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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.

SUBMITTER: Shi WJ

PROVIDER: S-EPMC6931269 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

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Unsupervised discovery of phenotype-specific multi-omics networks.

Shi W Jenny WJ Zhuang Yonghua Y Russell Pamela H PH Hobbs Brian D BD Parker Margaret M MM Castaldi Peter J PJ Rudra Pratyaydipta P Vestal Brian B Hersh Craig P CP Saba Laura M LM Kechris Katerina K

Bioinformatics (Oxford, England) 20191101 21

<h4>Motivation</h4>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.<h4>Results</h4>We propose a novel techniq ...[more]

PMID: 30957844

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Project description:BackgroundCancer is a complex and heterogeneous disease with many possible genetic and environmental causes. The same treatment for patients of the same cancer type often results in different outcomes in terms of efficacy and side effects of the treatment. Thus, the molecular characterization of individual cancer patients is increasingly important to find an effective treatment. Recently a few methods have been developed to construct cancer sample-specific gene networks based on the difference in the mRNA expression levels between the cancer sample and reference samples.MethodsWe constructed a patient-specific network with multi-omics data based on the difference between a reference network and a perturbed reference network by the patient. A network specific to a group of patients was obtained using the average change in correlation coefficients and node degree of patient-specific networks of the group.ResultsIn this paper, we present a new method for constructing cancer patient-specific and group-specific gene networks with multi-omics data. The main differences of our method from previous ones are as follows: (1) networks are constructed with multi-omics (mRNA expression, copy number variation, DNA methylation and microRNA expression) data rather than with mRNA expression data alone, (2) background networks are constructed with both normal samples and cancer samples of the specified type to extract cancer-specific gene correlations, and (3) both patient individual-specific networks and patient group-specific networks can be constructed. The results of evaluating our method with several types of cancer show that it constructs more informative and accurate gene networks than previous methods.ConclusionsThe results of evaluating our method with extensive data of seven cancer types show that the difference of gene correlations between the reference samples and a patient sample is a more predictive feature than mRNA expression levels and that gene networks constructed with multi-omics data show a better performance than those with single omics data in predicting cancer for most cancer types. Our approach will be useful for finding genes and gene pairs to tailor treatments to individual characteristics.

Dataset Information

Unsupervised discovery of phenotype-specific multi-omics networks.

Publications

Unsupervised discovery of phenotype-specific multi-omics networks.

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