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A Review of Integrative Imputation for Multi-Omics Datasets

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Song, Meng, Greenbaum, Jonathan, Luttrell, Joseph, Zhou, Weihua, Wu, Chong, Shen, Hui, Gong, Ping, Zhang, Chaoyang, Deng, Hong-Wen·2020·Frontiers in Genetics

DOI 10.3389/fgene.2020.570255PMC PMC7594632

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Abstract

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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FIGURE 1 The genetic information flow from DNA to protein via RNA and the interaction between transcriptomics and genomics, epigenomics or proteomics in multi-omics data imputation. The top diagram shows the central dogma of molecular biology in which DNA is transcribed to RNA and then translated to proteins. The bottom diagram shows the integrative relationship between the genomics, epigenomics, transcriptomics and proteomics datasets on which multi-omics data imputation methods are based. These methods are built by combining different omics datasets to perform integrative imputation of missing values. This is shown here by using arrows numbered by the type of data combination they represent. For example, arrow 1 shows that multi-omics imputation can be facilitated by leveraging the correlation between data from both genomics (such as SNP) and transcriptomics (such as gene expression).CC BY · PubMed Central

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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: Song, Meng
Greenbaum, Jonathan
Luttrell, Joseph
Zhou, Weihua
Wu, Chong
Shen, Hui
Gong, Ping
Zhang, Chaoyang
Deng, Hong-Wen
Venue: Frontiers in Genetics
Publisher: Frontiers Media SA
Published: 2020-10-15
Volume: 11
Pages: 570255-570255
ISSN: 1664-8021 (ISSN-L)
DOI: 10.3389/fgene.2020.570255
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: cc-by
creativecommons.org/licenses/by/4.0/ (vor)
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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A Review of Integrative Imputation for Multi-Omics Datasets

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