| Title: | Nucleome Imaging Toolbox |
| Version: | 1.0.11 |
| Date: | 2021-06-09 |
| Author: | Volker Schmid [aut, cre] |
| Maintainer: | Volker Schmid <volker.schmid@lmu.de> |
| Depends: | R (≥ 3.0.0), EBImage, bioimagetools (≥ 1.1.4) |
| Imports: | fields, parallel, stringr |
| SystemRequirements: | tiff fftw libcurl openssl |
| Description: | Tools for 4D nucleome imaging. Quantitative analysis of the 3D nuclear landscape recorded with super-resolved fluorescence microscopy. See Volker J. Schmid, Marion Cremer, Thomas Cremer (2017) <doi:10.1016/j.ymeth.2017.03.013>. |
| License: | GPL-3 |
| URL: | https://bioimaginggroup.github.io/nucim/ |
| RoxygenNote: | 7.1.0 |
| Suggests: | knitr, rmarkdown, R.rsp |
| VignetteBuilder: | knitr, R.rsp |
| BugReports: | https://github.com/bioimaginggroup/nucim/issues |
| NeedsCompilation: | no |
| Packaged: | 2021-06-09 20:43:29 UTC; schmid |
| Repository: | CRAN |
| Date/Publication: | 2021-06-10 04:40:05 UTC |
Barplot with Intervals
Description
Barplot with Intervals
Usage
barplot_with_interval(
x,
method = "minmax",
qu = c(0, 1),
ylim = NULL,
horiz = FALSE,
border = NA,
...
)
Arguments
x |
matrix |
method |
method for intervals: "minmax" (default), "quantile" or "sd" |
qu |
vector of two quantiles for method="quantile |
ylim |
limits for y axis. Default:NULL is ylim=c(0,max(interval)) |
horiz |
boolean: horizontal bars? |
border |
border parameter forwarded to barplot, default: NA is nor border |
... |
additional parameters forwarded to barplot |
Value
plot
Barplot with Intervals for two or three bars beside
Description
Barplot with Intervals for two or three bars beside
Usage
barplot_with_interval_23(x, method = "minmax", qu = c(0, 1), ylim = NULL, ...)
Arguments
x |
array |
method |
method for intervals: "minmax" (default), "quantile" or "sd" |
qu |
vector of two quantiles for method="quantile |
ylim |
limits for y axis. Default:NULL is ylim=c(0,max(interval)) |
... |
additional parameters forwarded to barplot |
Value
plot
Class neighbourhood distribution
Description
Class neighbourhood distribution
Usage
class.neighbours(img, N, N.max = 7, cores = 1)
Arguments
img |
Class image |
N |
which class |
N.max |
maximum class (default: 7) |
cores |
number of cores used in parallel (needs parallel package) |
Value
vector of length N.max
class.neighbours.folder
Description
class.neighbours.folder
Usage
class.neighbours.folder(inputfolder, outputfolder, N = 7)
Arguments
inputfolder |
Input folder |
outputfolder |
Output folder |
N |
Max class #' |
Value
plots
Classify DAPI
Description
Classify DAPI
Usage
classify(blue, mask, N, beta = 0.1, z = 1/3, silent = TRUE)
Arguments
blue |
DAPI channel (image) |
mask |
mask (image) |
N |
number of classes |
beta |
smoothing parameter used in potts model (default: 0.1) |
z |
scaling parameter: size of voxel in X-/Y-direction divided by the size of voxel in Z-direction (slice scaling parameter: size of voxel in X-/Y-direction divided by the size of voxel in Z-direction (slice thickness)) |
silent |
boolean. Should algorithm be silent? |
Value
image with classes
Classify DAPI
Description
Classify DAPI
Usage
classify.folder(f, N, beta = 0.1, output = paste0("class", N), cores = 1)
Arguments
f |
folder |
N |
number of classes |
beta |
beta parameter used in bioimagetools::segment() |
output |
output folder |
cores |
number of cores used in parallel (needs parallel package) |
Value
results in "output" and "output"-n
Classify DAPI
Description
These functions are provided for compatibility with older version of the nucim package. They may eventually be completely removed.
Usage
classify.single(...)
