New Method for Joint Network Analysis Reveals Common and Different Coexpression Patterns among Genes and Proteins in Breast Cancer

We focus on characterizing
common and different coexpression patterns
among RNAs and proteins in breast cancer tumors. To address this problem,
we introduce Joint Random Forest (JRF), a novel nonparametric algorithm
to simultaneously estimate multiple coexpression networks by effectively
borrowing information across protein and gene expression data. The
performance of JRF was evaluated through extensive simulation studies
using different network topologies and data distribution functions.
Advantages of JRF over other algorithms that estimate class-specific
networks separately were observed across all simulation settings.
JRF also outperformed a competing method based on Gaussian graphic
models. We then applied JRF to simultaneously construct gene and protein
coexpression networks based on protein and RNAseq data from CPTAC-TCGA
breast cancer study. We identified interesting common and differential
coexpression patterns among genes and proteins. This information can
help to cast light on the potential disease mechanisms of breast cancer.