Model-based joint visualization of multiple compositional omics datasets
Open accessAbstract
Abstract The integration of multiple omics datasets measured on the same samples is a challenging task: data come from heterogeneous sources and vary in signal quality. In addition, some omics data are inherently compositional, e.g. sequence count data. Most integrative methods are limited in their ability to handle covariates, missing values, compositional structure and heteroscedasticity. In this article we introduce a flexible model-based approach to data integration to address these current limitations: COMBI. We combine concepts, such as compositional biplots and log-ratio link functions with latent variable models, and propose an attractive visualization through multiplots to improve interpretation. Using real data examples and simulations, we illustrate and compare our method with other data integration techniques. Our algorithm is available in the R-package combi.
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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 Hawinkel et al., 2020
COMBI's quasi-likelihood estimation approach is robust to missing data values.
“The quasi-likelihood estimation approach is also robust to missing data values.”
From Missing data in multi-omics integration: Recent advances through artificial intelligence (2023) · cites this paper as Hawinkel et al., 2020
Provided that data are missing completely at random (MCAR) or missing at random (MAR), COMBI's quasi-likelihood estimation procedure yields unbiased parameter estimates based on all complete or partial observations.
“Provided that the data are missing completely at random (MCAR) or missing at random (MAR), the quasi-likelihood estimation procedure yields unbiased parameter estimates based on all (complete or partial) observations.”
From Missing data in multi-omics integration: Recent advances through artificial intelligence (2023) · cites this paper as Hawinkel et al., 2020
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Research works printed but not linked: 1
- [37]Argelaguet R., Velten B., Arnol D., Buettner F., Huber W., Stegle O. MOFA: multi-omics factor analysis (MOFA). 2019; R package version 1.0.0.. PMC6010767 29925568
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Missing data in multi-omics integration: Recent advances through artificial intelligence
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“, 2018), Compositional Omics ModelBased Integration (COMBI; Hawinkel et al., 2020) introduces model extensions better suited for modeling compositional and sequence count data.”
Background
- Authors
- Hawinkel, S., Bijnens, L., Cao, K.-A. L., & Thas, O.
- Affiliations
- Ghent University (BE)
- Janssen (Belgium) (BE)
- The University of Melbourne (AU)
- University of Wollongong (AU)
- Corresponding author
- Hawinkel, Stijn
- Venue
- NAR Genomics and Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2020-07-21
- Volume
- 2
- Issue
- 3
- Pages
- lqaa050-lqaa050
- ISSN
- 2631-9268 (ISSN-L)
- PMC
- PMC7671331
- OpenAlex
- W3045412769
- Cited by
- 9
- References
- 41
- Citations by year
- 2025: 2 · 2024: 3 · 2023: 1 · 2022: 1 · 2021: 1 · 2020: 1
- FWCI
- 0.32
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- NAR Genomics and Bioinformatics (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Ghent University Academic Bibliography (Ghent University) (submittedVersion, other-oa)
- Document Server@UHasselt (UHasselt) (submittedVersion, other-oa)
- Minerva Access (University of Melbourne) (submittedVersion, cc-by)
- Funders
- Johnson and Johnson
- 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 › Molecular Biology › Single-cell and spatial transcriptomics
- Keywords
- multi-omics integration, compositional data, latent variable models, heteroscedasticity, missing data, covariate adjustment
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