Tiffany Eulalio
The regionalpcs
package aims to address the challenge of summarizing
and interpreting DNA methylation data at a regional level. Traditional
methods of analysis may not capture the biological complexity of
methylation patterns, potentially leading to less accurate or less
meaningful interpretations. This package introduces the concept of
regional principal components (rPCs) as a tool for capturing more
biologically relevant signals in DNA methylation data. By using rPCs,
researchers can gain new insights into complex interactions and effects
in methylation data that might otherwise be missed.
-
R/
: Contains the source code for the project, written in R. This directory includes all the scripts and functions that constitute the core functionality of the package. -
inst/
: Stores files that are retained post-installation of the R package. This includes additional data, documentation, or scripts that users might find useful when working with the package. -
man/
: Contains the manual pages for the package. These documentation files are accessible within an R session using thehelp()
function, providing users with detailed information on package functions and usage. -
tests/
: Houses R unit tests developed with thetestthat
package. These tests are designed to automatically verify that the package functions correctly under various conditions, ensuring reliability and stability. -
vignettes/
: Provides R Markdown vignettes that offer comprehensive tutorials and examples on how to use the package. These vignettes are converted into HTML help pages accessible from within an R session, serving as a valuable resource for users to learn about the package features and functionalities.
The regionalpcs
package is designed to function efficiently on a
standard computer setup. The specific RAM requirement depends on the
scale of the analysis defined by the user. Below are our recommendations
for minimal and optimal performance configurations:
- Minimal Configuration: A computer with at least 2 GB of RAM is required for basic operation.
- Recommended Configuration: For optimal performance, especially for
more demanding analyses, we recommend the following specifications:
- RAM: 16 GB or more
- CPU: 4 or more cores, with a clock speed of 3.3 GHz per core or faster
Runtime Benchmarks: The reported runtimes are based on tests conducted on a system equipped with 64 GB RAM, an 8-core CPU @ 3.60 GHz, and an internet connection speed of 229 Mbps.
While the development version of the regionalpcs
package is primarily
tested on Windows platforms, we aim for broad compatibility across major
operating systems.
Our Bioconductor package
regionalpcs
has been tested with Windows, Mac, and Linux operating systems. Here are
the details regarding the tested systems:
- Linux: Ubuntu 22.04.03 LTS / x86_64
- Mac OSX: macOS 12.7.1 Monterey / x86_64
- Windows: 10 Pro / x64
To install and run the regionalpcs
package, the following software
requirements must be met:
- R Version: The package requires R version 4.3.0 or higher. Ensure
that your R installation is up to date before proceeding with the
installation of
regionalpcs
. - Dependencies: Additional R packages from CRAN and possibly Bioconductor are required. Users will be prompted to install any missing dependencies during the package installation process. Required packages are:
dplyr
PCAtools
tibble
GenomicRanges
Please refer to the package documentation for a detailed list of dependencies and instructions for setting up the required software environment.
You can install the regionalpcs package from Bioconductor using the following command:
if (!requireNamespace("BiocManager", quietly=TRUE))
install.packages("BiocManager")
BiocManager::install("regionalpcs")
which will install in about 30 seconds on a machine with the recommended specs.
You can install the development version of regionalpcs from GitHub with:
# install devtool package if needed
if (!requireNamespace("devtools", quietly=TRUE))
install.packages("devtools")
# download the regionalpcs package
devtools::install_github("tyeulalio/regionalpcs")
Explore the functionalities of the regionalpcs
package with our
interactive tutorials provided as vignettes. These vignettes offer
step-by-step guidance on using the package’s main features and are
designed to help you get started quickly.
To start the tutorials, ensure that the regionalpcs
package is
installed and loaded into your R session. You can then access the
vignettes directly in R with the following commands:
# Load the regionalpcs package
library(regionalpcs)
# Open the main vignette
vignette('regionalpcs-introduction')
Alternatively, for access to a browser-friendly version, visit the
regionalpcs
Bioconductor
page.
Here, you’ll find the vignettes available in HTML and R formats.
The primary vignette is concise and informative, designed to provide a comprehensive overview within approximately 20 seconds. This makes it an efficient way to familiarize yourself with the package’s capabilities and start applying them to your data analysis projects.
sessionInfo()
#> R version 4.3.1 (2023-06-16 ucrt)
#> Platform: x86_64-w64-mingw32/x64 (64-bit)
#> Running under: Windows 10 x64 (build 19045)
#>
#> Matrix products: default
#>
#>
#> locale:
#> [1] LC_COLLATE=English_United States.utf8
#> [2] LC_CTYPE=English_United States.utf8
#> [3] LC_MONETARY=English_United States.utf8
#> [4] LC_NUMERIC=C
#> [5] LC_TIME=English_United States.utf8
#>
#> time zone: America/Los_Angeles
#> tzcode source: internal
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> loaded via a namespace (and not attached):
#> [1] compiler_4.3.1 fastmap_1.1.1 cli_3.6.1 tools_4.3.1
#> [5] htmltools_0.5.6 rstudioapi_0.16.0 yaml_2.3.7 rmarkdown_2.26
#> [9] knitr_1.46 xfun_0.43 digest_0.6.33 rlang_1.1.1
#> [13] evaluate_0.23