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Showing content from https://cran.r-project.org/web/packages/sitar/../rmarkdown/../nucim/vignettes/workflow.html below:

Workflow

Install nucim using install.packages(). The package relies on the EBImage package, which is available on bioconductor, hence you need to set the repositories accordingly:

Start with loading the necessary libraries:

In the following, we use an example dataset available online. Use the readTIF function in bioimagetools to load the RGB image.

The dapimask function automatically finds the kernel using the DAPI (blue) channel. This takes about 30 seconds (all computation times are using MacBookPro 2019, 2,4 GhZ Quad-Core i5, 16 GB RAM)

Next we find compaction classes from the DAPI channel (Computation time around 180 sec.).

Barplot of classes and heatmap, finally save the classes image.

Find the distances between compaction classes (computation time around 480 seconds, due to parallelization this depends on number of cores). The plotting function defines what we mean by distance: the minimum distance between class voxels, a quantile or plot a boxplot.

classes<-readClassTIF("classes.tif")
distances = nearestClassDistances(classes,  voxelsize=c(x,y,z), classes=7, cores=4L)
save(distances,file="distances.Rdata")
plotNearestClassDistances(distances, method="min",ylim=c(0,.1),qu=.01)
plotNearestClassDistances(distances, method="quantile",ylim=c(0,.22),qu=.01)
plotNearestClassDistances(distances, method="boxplot",ylim=c(0,1.5))

We can compute the distribution of other color channels on compaction classes. Here we use a threshold approach and an intensity-weighted approach for comparison. A test on independence of color channels and compaction classes is automatically done (computation time around 4 sec.).

red = img[,,1,]
green = img[,,2,]
cc1<-colors.in.classes(classes,red,green,mask,7,type="thresh",plot=TRUE,
                       col1="red",col2="green",thresh1=0.05,thresh2=0.05,
                       test="Wilcoxon",ylim=c(0,.5),
                       xlab="chromatin compaction levels",ylab="percentage")

## Wilcoxon rank-sum test DAPI vs. channel 1: p-value < 5e-09
## Wilcoxon rank-sum test DAPI vs. channel 2: p-value < 5e-09
## Wilcoxon rank-sum test channel 1 vs. channel 2: p-value = 0.00021042
cc2<-colors.in.classes(classes,red,green,mask,7,type="intensity",plot=TRUE,
                       col1="red",col2="green",thresh1=0.05,thresh2=0.05,
                       test="Wilcoxon",ylim=c(0,.5),
                       xlab="chromatin compaction levels",ylab="percentage")

## Wilcoxon rank-sum test DAPI vs. channel 1: p-value < 5e-09
## Wilcoxon rank-sum test DAPI vs. channel 2: p-value < 5e-09
## Wilcoxon rank-sum test channel 1 vs. channel 2: p-value = 0.00362231

Alternatively, we can find spots first and then use this to find the distribution on compaction classes (computation time spots() around 90 sec.).

cc<-colors.in.classes(classes,spots$red,spots$green,mask,7,type="i",plot=TRUE,
                      col1="red",col2="green",test="Wilcoxon",ylim=c(0,.8),
                      xlab="chromatin compaction levels",ylab="percentage")

## Wilcoxon rank-sum test DAPI vs. channel 1: p-value < 5e-09
## Wilcoxon rank-sum test DAPI vs. channel 2: p-value < 5e-09
## Wilcoxon rank-sum test channel 1 vs. channel 2: p-value = 6.801e-05

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