Welcome to the new bioconductor.org!

pathwayPCA

Integrative Pathway Analysis with Modern PCA Methodology and Gene Selection


Bioconductor version: Release (3.18)

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>

Citation (from within R, enter citation("pathwayPCA")):

Installation

To install this package, start R (version "4.3") 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")
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 HTML R Script
Reference Manual PDF
NEWS Text

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 1.18.0
In Bioconductor since BioC 3.9 (R-3.6) (5 years)
License GPL-3
Depends R (>= 3.1)
Imports lars, methods, parallel, stats, survival, utils
System Requirements
URL
Bug Reports https://github.com/gabrielodom/pathwayPCA/issues
See More
Suggests airway, circlize, grDevices, knitr, RCurl, reshape2, rmarkdown, SummarizedExperiment, survminer, testthat, tidyverse
Linking To
Enhances
Depends On Me
Imports Me fcoex
Suggests Me
Links To Me
Build Report Build Report

Package Archives

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

Source Package pathwayPCA_1.18.0.tar.gz
Windows Binary pathwayPCA_1.18.0.zip
macOS Binary (x86_64) pathwayPCA_1.18.0.tgz
macOS Binary (arm64) pathwayPCA_1.18.0.tgz
Source Repository git clone https://git.bioconductor.org/packages/pathwayPCA
Source Repository (Developer Access) git clone git@git.bioconductor.org:packages/pathwayPCA
Bioc Package Browser https://code.bioconductor.org/browse/pathwayPCA/
Package Short Url https://bioconductor.org/packages/pathwayPCA/
Package Downloads Report Download Stats