Unsupervised discovery of phenotype-specific multi-omics networks

Abstract

Motivation Complex diseases often involve a wide spectrum of phenotypic traits. Better understanding of the biological mechanisms relevant to each trait promotes understanding of the etiology of the disease and the potential for targeted and effective treatment plans. There have been many efforts towards omics data integration and network reconstruction, but limited work has examined the incorporation of relevant (quantitative) phenotypic traits. Results We propose a novel technique, sparse multiple canonical correlation network analysis (SmCCNet), for integrating multiple omics data types along with a quantitative phenotype of interest, and for constructing multi-omics networks that are specific to the phenotype. As a case study, we focus on miRNA–mRNA networks. Through simulations, we demonstrate that SmCCNet has better overall prediction performance compared to popular gene expression network construction and integration approaches under realistic settings. Applying SmCCNet to studies on chronic obstructive pulmonary disease (COPD) and breast cancer, we found enrichment of known relevant pathways (e.g. the Cadherin pathway for COPD and the interferon-gamma signaling pathway for breast cancer) as well as less known omics features that may be important to the diseases. Although those applications focus on miRNA–mRNA co-expression networks, SmCCNet is applicable to a variety of omics and other data types. It can also be easily generalized to incorporate multiple quantitative phenotype simultaneously. The versatility of SmCCNet suggests great potential of the approach in many areas. Availability and implementation The SmCCNet algorithm is written in R, and is freely available on the web at https://cran.r-project.org/web/packages/SmCCNet/index.html. Supplementary information Supplementary data are available at Bioinformatics online.

Identifying Protein–metabolite Networks Associated with COPD Phenotypes

“…Protein-metabolite networks correlated to FEV 1 % and percent emphysema were constructed using SmCCNet ( Figure S15), a technique by Shi et al [15] that uses multiple canonical correlation network analysis to integrate multi-omics data types with a phenotype of interest. The original application of SmCCNet focused on miRNA-mRNA networks.…”

“…Lastly, after protein-metabolite networks were generated from SmCCNet, absolute edge thresholds were applied to the networks to filter out weak edges (edges with low values) [15]. Edge thresholds were systematically changed from 0 to 0.7, in increments of 0.05 to reveal trimmed, interpretable networks with strong edges that still had strong correlations to the phenotype of interest and a balanced protein to metabolite ratio.…”

Comparing Statistical Tests for Differential Network Analysis of Gene Modules

“…(5) Lastly, more research is needed for differential network analysis when integrating multiple different types of molecular features (e.g., transcriptome, metabolome, microbiome, proteome). Some existing methods include: ( Class et al, 2018 ; Erola et al, 2019 ; Shi et al, 2019 ).…”

TSCCA: A tensor sparse CCA method for detecting microRNA-gene patterns from multiple cancers

“…These invaluable database repositories provide new paradigms to explore context-specific miRNA-gene regulatory relationship. Several computational methods have been proposed on the basis of modular structure identification [15][16][17][18][19][20][21]. Zhang et al developed a joint non-negative matrix factorization method to discover miRNAgene co-modules in ovarian cancer [15].…”