GitHub - tyeulalio/regionalpcs (original) (raw)

regionalpcs

Table of Contents

1. Introduction
2. Repository Contents
3. System Requirements
4. Installation Guide
5. Demo

Introduction

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.

Repository Contents

• 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 the help() function, providing users with detailed information on package functions and usage.
• tests/: Houses R unit tests developed with the testthat 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.

System Requirements

Hardware Requirements

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.

Software Requirements

Operating System Compatibility

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 packageregionalpcshas 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

R and Package Dependencies

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.

Installation Guide

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")

Demonstration

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.

Accessing the Vignettes

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')

Online Access

Alternatively, for access to a browser-friendly version, visit theregionalpcs Bioconductor page. Here, you’ll find the vignettes available in HTML and R formats.

Tutorial Duration

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.

Session Information

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