PathwayPCA: an R/Bioconductor Package for Pathway Based Integrative Analysis of Multi‐Omics Data

Abstract

The authors present pathwayPCA, an R/Bioconductor package for integrative pathway analysis that utilizes modern statistical methodology, including supervised and adaptive, elastic-net, sparse principal component analysis. pathwayPCA can be applied to continuous, binary, and survival outcomes in studies with multiple covariates and/or interaction effects. It outperforms several alternative methods at identifying disease-associated pathways in integrative analysis using both simulated and real datasets. In addition, several case studies are provided to illustrate pathwayPCA analysis with gene selection, estimating, and visualizing sample-specific pathway activities, identifying sex-specific pathway effects in kidney cancer, and building integrative models for predicting patient prognosis. pathwayPCA is an open-source R package, freely available through the Bioconductor repository. pathwayPCA is expected to be a useful tool for empowering the wider scientific community to analyze and interpret the wealth of available proteomics data, along with other types of molecular data recently made available by Clinical Proteomic Tumor Analysis Consortium and other large consortiums.

COCOA: coordinate covariation analysis of epigenetic heterogeneity

“…1b). This principle of covariation of coregulated loci or genes has been leveraged by other methods related to gene regulation [2,3,[9][10][11][12][13]. To distinguish small differences among samples in the activity level of the effector, COCOA boosts statistical power by aggregating signal in region sets [3].…”

PathwayMultiomics: An R Package for Efficient Integrative Analysis of Multi-Omics Datasets With Matched or Un-matched Samples

“…For example, for pathway analysis of DNAm data, the missMethyl method ( Phipson et al, 2016 ), which takes account of the varying number of probes mapped to each gene, could be used. For pathway analysis of gene expression data, pathwayPCA method ( Odom et al, 2020 ), which selects the coherent subset of genes before estimating and testing principal components with phenotypes, could be applied.…”

Multi-Omics Model Applied to Cancer Genetics

“…A strategy that can provide readily interpretable results consist in mapping omic data on functional characteristics, in order to make them more informative and to associate them with a wider body of biomedical knowledge [ 2 ]. Some functional enrichment approaches are listed below: Over-Representation Analysis (ORA) [ 124 ]; Gene-Set Enrichment Analysis (GSEA) [ 125 ]; Multi-Omics Gene-Set Analysis (MOGSA) [ 126 ]; Massive Integrative Gene Set Analysis (MIGSA) [ 127 ]; Exploratory Data Analysis (PCA) [ 128 ]; Divergence Analysis [ 129 ]. …”