An R package for conducting co-localization analysis.
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.
colocrcolocr is available on CRAN and can be installed
using
# install from cran
install.packages('colocr')The package development version is available at github.
# install from github
devtools::install_github('ropensci/colocr')This package depends on imager which has some external
dependencies. The instructions for installing imager can be
found here.
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
citation('colocr')