The goal of the MetaR project is to provide a tool for educating biomedical researchers in data analysis by keeping the learning curve as smooth and simple as possible.
Omitting non-essential elements is especially important when the user is a computational beginner. For this reason, MetaR was created to keep the focus on the data analysis rather than the technical details.
MetaR takes advantage of Language Workbench Technology to facilitate data analysis with the R language. The tool is tailored for biologists with no programming experience, as well as expert bioinformaticians and statisticians. MetaR is not a replacement for R, it is a tool built on top of R.
MetaR performs statistical analyses over RNA-Seq data.
A typical MetaR analysis starts from a read count matrix, where each column is a sample and each row is a gene.
Major features of MetaR include:
- data abstraction
- data manipulation
- data analysis
- graphical visualization of the results
- integration with virtual environments (for enhanced reproducibility)
- versioning of your projects
MetaR is available on GitHub.
MetaR is currently in active development: please use the GitHub issue tracker to file enhancement requests and bug fixing.
See MetaR releases on GitHub.
MetaR is open-source and released under the Apache 2.0 license.
If you use MetaR in a paper, please cite:
Campagne, F., Digan, W., & Simi, M. (2015). MetaR: simple, high-level languages for data analysis with the R ecosystem. bioRxiv 030254; doi: http://dx.doi.org/10.1101/030254
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Simi, M. (2021) Learning Data Analysis with MetaR. In: Bucchiarone A., Cicchetti A., Ciccozzi F., Pierantonio A. (eds) Domain-Specific Languages in Practice. Springer, Cham. Hardcover ISBN 978-3-030-73757-3, eBook ISBN 978-3-030-73758-0. Chapter preview available at: https://link.springer.com/chapter/10.1007/978-3-030-73758-0_9
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Simi, M. (2020) MetaR Case Study. Available online at: https://resources.jetbrains.com/storage/products/mps/docs/MPS_MetaR_Case_Study.pdf
MetaR is offered by the Informatics Core at the Clinical and Translational Science Center (CTSC) at Weill Cornell Medicine (WCM).