This repository has been archived by the owner. It is now read-only.
R functions allowing for the assessment of colocalization in a High-Througput fashion
Switch branches/tags
Nothing to show
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Failed to load latest commit information.
Coloc Parameters.txt
Function description.txt


R functions allowing for the assessment of colocalization in a High-Througput fashion.



The provided piece of code was tested on both Linux (Ubuntu 16.04) and Windows (Windows 7) distributions. It has not been tested on Mac OS but should theoretically work. In any case, you will need to have the proper Java version (64 or 32 bits according to your machine) installed on your computer in the aim to have rJava package added to R.

For Linux distributions, you might need to install additionnal packages such as fftw3 so that you can install the R tiff package:

  • sudo apt-get install libfftw3-dev libfftw3-doc


Before using the provided function you will obviously need to have R installed:

I advise you to use a GUI, such as RStudio, to make its use simpler:

You will need to have a few packages installed. For doing so, open R and run these lines in the console:

  • source("")
  • biocLite(pkgs=c('EBImage','flowCore'), ask=F)
  • install.packages(pkgs = c('shiny','tiff','reshape','RODBC','foreach','doParallel','stringi','naturalsort','rChoiceDialogs'))

or simply source the InstallPackages.R after opening it with R.


Open RGui/RStudio and open the provided scripts GetImInfo.R,GetAllSQLInfo-FAST.R,ColocPixelAnalysis_Func.R (in the Functions folder). After sourcing each of them, you can type in the console :

  • coloc.HTS(getImInfo(SQL.use = F))

A first window will open to select the location of the images you want to analyze. After selecting the folder in which results will be exported in a second window, the analysis willl start. If you want to see a more detailed description of the functions and their arguments, please refer to Function description.txt

File format

GetImInfo.R will only run with a specific nomenclature regarding images names.

  • First, the images will need to be in *.tiff file format.
  • The names should be given according to the well, site, and channel that have been acquired : ExpName_WellName_sSiteNumber_wChannelNumber

For example, if you have acquired an image from the well A01 of a plate, on site 1, using the first channel of your microscope, its name should be MyExp_A01_s1_w1.TIF

  • If you wish to analyze a timecourse experiment, images from each timepoint should be stored in a folder named TimePoint_Time, with the same nomenclature that described previously.

For example, if you ran an experiment with 10 timepoints, images shall be stored in folders named TimePoint_1 to TimePoint_10. Obviously, each batch of images in each folder will have the same name.


Inside the choosen results location, you should find 2 files and a serie of subfolders in a folder called "Results".

  • The first file, Results.csv, is a comma-delimited table in which calculated colocalization parameters are recorded for each acquired site. A more detailed description of these parameters can be consulted in Coloc parameters.txt.

  • The second, namely Boxplot.pdf, is an informative boxplot depicting summarized results well by well (grouping sites altogether)

  • Every subfolder (one per timepoint) contains directories named according to the wells that were acquired. Each well directory contains as many pdfs as they ares sites inside the well, and one image. The pdf shows the bi-parametric plot of pixel intensities in assesed channels, together with the associated colocalization parameters. The recorder image is a color-combined image from site 1, in which detected Green and Red signals are detected. This image can be useful to refine segmentation parameters (see Function description.txt).


Once installed, the suite can be tested on the provided test images in the Testing-images folder. Please refer to Images info.txt for more information about these images. After sourcing the R scripts, type in the console :

  • coloc.HTS(getImInfo(SQL.use = F), Cyto = 'Red')

And select the folder where you stored the previously downloaded images.