nqh2412 / colocr

An R package for conducting co-localization analysis. Edit

colocr

An R package for conducting co-localization analysis.

Overview

A few R packages are available for conducting image analysis, which is a very wide topic. As a result, some of us might feel at a loss when all they want to do is a simple co-localization calculations on a small number of microscopy images. This package provides a simple straight forward workflow for loading images, choosing regions of interest (ROIs) and calculating co-localization statistics. Included in the package, is a shiny app that can be invoked locally to interactively select the regions of interest in a semi-automatic way. The package is based on the R package imager.

Installing colocr

The package development version is available at github.

# install from github
devtools::install_github('MahShaaban/colocr')

Getting started

To get started, load the required packages and the images. The images below are from DU145 cell line and were stained for two proteins; RKIP and LC3. Then, apply the appropriate parameters for choosing the regions of interest using the roi_select. Finally, check the appropriateness of the parameters by highlighting the ROIs on the image.

# load libraries
library(colocr)

# load images
fl <- system.file('extdata', 'Image0001_.jpg', package = 'colocr')
img <- image_load(fl)

# select ROI and show the results
par(mfrow = c(2,2), mar = rep(1, 4))

img %>%
  roi_select(threshold = 90) %>%
  roi_show()

The same can be achieved interactively using an accompanying shiny app. To launch the app run.

run_app()

The reset of the analysis depends on the particular kind of images. Now, colocr implements two simple co-localization statistics; Pearson's Coefficient Correlation (PCC) and the Manders Overlap Coefficient (MOC).

To apply both measures of correlation, we first get the pixel intensities and call roi_test on the merge image.

# calculate co-localization statistics
img %>%
  roi_select(threshold = 90) %>%
  roi_test(type = 'both')

The same analysis and more can be conducted using a web interface for the package available here

Acknowledgement

• The vignette images from Lai Huyen Trang
• The test examples from Colocalization Benchmark Source (CBS)
• The implementation of the co-localization statistics from Dunn et al.

More

browseVignettes('colocr')