PT  - JOURNAL ARTICLE
AU  - Gabriel J. Odom
AU  - Yuguang Ban
AU  - Lizhong Liu
AU  - Xiaodian Sun
AU  - Alexander R. Pico
AU  - Bing Zhang
AU  - Lily Wang
AU  - Xi Chen
TI  - pathwayPCA: an R package for integrative pathway analysis with modern PCA methodology and gene selection
AID  - 10.1101/615435
DP  - 2019 Jan 01
TA  - bioRxiv
PG  - 615435
4099  - http://biorxiv.org/content/early/2019/04/22/615435.short
4100  - http://biorxiv.org/content/early/2019/04/22/615435.full
AB  - With the advance in high-throughput technology for molecular assays, multi-omics datasets have become increasingly available. However, most currently available pathway analysis software provide little or no functionalities for analyzing multiple types of -omics data simultaneously. In addition, most tools do not provide sample-specific estimates of pathway activities, which are important for precision medicine. To address these challenges, we present pathwayPCA, a unique R package for integrative pathway analysis that utilizes modern statistical methodology including supervised PCA and adaptive elastic-net PCA for principal component analysis. pathwayPCA can analyze continuous, binary, and survival outcomes in studies with multiple covariate and/or interaction effects. We provide three case studies to illustrate pathway analysis with gene selection, integrative analysis of multi-omics datasets to identify driver genes, estimating and visualizing sample-specific pathway activities in ovarian cancer, and identifying sex-specific pathway effects in kidney cancer. pathwayPCA is an open source R package, freely available to the research community. We expect pathwayPCA to be a useful tool for empowering the wide scientific community on the analyses and interpretation of the wealth of multiomics data recently made available by TCGA, CPTAC and other large consortiums.