Arguments
... |
parameters for classify |
Value
image with classes
Count classes in classified image
Description
Count classes in classified image
Usage
classify.table(class, N)
Arguments
class |
classes image |
N |
number of classes |
Value
table with number of voxels per class
Compute colors in classes distribution
Description
Compute colors in classes distribution
Usage
colors.in.classes(
classes,
color1,
color2 = NULL,
mask = array(TRUE, dim(classes)),
N = max(classes, na.rm = TRUE),
type = "tresh",
thresh1 = NULL,
thresh2 = NULL,
sd1 = 2,
sd2 = 2,
col1 = "green",
col2 = "red",
test = FALSE,
plot = TRUE,
beside = TRUE,
ylim = NULL,
verbose = FALSE,
...
)
Arguments
classes |
Image of classes |
color1 |
Image of first color |
color2 |
Image of second color |
mask |
Image mask |
N |
Maximum number of classes |
type |
Type of spot definition, see details |
thresh1 |
Threshold for first color image |
thresh2 |
Threshold for second color image |
sd1 |
For automatic threshold, that is: mean(color1)+sd1*sd(color1) |
sd2 |
For automatic threshold of color2 |
col1 |
Name of color 1 |
col2 |
Name of color 2 |
test |
Compute tests: "Wilcoxon" for Wilcoxon rank-sum (Mann-Whitney U), chisq for Chi-squared test |
plot |
Plot barplots |
beside |
a logical value. If FALSE, the columns of height are portrayed as stacked bars, and if TRUE the columns are portrayed as juxtaposed bars. |
ylim |
limits for the y axis (plot) |
verbose |
verbose mode |
... |
additional plotting parameters |
Details
Type of spot definitions: "thresh" or "t": Threshold based (threshold can be given by thresh1/2 or automatically derived) "voxel" or "v": Spots are given as binary voxel mask "intensity" or "i": Voxels are weighted with voxel intensity. Intensity is scaled to [0,1] after subtracting thresh1/2 (or automatic threshold)
Value
Table of classes with color 1 (and 2)
Compute colors in classes distribution for folders
Description
Compute colors in classes distribution for folders
Usage
colors.in.classes.folder(
path,
color1,
color2 = NULL,
N = 7,
type = "intensity",
thresh1 = NULL,
thresh2 = NULL,
sd1 = 2,
sd2 = 2,
col1 = "green",
col2 = "red",
cores = 1,
verbose = FALSE
)
Arguments
path |
Path to root folder |
color1 |
Image of first color |
color2 |
Image of second color |
N |
Maximum number of classes |
type |
Type of spot definition, see details |
thresh1 |
Threshold for first color image |
thresh2 |
Threshold for second color image |
sd1 |
For automatic threshold, that is: mean(color1)+sd1*sd(color1) |
sd2 |
For automatic threshold of color2 |
col1 |
Name of color 1 |
col2 |
Name of color 2 |
cores |
Number of cores used in parallel, cores=1 implies no parallelization |
verbose |
verbose mode |
Value
Results are in folder colorsinclasses
Compute distance to border of classes
Description
Compute distance to border of classes
Usage
compute.distance2border(
f,
color,
N,
from.spots = FALSE,
output = "dist2border",
cores = 1
)
Arguments
f |
folder of classes images |
color |
folder of color images ("spots-"color for spots images) |
N |
which class |
from.spots |
Logical. |
output |
output folder |
cores |
number of parallel cores which can be used |
Value
images in output"-"color"-"N
Mask DAPI in kernel
Description
Mask DAPI in kernel
Usage
dapimask(
img,
size = NULL,
voxelsize = NULL,
thresh = "auto",
silent = TRUE,
cores = 1
)
Arguments
img |
DAPI channel image (3d) |
size |
size of img in microns |
voxelsize |
size of voxel in microns |
thresh |
threshold for intensity. Can be "auto": function will try to find automatic threshold |
silent |
Keep silent? |
cores |
number of cores available for parallel computing |
Value
mask image, array with same dimension as img.
