Identification of cancer omics commonality and difference via community fusion

Abstract

The analysis of cancer omics data is a "classic" problem, however, still remains challenging. Advancing from early studies that are mostly focused on a single type of cancer, some recent studies have analyzed data on multiple "related" cancer types/subtypes, examined their commonality and difference, and led to insightful findings. In this article, we consider the analysis of multiple omics datasets, with each dataset on one type/subtype of "related" cancers. A Community Fusion (CoFu) approach is developed, which conducts marker selection and model building using a novel penalization technique, informatively accommodates the network community structure of omics measurements, and automatically identifies the commonality and difference of cancer omics markers. Simulation demonstrates its superiority over direct competitors. The analysis of TCGA lung cancer and melanoma data leads to interesting findings

33 pages, 11 figures

References in corpus (3)

• Fast unfolding of communities in large networks
• Stochastic blockmodels and community structure in networks
• Extracting the multiscale backbone of complex weighted networks

Cited by in corpus (4)

• Robust nonparametric integrative analysis to decipher heterogeneity and commonality across subgroups using sparse boosting
• An integrative sparse boosting analysis of cancer genomic commonality and difference
• Integrative Sparse Partial Least Squares
• Biomarker-guided heterogeneity analysis of genetic regulations via multivariate sparse fusion