maxdist = NULL, silent = FALSE, cores = 1 )
img |
Image array of classes |
voxelsize |
Real size of voxels in microns. |
size |
Real size of image in microns. Either size or voxelsize must be given. |
classes |
Number of classes |
maxdist |
Maximum distance to consider |
silent |
Remain silent? |
cores |
Number of cores available for parallel computing |
array with distances
Segmentation of the background of 3D images based on classes
outside(img, what, blobsize = 1)outside(img, what, blobsize = 1)
img |
is a 3d array representing an image. |
what |
is an integer of the class of the background. |
blobsize |
is an integer, representing the minimal diameter for bridges from the outside. E.g., a blobsize=3 allows for holes of size 2*(blobsize-1)=4 in the edge of the object. |
A binary 3d array: 1 outside the object, 0 inside the object
Title Plot nearest class distances
plotNearestClassDistances( distances, method, classes = length(distances), ylim = c(0, 1), qu = 0.01, mfrow = NULL )plotNearestClassDistances( distances, method, classes = length(distances), ylim = c(0, 1), qu = 0.01, mfrow = NULL )
distances |
list of list with distances as produced by nearestClassDistances() |
method |
"boxplot", "min" or "quantile" |
classes |
number of classes, default=7 |
ylim |
limits for distances, default=c(0,1) |
qu |
quantile for method="quantile"; default 0.01 |
mfrow |
mfrow option forwarded to par; default NULL, computes some optimal values |
plots
Read 2D grey-value BMP files
readBMP(file)readBMP(file)
file |
A character vector of file names or URLs. |
Returns a matrix with BMP data as integer.
Volker J. Schmid
bi<-readBMP(system.file("extdata/V.bmp",package="bioimagetools")) image(bi,col=grey(seq(1,0,length=100)))bi<-readBMP(system.file("extdata/V.bmp",package="bioimagetools")) image(bi,col=grey(seq(1,0,length=100)))
Read TIF file with classes
readClassTIF(file, n = 7)readClassTIF(file, n = 7)
file |
file |
n |
number of classes |
array
Read tif stacks
readTIF(file = file.choose(), native = FALSE, as.is = FALSE, channels = NULL)readTIF(file = file.choose(), native = FALSE, as.is = FALSE, channels = NULL)
file |
Name of the file to read from. Can also be an URL. |
native |
determines the image representation - if FALSE (the default) then the result is an array, if TRUE then the result is a native raster representation (suitable for plotting). |
as.is |
attempt to return original values without re-scaling where possible |
channels |
number of channels |
3d or 4d array
kringel <- readTIF(system.file("extdata","kringel.tif",package="bioimagetools")) img(kringel)kringel <- readTIF(system.file("extdata","kringel.tif",package="bioimagetools")) img(kringel)
Segmentation of 3D images using EM algorithms
segment( img, nclust, beta, z.scale = 0, method = "cem", varfixed = TRUE, maxit = 30, mask = array(TRUE, dim(img)), priormu = rep(NA, nclust), priormusd = rep(NULL, nclust), min.eps = 10^{ -7 }, inforce.nclust = FALSE, start = NULL, silent = FALSE )segment( img, nclust, beta, z.scale = 0, method = "cem", varfixed = TRUE, maxit = 30, mask = array(TRUE, dim(img)), priormu = rep(NA, nclust), priormusd = rep(NULL, nclust), min.eps = 10^{ -7 }, inforce.nclust = FALSE, start = NULL, silent = FALSE )
img |
is a 3d array representing an image. |
nclust |
is the number of clusters/classes to be segmented. |
beta |
is a matrix of size nclust x nclust, representing the prior weight of classes neighboring each other. |
z.scale |
ratio of voxel dimension in x/y direction and z direction. Will be multiplied on beta for neighboring voxel in z direction. |
method |
only "cem" classification EM algorithm implemented. |
varfixed |
is a logical variable. If TRUE, the variance is equal in each class. |
maxit |
is the maximum number of iterations. |
mask |
is a logical array, representing the voxels to be used in the segmentation. |
priormu |
is a vector with mean of the normal prior of the expected values of all classes. Default is NA, which represents no prior assumption. |
priormusd |
is a vector with standard deviations of the normal prior of the expected values of all classes. |
min.eps |
stop criterion. Minimal change in sum of squared estimate of mean in order to stop. |
inforce.nclust |
if TRUE enforces number of clusters to be nclust. Otherwise classes might be removed during algorithm. |
start |
not used |
silent |
if TRUE, function remains silent during running time |
A list with "class": 3d array of class per voxel; "mu" estimated means; "sigma": estimated standard deviations.