Automatic DAPI mask segmentation for files
Description
Automatic DAPI mask segmentation for files
Usage
dapimask.file(
file,
folder = "blue",
voxelsize = NULL,
size = NULL,
silent = FALSE,
cores = 1
)
Arguments
file |
file to read |
folder |
with |
voxelsize |
real size of voxel (in microns), if NULL (default), look in folder XYZmic |
size |
real size of image (in microns), if NULL (default), look in folder XYZmic |
silent |
Keep silent? |
cores |
Number of cores available for parallel computing |
Value
nothing, DAPI mask image will be saved to dapimask/
Automatic DAPI mask segmentation for folder
Description
Automatic DAPI mask segmentation for folder
Usage
dapimask.folder(
path,
folder = "blue",
voxelsize = NULL,
size = NULL,
cores = 1
)
Arguments
path |
path to folder with DAPI |
folder |
folder with DAPI images |
voxelsize |
real size of voxel (in microns), if NULL (default), look in folder XYZmic |
size |
real size of image (in microns), if NULL (default), look in folder XYZmic |
cores |
number of cores to use in parallel (need parallel package) |
Value
nothing, results are in folder dapimask
Detects spots for one file
Description
Detects spots for one file
Usage
find.spots.file(
file,
dir,
color,
thresh = NULL,
thresh.auto = FALSE,
thresh.quantile = 0.9,
filter = NULL,
cores = 1
)
Arguments
file |
file |
dir |
directory for results |
color |
which color, images have to be in folder with color name |
thresh |
threshold |
thresh.auto |
Logical. Find threshold automatically? |
thresh.quantile |
numeric. use simple |
filter |
2d-filter to use before spot detection |
cores |
number of cores to use in parallel (with parallel package only) |
Value
spot images in spot-color/, number of spots as txt files in spot-color/
Detects spots
Description
Detects spots
Usage
find.spots.folder(
f,
color,
thresh = 1,
thresh.auto = TRUE,
filter = NULL,
cores = 1
)
Arguments
f |
path to folder |
color |
which color, images have to be in folder with color name |
thresh |
threshold |
thresh.auto |
Logical. Find threshold automatically? |
filter |
2d-filter to use before spot detection |
cores |
number of cores to use in parallel (with parallel package only) |
Value
spot images in spot-color/, number of spots as txt files in spot-color/
Heatmap colors for n classes
Description
Heatmap colors for n classes
Usage
heatmap.color(n)
Arguments
n |
number of colors. |
Examples
barplot(8:1,col=heatmap.color(8))
Heatmap colors for 7 classes
Description
Heatmap colors for 7 classes
Usage
heatmap7(...)
Arguments
... |
parameters are ignored. |
Examples
barplot(7:1,col=heatmap7())
Find all distances to next neighbour of all classes for folders
Description
Find all distances to next neighbour of all classes for folders
Usage
nearestClassDistances.folder(
path,
N = 7,
voxelsize = NULL,
add = FALSE,
cores = 1
)
Arguments
path |
path to folder |
N |
number of classes, default: 7 |
voxelsize |
real size of voxels (in microns), if NULL (default), look in folder XYZmic |
add |
if TRUE, only process images which have not been processed before (i.e. have been added to classN) |
cores |
number of cores to use in parallel (needs parallel package if cores>1) |
Value
nothing, results are in folder distances in RData format
Plot barplot for classified images in a folder
Description
Plot barplot for classified images in a folder
Usage
plot_classify.folder(
path,
N = 7,
cores = 1,
col = grDevices::grey(0.7),
method = "sd"
)
Arguments
path |
path to folder |
N |
number of classes, default: 7 |
cores |
number of cores to use in parallel (needs parallel package if cores>1) |
col |
color of bars, either one or a vector of hex RGB characters |
method |
method for error bars ("sd", "minmax", "quartile") |
Value
plots
Plot for colors in classes distribution for folders
Description
Plot for colors in classes distribution for folders
Usage
plot_colors.in.classes.folder(path, col1 = "green", col2 = "red")
Arguments
path |
path to folder |
col1 |
color of channel 1 |
col2 |
color of channel 2 |
Value
plot
Plots all distances to next neighbour of all classes for folders
Description
Plots all distances to next neighbour of all classes for folders
Usage
plot_nearestClassDistances.folder(
path,
N = 7,
cores = 1,
method = "quantile",
qu = 0.01
)
Arguments
path |
path to folder |
N |
number of classes, default: 7 |
cores |
number of cores to use in parallel (needs parallel package if cores>1) |
method |
method for summarizing distances, either "min" or "quantile" |
qu |
quantile for method="quantile", default: 0.01 |
Value
plots
Split RGB channels
Description
Split RGB channels
Usage
splitchannel(img, preprocess = TRUE)
Arguments
img |
rgb image |
preprocess |
logical. Should preprocessing be applied? |
Value
list with red, green, blue channels and size in microns.
Split RGB images into channels and pixel size information
Description
These functions are provided for compatibility with older version of the nucim package. They may eventually be completely removed.
Usage
splitchannels(...)
