The aim of
BPRMeth is to extract higher order features associated with the shape of methylation profiles across a defined genomic region. Using these higher order features across promoter-proximal regions, BPRMeth provides a powerful machine learning predictor of gene expression. Check the vignette on how to use the package. Modelling details for the different models can be found online: http://rpubs.com/cakapourani.
The original implementation has now been enhanced in two important ways: we introduced a fast, variational inference approach which enables the quantification of Bayesian posterior confidence measures on the model, and we adapted the method to use several observation models, making it suitable for a diverse range of platforms including single-cell and bulk sequencing experiments and methylation arrays.
To get the latest development version from Github:
# install.packages("devtools") devtools::install_github("andreaskapou/BPRMeth", build_vignettes = TRUE)
Or install from the stable release version from Bioconductor
## try http:// if https:// URLs are not supported if (!requireNamespace("BiocManager", quietly=TRUE)) install.packages("BiocManager") BiocManager::install("BPRMeth")
You can the check the vignette on how to use the package:
If you get the following error when installing the package:
clang: error: unsupported option '-fopenmp'
try the following:
brew install llvm brew install boost brew install homebrew/science/hdf5 --enable-cxx mkdir -p ~/.R vim ~/.R/Makevars ## Paste the following commands # The following statements are required to use the clang4 binary CC=/usr/local/clang4/bin/clang CXX=/usr/local/clang4/bin/clang++ CXX11=/usr/local/clang4/bin/clang++ CXX14=/usr/local/clang4/bin/clang++ CXX17=/usr/local/clang4/bin/clang++ CXX1X=/usr/local/clang4/bin/clang++ LDFLAGS=-L/usr/local/clang4/lib # End clang4 inclusion statements
These commands will point R to the new version of clang.
The diagram below shows an overview of the pre-processing and analysis workflow in
BPRMeth, together with example output graphs.
Kapourani, C.-A. and Sanguinetti, G. (2016). Higher order methylation features for clustering and prediction in epigenomic studies. Bioinformatics 32 (17), i405-i412. (Best Paper Award in ECCB 2016).
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