cross_ratio | R Documentation |
Function to calculate 3D cross-ratio between three channels. The ratio is between first and second target channel at the distance from the focal channel.
cross_ratio( imgs, focal.channel, target.channels, size, npixel, R = 10, dstep = 1, pwidth, zstep, cores = 1, kern.smooth = NULL, layers = NULL, naming = NULL ) cross_ratio.default( imgs, focal.channel, target.channels, size, npixel, dstep = 1, pwidth, zstep, cores = 1, kern.smooth = NULL, layers = NULL, naming = NULL )
imgs |
The paths of array files; i.e. output from |
focal.channel |
Character of name of channel of focus. |
target.channels |
Character vector with names of the two target channels to calculate cross-ratio between |
size |
The maximum distance (microns) to examine. Has to be a multiple of both pwidth and zstep. Beware, increasing size will increase runtime exponetially! |
npixel |
Number of random pixels to examine. Increasing this will increase precision (and runtime in a linear fashion) |
R |
Number of times to run the cross-ratio analysis |
dstep |
The interval between examined distances (microns). Increasing this decreases resolution but speeds up function linearly. Defaults to 1 |
pwidth |
Width of pixels in microns |
zstep |
z-step in microns |
cores |
The number of cores to use.Defaults to 1 |
kern.smooth |
Optional. Numeric vector indicating range of median smoothing in the x,y,z directions. Has to be odd intergers. c(1,1,1) means no smoothing. |
layers |
Optional. Should the function only look in a subset of layers. A list with lists of layers to use for each image. Can also be the output from |
naming |
Optional. Add metadata to the output dataframe by looking through names of array files. Should be a list of character vectors, each list element will be added as a variable. Example: naming=list(Time=c("T0","T1","T2")). The function inserts a variable called Time, and then looks through the names of the array files and inserts characters mathcing either T0, T1 or T2 |
A dataframe with the cross-ratio values for each distance
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