A Julia package providing colocalization metrics for images and their sparse representations.
This package allows you to quickly run all metrics, and report the results both in image and CSV format.
Colocalization is used often in multichannel microscopy to quantify functional interaction between fluorescently marked proteins or subcellular organelles. Note that colocalization in superresolution microscopy has to be very carefully applied, as with increasing precision no two objects can share the same location at the same time.
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2.2 Demo
2.3 Documentation
- Download Julia
Start Julia (in VSCode or Command line)
julia
In Julia
using Pkg
# Optionally, activate your environment
# Pkg.activate("path/to/your/environment")
Pkg.add(url="https://github.com/bencardoen/Colocalization.jl")
using Colocalization
This assumes you have Git installed and configured.
git clone https://github.com/bencardoen/Colocalization.jl.git
cd Colocalization.jl
julia --project=.
Then, in Julia:
using Pkg
Pkg.instantiate()
using Colocalization
That's it.
Let's say you have 2 image files a.tif
and b.tif
.
julia --project=. scripts/colocalize.jl -f a.tif -s b.tif --outdir . --segment -w 3
You can get an up to date listing of the supported metrics by running the following code:
using Colocalization, Logging
@info list_metrics()
or access the actual functions:
for (name, metric) in metrics_iterator()
@info name, metric
end
Let's create 2 objects with variable levels of fluorescence labelling, that overlap by 50%.
using Images, Statistics, Distributions, Colocalization, ImageFiltering, Random
X, Y = 100, 100
xs = zeros(X, Y)
ys = zeros(X, Y)
xs[40:50, 40:50] .= rand(11, 11)
ys[45:55, 45:55] .= rand(11, 11)
sx = ImageFiltering.imfilter(xs, ImageFiltering.Kernel.gaussian((3, 3)))
sy = ImageFiltering.imfilter(ys, ImageFiltering.Kernel.gaussian((3, 3)))
We'll add some noise to make things realistic
s2x = copy(sx)
s2y = copy(sy)
s2x .+= rand(100, 100) ./ 10
s2y .+= rand(100, 100) ./ 10
View the results
using SPECHT, ImageView
imshow(mosaicview( [SPECHT.tcolors([xs, ys]), SPECHT.tcolors([sx, sy]), SPECHT.tcolors([s2x, s2y])], nrow=1))
The visualzation snippet uses SPECHT and Imageview, if you don't have them:
using Pkg
Pkg.add("ImageView")
Pkg.add(url="https//github.com/bencardoen/SPECHT.jl")
This should produce something like this image
Now, we compute all coloc metrics
results = colocalize_all(s2x, s2y)
Let's view the results, the metrics from left to right are: spearman, m2, m1, jaccard, manders, sorensen, pearson
mv = mosaicview([abs.(results[k]) for k in keys(results)], nrow=1)
imshow(mv)
Clearly, the noise is throwing a wrench in things. Metrics like Jacard, M1 and so forth expect segmented images to work on. Let's do a quick segmentation.
xt = otsu_threshold(s2x)
yt = otsu_threshold(s2y)
s2x[s2x.<xt] .= 0
s2y[s2y.<yt] .= 1
results = colocalize_all(s2x, s2y)
mv = mosaicview([abs.(results[k]) for k in keys(results)], nrow=1)
imshow(mv)
Which should produce something like the below image.
The documentation of the functions describes proper usage and meaning of parameters, to access it:
using Colocalization
?colocalize_all
The ?
key invokes Julia documentation, tools/IDES such as VSCode/Atom would have built in documentation panes.
If you find this useful, consider citing:
@software{ben_cardoen_2023_7552357,
author = {Ben Cardoen},
title = {Colocalization.jl},
month = jan,
year = 2023,
publisher = {Zenodo},
doi = {10.5281/zenodo.7552357},
url = {https://doi.org/10.5281/zenodo.7552357}
}
Note For the individual metrics, please cite the introducing author!!!.
- To display the images, you need to install ImageView
using Pkg
Pkg.add("ImageView")
If you have any problems or suggestions, please create an issue
FiJi:
This package would not be possible without the Julia Images ecosystem
Sure, please create an issue describing the metric mathematically, ideally accompanied by the introducing paper.