## Not run: original<-array(1,c(300,300,50)) for (i in 1:5)original[(i*60)-(0:20),,]<-original[(i*60)-(0:20),,]+1 for (i in 1:10)original[,(i*30)-(0:15),]<-original[,(i*30)-(0:15),]+1 original[,,26:50]<-4-aperm(original[,,26:50],c(2,1,3)) img<-array(rnorm(300*300*50,original,.2),c(300,300,50)) img<-img-min(img) img<-img/max(img) try1<-segment(img,3,beta=0.5,z.scale=.3) print(sum(try1$class!=original)/prod(dim(original))) beta<-matrix(rep(-.5,9),nrow=3) beta<-beta+1.5*diag(3) try2<-segment(img,3,beta,z.scale=.3) print(sum(try2$class!=original)/prod(dim(original))) par(mfrow=c(2,2)) img(original) img(img) img(try1$class) img(try2$class) ## End(Not run)## Not run: original<-array(1,c(300,300,50)) for (i in 1:5)original[(i*60)-(0:20),,]<-original[(i*60)-(0:20),,]+1 for (i in 1:10)original[,(i*30)-(0:15),]<-original[,(i*30)-(0:15),]+1 original[,,26:50]<-4-aperm(original[,,26:50],c(2,1,3)) img<-array(rnorm(300*300*50,original,.2),c(300,300,50)) img<-img-min(img) img<-img/max(img) try1<-segment(img,3,beta=0.5,z.scale=.3) print(sum(try1$class!=original)/prod(dim(original))) beta<-matrix(rep(-.5,9),nrow=3) beta<-beta+1.5*diag(3) try2<-segment(img,3,beta,z.scale=.3) print(sum(try2$class!=original)/prod(dim(original))) par(mfrow=c(2,2)) img(original) img(img) img(try1$class) img(try2$class) ## End(Not run)
Segmentation of the background of 3D images. Starting from the borders of the image, the algorithm tries to find the edges of an object in the middle of the image. From this, a threshold for the edge is defined automatically. The function then return the a logical array representing voxel inside the object.
segment.outside(img, blobsize = 1)segment.outside(img, blobsize = 1)
img |
is a 3d array representing an image. |
blobsize |
is an integer, representing the minimal diameter for bridges from the outside. E.g., a blobsize=3 allows for holes of size 2*(blobsize-1)=4 in the edge of the object. |
A binary 3D array: 1 outside the object, 0 inside the object.
kringel <- readTIF(system.file("extdata","kringel.tif",package="bioimagetools")) out <- segment.outside(kringel) img(out, z=1)kringel <- readTIF(system.file("extdata","kringel.tif",package="bioimagetools")) out <- segment.outside(kringel) img(out, z=1)
Find spots based on threshold and minimum total intensity
spots( img, mask, thresh.offset = 0.1, window = c(5, 5), min.sum.intensity = 0, zero = NA, max.spots = NULL, return = "intensity" )spots( img, mask, thresh.offset = 0.1, window = c(5, 5), min.sum.intensity = 0, zero = NA, max.spots = NULL, return = "intensity" )
img |
image array. |
mask |
mask array. |
thresh.offset |
threshold for minimum voxel intensity. |
window |
Half width and height of the moving rectangular window. |
min.sum.intensity |
threshold for minimum total spot intensity |
zero |
if NA, background is set to NA, if 0, background is set to 0. |
max.spots |
find max.spots spots with highest total intensity. |
return |
"mask" returns binarized mask, "intensity" returns intensity for spots, zero or NA otherwise "label" return labeled (numbered) spots. |
array
Standardizes images in order to compare different images. Mean of standardized image is 0.5, standard deviation is sd.
