Bayesian integrative model for multi-omics data with missingness
Open accessAbstract
Motivation: Integrative analysis of multi-omics data from different high-throughput experimental platforms provides valuable insight into regulatory mechanisms associated with complex diseases, and gains statistical power to detect markers that are otherwise overlooked by single-platform omics analysis. In practice, a significant portion of samples may not be measured completely due to insufficient tissues or restricted budget (e.g. gene expression profile are measured but not methylation). Current multi-omics integrative methods require complete data. A common practice is to ignore samples with any missing platform and perform complete case analysis, which leads to substantial loss of statistical power. Methods: In this article, inspired by the popular Integrative Bayesian Analysis of Genomics data (iBAG), we propose a full Bayesian model that allows incorporation of samples with missing omics data. Results: Simulation results show improvement of the new full Bayesian approach in terms of outcome prediction accuracy and feature selection performance when sample size is limited and proportion of missingness is large. When sample size is large or the proportion of missingness is low, incorporating samples with missingness may introduce extra inference uncertainty and generate worse prediction and feature selection performance. To determine whether and how to incorporate samples with missingness, we propose a self-learning cross-validation (CV) decision scheme. Simulations and a real application on child asthma dataset demonstrate superior performance of the CV decision scheme when various types of missing mechanisms are evaluated. Availability and implementation: Freely available on the GitHub at https://github.com/CHPGenetics/FBM. Supplementary information: Supplementary data are available at Bioinformatics online.
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FBM's decreases in predictive performance with missingness are mitigated as sample size increases.
“However, the observed decreases in predictive performance were observed to be mitigated as the sample size was increased.”
From Missing data in multi-omics integration: Recent advances through artificial intelligence (2023) · cites this paper as Fang et al., 2018
Models incorporating gene-gene and methylation-methylation correlations were considered for FBM but had higher computational cost without substantive performance gains relative to independence models.
“However the authors note that models incorporating gene-gene and methylation-methylation correlations were additionally considered, but these models were associated with a higher computational cost without indicating substantive gains in performance relative to models based on the independence assumption.”
From Missing data in multi-omics integration: Recent advances through artificial intelligence (2023) · cites this paper as Fang et al., 2018
The increase in FBM prediction root mean squared error with missingness is most prominent when both gene expression and methylation data have high missingness (30%).
“This effect was observed to be most prominent when both gene expression and methylation data were characterized by high percentages of missingness (i.e., 30%).”
From Missing data in multi-omics integration: Recent advances through artificial intelligence (2023) · cites this paper as Fang et al., 2018
FBM simulation studies showed that prediction root mean squared error increases as missing data percentage increases.
“Simulation studies performed in the original publication indicate increases in the root mean squared error of prediction with increases in missing data percentage.”
From Missing data in multi-omics integration: Recent advances through artificial intelligence (2023) · cites this paper as Fang et al., 2018
Complete case analysis, while convenient, results in a decrease in sample size, throws away useful data, is only valid under the MCAR assumption and can otherwise lead to biased estimates.
“This approach, while convenient, results in a decrease in sample size, throws away useful data, is only valid under the MCAR assumption and can otherwise lead to biased estimates (Fang et al., 2018; Hawinkel et al., 2020).”
From Missing data in multi-omics integration: Recent advances through artificial intelligence (2023) · cites this paper as Fang et al., 2018
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Missing data in multi-omics integration: Recent advances through artificial intelligence
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“When an assumption of MCAR or MAR is used, the missingness is said to be ignorable and methods designed to handle missing values are applied to both cases (Gelman and Hill, 2006; Fang et al., 2018; Wei et al., 2018).”
BackgroundMethod
- Authors
- Fang, Z., Ma, T., Tang, G., Zhu, L., Yan, Q., Wang, T., Celedón, J. C., Chen, W., & Tseng, G. C.
- Affiliations
- University of Pittsburgh (US)
- University of Maryland, College Park (US)
- Children's Hospital of Pittsburgh (US)
- Division of Pediatric Pulmonology, Allergy and Immunology, Children's Hospital of Pittsburgh of UPMC, Pittsburgh, USA
- Corresponding author
- Chen, Wei
- Tseng, George C
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2018-09-01
- Volume
- 34
- Issue
- 22
- Pages
- 3801-3808
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- PMC
- PMC6223369
- OpenAlex
- W2889472696
- Cited by
- 34
- References
- 21
- Citations by year
- 2026: 2 · 2025: 5 · 2024: 9 · 2023: 4 · 2022: 5 · 2021: 5 · 2020: 3 · 2019: 1
- FWCI
- 0.80
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model (vor)
- Locations
- Bioinformatics (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health (RO1 CA190766, RO1 MD011764, RO1 HL117191)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Keywords
- multi-omics integration, missing data, complete case analysis, missing data mechanisms, feature selection, outcome prediction, childhood asthma
- MeSH terms
- Bayes Theorem, Humans, Research Design, Sample Size, Genomics, Transcriptome
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