Arguments
... |
parameters for splitchannels.folder |
Value
Nothing, folders red, green, blue and XYZmic include separate channels and pixel size information
Split channels into files and extracts size in microns
Description
Split channels into files and extracts size in microns
Usage
splitchannels.file(file, channels, rgb.folder, normalize = FALSE)
Arguments
file |
file name |
channels |
e.g. c("red","green","blue") |
rgb.folder |
folder with file |
normalize |
boolean. Should we try to do normalization? |
Value
files in "./red/", "./green", "./blue" and "./XYZmic"
Split RGB images into channels and pixel size information
Description
Split RGB images into channels and pixel size information
Usage
splitchannels.folder(
path,
channels = c("red", "green", "blue"),
rgb.folder = "rgb",
normalize = FALSE,
cores = 1
)
Arguments
path |
Path to root folder |
channels |
Vector of channels in images |
rgb.folder |
Folder with RGB images |
normalize |
boolean. Should we try to do normalization |
cores |
Number of cores used in parallel, cores=1 implies no parallelization |
Value
Nothing, folders red, green, blue and XYZmic include separate channels and pixel size information
Examples
splitchannels.folder("./")
Find spots using information from two channels
Description
Find spots using information from two channels
Usage
spots.combined(
red,
green,
mask,
size = NULL,
voxelsize = NULL,
thresh.offset = 0.1,
window = c(5, 5),
min.sum.intensity = 2,
max.distance = 0.5,
use.brightest = FALSE,
max.spots = NA,
full.voxel = FALSE
)
Arguments
red |
image |
green |
image |
mask |
image mask |
size |
size of img in microns |
voxelsize |
size of voxel in microns |
thresh.offset |
Thresh offset used in EBImage::thresh() |
window |
Half width and height of the moving rectangular window. |
min.sum.intensity |
spots smaller than min.sum.intensity are ignored |
max.distance |
use only spots with distance to other color spot smaller than max.distance |
use.brightest |
Logical; use only brightest in max.distance? |
max.spots |
maximum of spots (per channel), only when use brightest=TRUE |
full.voxel |
Logical; output contains full voxel instead of rgb intensities |
Value
RGB image with spots will be written to output folder
Find spots using information from two channels
Description
Find spots using information from two channels
Usage
spots.combined.file(
file,
size = NULL,
voxelsize = NULL,
folder = "./",
thresh.offset = 0.1,
min.sum.intensity = 2,
max.distance = 0.5,
use.brightest = FALSE,
max.spots = 2,
full.voxel = FALSE,
output = "markers"
)
Arguments
file |
File name |
size |
size of img in microns, if size and voxelsize are NULL, size is determined from folder XYZmic |
voxelsize |
size of voxel in microns |
folder |
Folder |
thresh.offset |
Thresh offset used in EBImage::thresh() |
min.sum.intensity |
spots smaller than min.sum.intensity are ignored |
max.distance |
use only spots with distance to other color spot smaller than max.distance |
use.brightest |
Logical; use only brightest in max.distance? |
max.spots |
maximum of spots (per channel), only when use brightest=TRUE |
full.voxel |
Logical; output contains full voxel instead of rgb intensities |
output |
output folder |
Value
RGB image with spots will be written to output folder
Find spots using information from two channels for folder
Description
Find spots using information from two channels for folder
Usage
spots.combined.folder(
path,
size = NULL,
voxelsize = NULL,
thresh.offset = 0.1,
min.sum.intensity = 2,
max.distance = 0.5,
use.brightest = FALSE,
max.spots = 2,
full.voxel = FALSE,
output = "markers",
cores = 1
)
Arguments
path |
path to folder |
size |
size of img in microns, if size and voxelsize are NULL, size is determined from folder XYZmic |
voxelsize |
size of voxel in microns |
thresh.offset |
Thresh offset used in EBImage::thresh() |
min.sum.intensity |
spots smaller than min.sum.intensity are ignored |
max.distance |
use only spots with distance to other color spot smaller than max.distance |
use.brightest |
Logical; use only brightest in max.distance? |
max.spots |
maximum of spots (per channel), only when use brightest=TRUE |
full.voxel |
Logical; output contains full voxel instead of rgb intensities |
output |
output folder |
cores |
number of cores we can use of parallel computing (needs parallel package if cores>1) |
Value
RGB image with spots will be written to output folder
Test for colors in classes distribution for folders
Description
Test for colors in classes distribution for folders
Usage
t_colors.in.classes.folder(path, test = "Wilcoxon")
Arguments
path |
path to folder |
test |
"Wilcoxon", "wilcox" or "U" for Wilcoxon rank-sum (Mann-Whitney U), chisq for Chi-squared test |
Value
test results