Colocalization

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.

Table of contents

1. Installation

2. Usage

   2.1 Supported Metrics

   2.2 Demo

   2.3 Documentation

3. Cite

4. FAQ

5. Related projects and tools

Installation

• Download Julia

Using as a package

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

Cloning the repository

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.

On Command line

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

Usage

Supported Metrics

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

In silico example

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 visualzaition 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.

Documentation

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.

Cite

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!!!.

FAQ

• To display the images, you need to install ImageView

using Pkg
Pkg.add("ImageView")

Related software

FiJi:

• https://imagej.net/plugins/coloc-2
• https://github.com/fiji/Colocalisation_Analysis

This package would not be possible without the Julia Images ecosystem