standardize(img, mask = array(TRUE, dim(img)), log = FALSE, N = 32, sd = 1/6)standardize(img, mask = array(TRUE, dim(img)), log = FALSE, N = 32, sd = 1/6)
img |
is a 2d/3d array representing an image. |
mask |
a mask. |
log |
Logical. Transform to log scale before standardization? |
N |
number of classes. |
sd |
standard deviation. |
Multi-dimensional array of standardized image.
#simuliere Daten zum Testen test2<-runif(128*128,0,1) test2<-sort(test2) test2<-array(test2,c(128,128)) img(test2) # Standardisiere test2 in 32 Klassen std<-standardize(test2,N=32,sd=4)#simuliere Daten zum Testen test2<-runif(128*128,0,1) test2<-sort(test2) test2<-array(test2,c(128,128)) img(test2) # Standardisiere test2 in 32 Klassen std<-standardize(test2,N=32,sd=4)
Cross Tabulation and Table Creation (including empty classes)
table.n( x, m = max(x, na.rm = TRUE), percentage = FALSE, weight = NULL, parallel = FALSE )table.n( x, m = max(x, na.rm = TRUE), percentage = FALSE, weight = NULL, parallel = FALSE )
x |
R object with classes |
m |
maximum number of classes |
percentage |
boolean. If TRUE result is in percentages. |
weight |
weight for each voxel |
parallel |
Logical. Can we use parallel computing? |
vector with (weighted) counts (including empty classes)
Volker Schmid 2013-2016
x <- c(1,1,2,2,4,4,4) table.n(x) # [1] 2 2 0 3 table.n(x, m=5) # [1] 2 2 0 3 0 table.n(x, weight=c(1,1,1,2,.5,.5,.5)) # [1] 2.0 3.0 0.0 1.5x <- c(1,1,2,2,4,4,4) table.n(x) # [1] 2 2 0 3 table.n(x, m=5) # [1] 2 2 0 3 0 table.n(x, weight=c(1,1,1,2,.5,.5,.5)) # [1] 2.0 3.0 0.0 1.5
Permutation Test for cross-type nearest neighbor distances
testColoc( im1, im2, hres = 0.102381, vres = 0.25, B = 999, alternative = "less", returnSample = TRUE, ... )testColoc( im1, im2, hres = 0.102381, vres = 0.25, B = 999, alternative = "less", returnSample = TRUE, ... )
im1 |
image stack as returned by preprocessing |
im2 |
image stack as returned by preprocessing |
hres |
horizontal resolution of the stacks |
vres |
vertical resolution of the stacks |
B |
number of permutations to generate |
alternative |
alternative hypothesis ("less" to test H0:Colocalization ) |
returnSample |
return sampled null distribution |
... |
additional arguments for papply |
a list with the p.value, the observed weighted mean of the cNN-distances
Fabian Scheipl
Writes image stack into a TIFF file. Wrapper for writeTIFF
writeTIF( img, file, bps = attributes(img)$bits.per.sample, twod = FALSE, reduce = TRUE, attr = attributes(img), compression = "none" )writeTIF( img, file, bps = attributes(img)$bits.per.sample, twod = FALSE, reduce = TRUE, attr = attributes(img), compression = "none" )
img |
An image, a 3d or 4d array. |
file |
File name. |
bps |
number of bits per sample (numeric scalar). Supported values in this version are 8, 16, and 32. |
twod |
Dimension of channels. TRUE for 2d images, FALSE for 3d images. |
reduce |
if TRUE then writeTIFF will attempt to reduce the number of planes in native rasters by analyzing the image to choose one of RGBA, RGB, GA or G formats, whichever uses the least planes without any loss. Otherwise the image is always saved with four planes (RGBA). |
attr |
Attributes of image stack. Will be propagated to each 2d image. |
compression |
(see ?writeTIFF) |