colocr
An R package for conducting co-localization analysis. Edit
Science Score: 20.0%
This score indicates how likely this project is to be science-related based on various indicators:
-
○CITATION.cff file
-
○codemeta.json file
-
○.zenodo.json file
-
○DOI references
-
✓Academic publication links
Links to: researchgate.net, ncbi.nlm.nih.gov -
✓Committers with academic emails
1 of 4 committers (25.0%) from academic institutions -
○Institutional organization owner
-
○JOSS paper metadata
-
○Scientific vocabulary similarity
Low similarity (14.7%) to scientific vocabulary
Keywords
Keywords from Contributors
Repository
An R package for conducting co-localization analysis. Edit
Basic Info
- Host: GitHub
- Owner: ropensci
- License: gpl-3.0
- Language: R
- Default Branch: master
- Homepage: https://docs.ropensci.org/colocr
- Size: 5.68 MB
Statistics
- Stars: 25
- Watchers: 6
- Forks: 3
- Open Issues: 2
- Releases: 0
Topics
Metadata Files
README.md
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
colocr is available on CRAN
and can be installed using
```r
install from cran
install.packages('colocr') ```
The package development version is available at github.
```r
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.
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.
```r
load libraries
library(colocr)
load images
fl <- system.file('extdata', 'Image0001.jpg', package = 'colocr') img <- imageload(fl)
select ROI and show the results
par(mfrow = c(2,2), mar = rep(1, 4))
img %>% roiselect(threshold = 90) %>% roishow() ```
The same can be achieved interactively using an accompanying shiny app. To launch the app run.
r
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.
```r
calculate co-localization statistics
img %>% roiselect(threshold = 90) %>% roitest(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.
Citation
r
citation('colocr')
Owner
- Name: rOpenSci
- Login: ropensci
- Kind: organization
- Email: info@ropensci.org
- Location: Berkeley, CA
- Website: https://ropensci.org/
- Twitter: rOpenSci
- Repositories: 307
- Profile: https://github.com/ropensci
GitHub Events
Total
Last Year
Committers
Last synced: about 2 years ago
Top Committers
| Name | Commits | |
|---|---|---|
| Mahmoud Ahmed | m****d@M****l | 37 |
| MahShaaban | m****y@s****g | 18 |
| Mahmoud Ahmed | m****d@M****l | 3 |
| ropenscibot | m****t@g****m | 1 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 10 months ago
All Time
- Total issues: 5
- Total pull requests: 3
- Average time to close issues: about 1 year
- Average time to close pull requests: 2 months
- Total issue authors: 4
- Total pull request authors: 2
- Average comments per issue: 1.2
- Average comments per pull request: 2.33
- Merged pull requests: 2
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 1
- Pull requests: 0
- Average time to close issues: about 2 months
- Average time to close pull requests: N/A
- Issue authors: 1
- Pull request authors: 0
- Average comments per issue: 3.0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- MahShaaban (2)
- cwilso6 (1)
- jeroen (1)
- apelin20 (1)
Pull Request Authors
- MahShaaban (2)
- maelle (1)
Top Labels
Issue Labels
Pull Request Labels
Packages
- Total packages: 1
-
Total downloads:
- cran 149 last-month
- Total dependent packages: 0
- Total dependent repositories: 0
- Total versions: 2
- Total maintainers: 1
cran.r-project.org: colocr
Conduct Co-Localization Analysis of Fluorescence Microscopy Images
- Homepage: https://docs.ropensci.org/colocr
- Documentation: http://cran.r-project.org/web/packages/colocr/colocr.pdf
- License: GPL-3
- Status: removed
-
Latest release: 0.1.1
published almost 6 years ago
Rankings
Maintainers (1)
Dependencies
- imager * imports
- magick * imports
- magrittr * imports
- scales * imports
- shiny * imports
- covr * suggests
- devtools * suggests
- knitr * suggests
- purrr * suggests
- rmarkdown * suggests
- shinyBS * suggests
- shinytest * suggests
- testthat * suggests
- rocker/verse latest build