A Review of Integrative Imputation for Multi-Omics Datasets
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 f...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2020-10-01
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| Series: | Frontiers in Genetics |
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| Online Access: | https://www.frontiersin.org/article/10.3389/fgene.2020.570255/full |
| _version_ | 1819007026873761792 |
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| author | Meng Song Jonathan Greenbaum Joseph Luttrell Weihua Zhou Chong Wu Hui Shen Ping Gong Chaoyang Zhang Hong-Wen Deng |
| author_facet | Meng Song Jonathan Greenbaum Joseph Luttrell Weihua Zhou Chong Wu Hui Shen Ping Gong Chaoyang Zhang Hong-Wen Deng |
| author_sort | Meng Song |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-21T00:18:02Z |
| format | Article |
| id | doaj.art-92a95f9933184fd1a20be84d81360fa1 |
| institution | Directory Open Access Journal |
| issn | 1664-8021 |
| language | English |
| last_indexed | 2024-12-21T00:18:02Z |
| publishDate | 2020-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Genetics |
| spelling | doaj.art-92a95f9933184fd1a20be84d81360fa12022-12-21T19:22:11ZengFrontiers Media S.A.Frontiers in Genetics1664-80212020-10-011110.3389/fgene.2020.570255570255A Review of Integrative Imputation for Multi-Omics DatasetsMeng Song0Jonathan Greenbaum1Joseph Luttrell2Weihua Zhou3Chong Wu4Hui Shen5Ping Gong6Chaoyang Zhang7Hong-Wen Deng8School of Computing Sciences and Computer Engineering, University of Southern Mississippi, Hattiesburg, MS, United StatesTulane Center of Biomedical Informatics and Genomics, School of Medicine, Tulane University, New Orleans, LA, United StatesSchool of Computing Sciences and Computer Engineering, University of Southern Mississippi, Hattiesburg, MS, United StatesCollege of Computing, Michigan Technological University, Houghton, MI, United StatesDepartment of Statistics, Florida State University, Tallahassee, FL, United StatesTulane Center of Biomedical Informatics and Genomics, School of Medicine, Tulane University, New Orleans, LA, United StatesEnvironmental Laboratory, U.S. Army Engineer Research and Development Center, Vicksburg, MS, United StatesSchool of Computing Sciences and Computer Engineering, University of Southern Mississippi, Hattiesburg, MS, United StatesTulane Center of Biomedical Informatics and Genomics, School of Medicine, Tulane University, New Orleans, LA, United StatesMulti-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.https://www.frontiersin.org/article/10.3389/fgene.2020.570255/fullmulti-omics imputationintegrative imputationsingle-omics imputationdeep learningautoencodersmachine learning |
| spellingShingle | Meng Song Jonathan Greenbaum Joseph Luttrell Weihua Zhou Chong Wu Hui Shen Ping Gong Chaoyang Zhang Hong-Wen Deng A Review of Integrative Imputation for Multi-Omics Datasets Frontiers in Genetics multi-omics imputation integrative imputation single-omics imputation deep learning autoencoders machine learning |
| title | A Review of Integrative Imputation for Multi-Omics Datasets |
| title_full | A Review of Integrative Imputation for Multi-Omics Datasets |
| title_fullStr | A Review of Integrative Imputation for Multi-Omics Datasets |
| title_full_unstemmed | A Review of Integrative Imputation for Multi-Omics Datasets |
| title_short | A Review of Integrative Imputation for Multi-Omics Datasets |
| title_sort | review of integrative imputation for multi omics datasets |
| topic | multi-omics imputation integrative imputation single-omics imputation deep learning autoencoders machine learning |
| url | https://www.frontiersin.org/article/10.3389/fgene.2020.570255/full |
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