IMIX: a multivariate mixture model approach to association analysis through multi-omics data integration

Abstract Motivation Integrative genomic analysis is a powerful tool used to study the biological mechanisms underlying a complex disease or trait across multiplatform high-dimensional data, such as DNA methylation, copy number variation and gene expression. It is common to perform large-scale genome...

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Bibliographic Details
Published in:Bioinformatics (Oxford, England) England), 2020-12, Vol.36 (22-23), p.5439-5447
Main Authors: Wang, Ziqiao, Wei, Peng
Format: Article
Language:English
Subjects:
Association analysis
Bioinformatics
Cancer
Copy number
Data analysis
Data integration
Dimensional analysis
DNA Copy Number Variations
DNA Methylation
Gene expression
Genome
Genome-Wide Association Study
Genomes
Genomic analysis
Genomics
Multivariate analysis
Original Papers
Pancreatic cancer
Statistical models
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Summary:Abstract Motivation Integrative genomic analysis is a powerful tool used to study the biological mechanisms underlying a complex disease or trait across multiplatform high-dimensional data, such as DNA methylation, copy number variation and gene expression. It is common to perform large-scale genome-wide association analysis of an outcome for each data type separately and combine the results ad hoc, leading to loss of statistical power and uncontrolled overall false discovery rate (FDR). Results We propose a multivariate mixture model (IMIX) framework that integrates multiple types of genomic data and allows modeling of inter-data-type correlations. We investigated the across-data-type FDR control in IMIX and demonstrated lower misclassification rates at controlled overall FDR than established individual data type analysis strategies, such as the Benjamini–Hochberg FDR control, the q-value and the local FDR control by extensive simulations. IMIX features statistically principled model selection, FDR control and computational efficiency. Applications to The Cancer Genome Atlas data provided novel multi-omics insights into the genes and mechanisms associated with the luminal and basal subtypes of bladder cancer and the prognosis of pancreatic cancer. Availabilityand implementation We have implemented our method in R package ‘IMIX’ available at https://github.com/ziqiaow/IMIX, as well as CRAN soon. Supplementary information Supplementary data are available at Bioinformatics online.
ISSN:1367-4803
1367-4811
DOI:10.1093/bioinformatics/btaa1001