A Review of Integrative Imputation for Multi-Omics Datasets.
autoencoders
deep learning
integrative imputation
machine learning
multi-omics imputation
multi-view matrix factorization
single-omics imputation
transfer learning
Journal
Frontiers in genetics
ISSN: 1664-8021
Titre abrégé: Front Genet
Pays: Switzerland
ID NLM: 101560621
Informations de publication
Date de publication:
2020
2020
Historique:
received:
07
06
2020
accepted:
16
09
2020
entrez:
16
11
2020
pubmed:
17
11
2020
medline:
17
11
2020
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 33193667
doi: 10.3389/fgene.2020.570255
pmc: PMC7594632
doi:
Types de publication
Journal Article
Review
Langues
eng
Pagination
570255Subventions
Organisme : NCRR NIH HHS
ID : M01 RR000585
Pays : United States
Organisme : NIA NIH HHS
ID : R01 AG061917
Pays : United States
Organisme : NIAMS NIH HHS
ID : R01 AR069055
Pays : United States
Organisme : NIAMS NIH HHS
ID : R01 AR027065
Pays : United States
Organisme : NIA NIH HHS
ID : U19 AG055373
Pays : United States
Organisme : NIGMS NIH HHS
ID : P20 GM109036
Pays : United States
Informations de copyright
Copyright © 2020 Song, Greenbaum, Luttrell, Zhou, Wu, Shen, Gong, Zhang and Deng.
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