pathwayPCA

DOI: 10.18129/B9.bioc.pathwayPCA    

Integrative Pathway Analysis with Modern PCA Methodology and Gene Selection

Bioconductor version: Release (3.14)

pathwayPCA is an integrative analysis tool that implements the principal component analysis (PCA) based pathway analysis approaches described in Chen et al. (2008), Chen et al. (2010), and Chen (2011). pathwayPCA allows users to: (1) Test pathway association with binary, continuous, or survival phenotypes. (2) Extract relevant genes in the pathways using the SuperPCA and AES-PCA approaches. (3) Compute principal components (PCs) based on the selected genes. These estimated latent variables represent pathway activities for individual subjects, which can then be used to perform integrative pathway analysis, such as multi-omics analysis. (4) Extract relevant genes that drive pathway significance as well as data corresponding to these relevant genes for additional in-depth analysis. (5) Perform analyses with enhanced computational efficiency with parallel computing and enhanced data safety with S4-class data objects. (6) Analyze studies with complex experimental designs, with multiple covariates, and with interaction effects, e.g., testing whether pathway association with clinical phenotype is different between male and female subjects. Citations: Chen et al. (2008) ; Chen et al. (2010) ; and Chen (2011) .

Author: Gabriel Odom [aut, cre], James Ban [aut], Lizhong Liu [aut], Lily Wang [aut], Steven Chen [aut]

Maintainer: Gabriel Odom <gabriel.odom at med.miami.edu>

Installation

To install this package, start R (version "4.1") and enter:

if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("pathwayPCA")

For older versions of R, please refer to the appropriate Bioconductor release.

Documentation

To view documentation for the version of this package installed in your system, start R and enter:

browseVignettes("pathwayPCA")

 

HTML	R Script	Integrative Pathway Analysis with pathwayPCA
HTML	R Script	Suppl. 1. Quickstart Guide
HTML	R Script	Suppl. 2. Importing Data
HTML	R Script	Suppl. 3. Create Data Objects
HTML	R Script	Suppl. 4. Test Pathway Significance
HTML	R Script	Suppl. 5. Visualizing the Results
PDF		Reference Manual
Text		NEWS

Details

biocViews	CellBiology, Classification, CopyNumberVariation, DNAMethylation, DimensionReduction, Epigenetics, FeatureExtraction, FunctionalGenomics, GeneExpression, GenePrediction, GeneSetEnrichment, GeneSignaling, GeneTarget, Genetics, GenomeWideAssociation, GenomicVariation, Lipidomics, Metabolomics, MultipleComparison, Pathways, PrincipalComponent, Proteomics, Regression, SNP, Software, Survival, SystemsBiology, Transcription, Transcriptomics
Version	1.10.0
In Bioconductor since	BioC 3.9 (R-3.6) (3 years)
License	GPL-3
Depends	R (>= 3.1)
Imports	lars, methods, parallel, stats, survival, utils
LinkingTo
Suggests	airway, circlize, grDevices, knitr, RCurl, reshape2, rmarkdown, SummarizedExperiment, survminer, testthat, tidyverse
SystemRequirements
Enhances
URL
BugReports	https://github.com/gabrielodom/pathwayPCA/issues
Depends On Me
Imports Me	fcoex
Suggests Me
Links To Me
Build Report

Package Archives

Follow Installation instructions to use this package in your R session.

Source Package	pathwayPCA_1.10.0.tar.gz
Windows Binary	pathwayPCA_1.10.0.zip
macOS 10.13 (High Sierra)	pathwayPCA_1.10.0.tgz
Source Repository	git clone https://git.bioconductor.org/packages/pathwayPCA
Source Repository (Developer Access)	git clone git@git.bioconductor.org:packages/pathwayPCA
Package Short Url	https://bioconductor.org/packages/pathwayPCA/
Package Downloads Report	Download Stats
Old Source Packages for BioC 3.14	Source Archive