pathwayPCA
Integrative Pathway Analysis with Modern PCA Methodology and Gene Selection
Bioconductor version: Release (3.17)
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)
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("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 | ||
| 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 | 1.16.1 |
| In Bioconductor since | BioC 3.9 (R-3.6) (4.5 years) |
| License | GPL-3 |
| Depends | R (>= 3.1) |
| Imports | lars, methods, parallel, stats, survival, utils |
| Linking To | |
| Suggests | airway, circlize, grDevices, knitr, RCurl, reshape2, rmarkdown, SummarizedExperiment, survminer, testthat, tidyverse |
| System Requirements | |
| Enhances | |
| URL | |
| Bug Reports | https://github.com/gabrielodom/pathwayPCA/issues |
See More
| 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_1.16.1.tar.gz |
| Windows Binary | pathwayPCA_1.16.1.zip |
| macOS Binary (x86_64) | pathwayPCA_1.16.1.tgz |
| macOS Binary (arm64) | pathwayPCA_1.16.1.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 |
| Old Source Packages for BioC 3.17 | Source Archive |