Assisted clustering of gene expression data using regulatory data from partially overlapping sets of individuals

BMC Genomics

Research

Josée Dupuis¹  Wenqing Jiang¹  George T O’Connor²  Roby Joehanes³  Daniel Levy⁴ 

[1] Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA;Department of Medicine, Pulmonary Center, Boston University, Boston, MA, USA;National Heart, Lung, and Blood Institute’s Framingham Heart Study, Framingham, MA, USA;National Heart, Lung, and Blood Institute’s Framingham Heart Study, Framingham, MA, USA;The Population Sciences Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA;

关键词: Multi-omics data integration;    Gene expression;    Clustering;    DNA methylation;    Genotype;    Framingham Heart Study;   

DOI  :  10.1186/s12864-022-09026-1

 received in 2022-06-24, accepted in 2022-11-18,  发布年份 2022

来源: Springer

【 摘 要 】

BackgroundAs omics measurements profiled on different molecular layers are interconnected, integrative approaches that incorporate the regulatory effect from multi-level omics data are needed. When the multi-level omics data are from the same individuals, gene expression (GE) clusters can be identified using information from regulators like genetic variants and DNA methylation. When the multi-level omics data are from different individuals, the choice of integration approaches is limited.MethodsWe developed an approach to improve GE clustering from microarray data by integrating regulatory data from different but partially overlapping sets of individuals. We achieve this through (1) decomposing gene expression into the regulated component and the other component that is not regulated by measured factors, (2) optimizing the clustering goodness-of-fit objective function. We do not require the availability of different omics measurements on all individuals. A certain amount of individual overlap between GE data and the regulatory data is adequate for modeling the regulation, thus improving GE clustering.ResultsA simulation study shows that the performance of the proposed approach depends on the strength of the GE-regulator relationship, degree of missingness, data dimensionality, sample size, and the number of clusters. Across the various simulation settings, the proposed method shows competitive performance in terms of accuracy compared to the alternative K-means clustering method, especially when the clustering structure is due mostly to the regulated component, rather than the unregulated component. We further validate the approach with an application to 8,902 Framingham Heart Study participants with data on up to 17,873 genes and regulation information of DNA methylation and genotype from different but partially overlapping sets of participants. We identify clustering structures of genes associated with pulmonary function while incorporating the predicted regulation effect from the measured regulators. We further investigate the over-representation of these GE clusters in pathways of other diseases that may be related to lung function and respiratory health.ConclusionWe propose a novel approach for clustering GE with the assistance of regulatory data that allowed for different but partially overlapping sets of individuals to be included in different omics data.

【 授权许可】

CC BY   
© The Author(s) 2022

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