A Review of Integrative Imputation for Multi-Omics Datasets
Open accessAbstract
Multi-omics studies, which explore the interactions between multiple types of biological factors, have significant advantages over single-omics analysis for their ability to provide a more holistic view of biological processes, uncover the causal and functional mechanisms for complex diseases, and facilitate new discoveries in precision medicine. However, omics datasets often contain missing values, and in multi-omics study designs it is common for individuals to be represented for some omics layers but not all. Since most statistical analyses cannot be applied directly to the incomplete datasets, imputation is typically performed to infer the missing values. Integrative imputation techniques which make use of the correlations and shared information among multi-omics datasets are expected to outperform approaches that rely on single-omics information alone, resulting in more accurate results for the subsequent downstream analyses. In this review, we provide an overview of the currently available imputation methods for handling missing values in bioinformatics data with an emphasis on multi-omics imputation. In addition, we also provide a perspective on how deep learning methods might be developed for the integrative imputation of multi-omics datasets.
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Omics data often lack certain values, and in studies on omics data, individuals are generally represented in some, but not all, omics layers.
“However, omics data often lack certain values, and in studies on omics data, individuals are generally represented in some, but not all, omics layers [105].”
From Revisiting Epigenetics Fundamentals and Its Biomedical Implications (2024) · cites this paper as [105]
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Deep neural network aided multi-omics drug response prediction for breast cancer
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Imputef: imputation of polyploid genotype classes and allele frequencies
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Self-citation
- Authors
- Song, M., Greenbaum, J., Luttrell, J., Zhou, W., Wu, C., Shen, H., Gong, P., Zhang, C., & Deng, H.-W.
- Affiliations
- University of Southern Mississippi (US)
- Tulane University (US)
- Michigan Technological University (US)
- Florida State University (US)
- United States Army (US)
- Corresponding author
- Zhang, Chaoyang
- Deng, Hong-Wen
- ORCID
- Venue
- Frontiers in Genetics
- Publisher
- Frontiers Media SA
- Published
- 2020-10-15
- Volume
- 11
- Pages
- 570255-570255
- ISSN
- 1664-8021 (ISSN-L)
- PMC
- PMC7594632
- OpenAlex
- W3100788068
- Cited by
- 127
- References
- 103
- Citations by year
- 2026: 11 · 2025: 30 · 2024: 29 · 2023: 25 · 2022: 21 · 2021: 11
- FWCI
- 1.36
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Genetics (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Digital Commons - Michigan Tech (Michigan Technological University) (submittedVersion, cc-by)
- Aquila Digital Community (University of Southern Mississippi) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Institutes of Health (P20GM109036, R01AR069055, U19AG055373, R01AG061917, AR-27065, M01 RR00585)
- Engineer Research and Development Center (W912HZ20P0023)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Keywords
- multi-omics data, missing data imputation, deep learning imputation, data integration, precision medicine, complex diseases
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