BiasCorrector

BiasCorrector is published in ‘BiasCorrector: fast and accurate correction of all types of experimental biases in quantitative DNA methylation data derived by different technologies’ (2021) in the International Journal of Cancer (DOI: https://onlinelibrary.wiley.com/doi/10.1002/ijc.33681).

BiasCorrector is the user friendly implementation of the algorithms described by Moskalev et. al in their research article ‘Correction of PCR-bias in quantitative DNA methylation studies by means of cubic polynomial regression’, published 2011 in Nucleic acids research, Oxford University Press (DOI: https://doi.org/10.1093/nar/gkr213).

Installation

Using R

• Make sure, you have R installed on your system:

  • https://cran.r-project.org/

• Then open your development environment and install this R package:

CRAN version

You can install BiasCorrector simply with via R’s install.packages interface:

install.packages("BiasCorrector")

Development version

If you want to use the latest development version, you can install the github version of BiasCorrector with:

install.packages("remotes")
remotes::install_github("kapsner/BiasCorrector")

• To start BiasCorrector, just run the following command in R. A browser tab should open displaying BiasCorrector. Alternatively you can type the URL “localhost:3838/” in your browser.

library(BiasCorrector)
launch_app()

Using Docker

To simplify installation an deployment of BiasCorrector you can clone this repository and build your own docker image. Make sure, you have Docker and docker-compose installed on your system.

Build Docker Image Manually

# clone the repository
git clone https://github.com/kapsner/BiasCorrector

# go to the docker subfolder
cd BiasCorrector/docker/

# run the build script
./build_image.sh

# when the building is finished, just start the container by running
docker-compose -f docker-compose.local.yml up -d

Using a Remote Docker Image

# clone the repository
git clone https://github.com/kapsner/BiasCorrector

# go to the docker subfolder
cd BiasCorrector/docker/

# start the Docker container
docker-compose -f docker-compose.remote.yml up -d

Type the URL “localhost:3838/” in your browser and start working with BiasCorrector.

rBiasCorrection

BiasCorrector depends on the rBiasCorrection R-package, which is the implementation of the core functionality to correct measurement biases in DNA methylation analyses. BiasCorrector brings this functionality to a user-friendly shiny web application.
rBiasCorrection is available at https://github.com/kapsner/rBiasCorrection.

Video Tutorial

A video tutorial describing the workflow of how to use BiasCorrector in order to correct measurement bias in DNA methylation data is available on youtube.

Demo Version

A demo version of BiasCorrector is available here.

Frequently Asked Questions

More detailed information on how to use the backend-package rBiasCorrection can be found in its vignette. The FAQs can be found here.

Citation

L.A. Kapsner, M.G. Zavgorodnij, S.P. Majorova, A. Hotz‐Wagenblatt, O.V. Kolychev, I.N. Lebedev, J.D. Hoheisel, A. Hartmann, A. Bauer, S. Mate, H. Prokosch, F. Haller, and E.A. Moskalev, BiasCorrector: fast and accurate correction of all types of experimental biases in quantitative DNA methylation data derived by different technologies, Int. J. Cancer. (2021) ijc.33681. doi:10.1002/ijc.33681.

@article{kapsner2021,
  title = {{{BiasCorrector}}: Fast and Accurate Correction of All Types of Experimental Biases in Quantitative {{DNA}} Methylation Data Derived by Different Technologies},
  author = {Kapsner, Lorenz A. and Zavgorodnij, Mikhail G. and Majorova, Svetlana P. and Hotz-Wagenblatt, Agnes and Kolychev, Oleg V. and Lebedev, Igor N. and Hoheisel, J{\"o}rg D. and Hartmann, Arndt and Bauer, Andrea and Mate, Sebastian and Prokosch, Hans-Ulrich and Haller, Florian and Moskalev, Evgeny A.},
  year = {2021},
  month = may,
  pages = {ijc.33681},
  issn = {0020-7136, 1097-0215},
  doi = {10.1002/ijc.33681},
  journal = {International Journal of Cancer},
  language = {en}
}

More Infos

• Original work by Moskalev et al.: https://doi.org/10.1093/nar/gkr213
• about Shiny: https://www.rstudio.com/products/shiny/
• RStudio and Shiny are trademarks of RStudio, Inc.
• about Docker: https://www.docker.com/