Microbiome Multi-Omics Network Analysis: Statistical Considerations, Limitations, and Opportunities
The advent of large-scale microbiome studies affords newfound analytical opportunities to understand how these communities of microbes operate and relate to their environment. However, the analytical methodology needed to model microbiome data and integrate them with other data constructs remains na...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2019-11-01
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| Series: | Frontiers in Genetics |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/article/10.3389/fgene.2019.00995/full |
| _version_ | 1811325314322661376 |
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| author | Duo Jiang Courtney R. Armour Chenxiao Hu Meng Mei Chuan Tian Thomas J. Sharpton Thomas J. Sharpton Yuan Jiang |
| author_facet | Duo Jiang Courtney R. Armour Chenxiao Hu Meng Mei Chuan Tian Thomas J. Sharpton Thomas J. Sharpton Yuan Jiang |
| author_sort | Duo Jiang |
| collection | DOAJ |
| description | The advent of large-scale microbiome studies affords newfound analytical opportunities to understand how these communities of microbes operate and relate to their environment. However, the analytical methodology needed to model microbiome data and integrate them with other data constructs remains nascent. This emergent analytical toolset frequently ports over techniques developed in other multi-omics investigations, especially the growing array of statistical and computational techniques for integrating and representing data through networks. While network analysis has emerged as a powerful approach to modeling microbiome data, oftentimes by integrating these data with other types of omics data to discern their functional linkages, it is not always evident if the statistical details of the approach being applied are consistent with the assumptions of microbiome data or how they impact data interpretation. In this review, we overview some of the most important network methods for integrative analysis, with an emphasis on methods that have been applied or have great potential to be applied to the analysis of multi-omics integration of microbiome data. We compare advantages and disadvantages of various statistical tools, assess their applicability to microbiome data, and discuss their biological interpretability. We also highlight on-going statistical challenges and opportunities for integrative network analysis of microbiome data. |
| first_indexed | 2024-04-13T14:30:52Z |
| format | Article |
| id | doaj.art-abfff4644ab14b76a3b8dbec3aeec5e8 |
| institution | Directory Open Access Journal |
| issn | 1664-8021 |
| language | English |
| last_indexed | 2024-04-13T14:30:52Z |
| publishDate | 2019-11-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Genetics |
| spelling | doaj.art-abfff4644ab14b76a3b8dbec3aeec5e82022-12-22T02:43:12ZengFrontiers Media S.A.Frontiers in Genetics1664-80212019-11-011010.3389/fgene.2019.00995454213Microbiome Multi-Omics Network Analysis: Statistical Considerations, Limitations, and OpportunitiesDuo Jiang0Courtney R. Armour1Chenxiao Hu2Meng Mei3Chuan Tian4Thomas J. Sharpton5Thomas J. Sharpton6Yuan Jiang7Department of Statistics, Oregon State University, Corvallis, OR, United StatesDepartment of Microbiology, Oregon State University, Corvallis, OR, United StatesDepartment of Statistics, Oregon State University, Corvallis, OR, United StatesDepartment of Statistics, Oregon State University, Corvallis, OR, United StatesDepartment of Statistics, Oregon State University, Corvallis, OR, United StatesDepartment of Statistics, Oregon State University, Corvallis, OR, United StatesDepartment of Microbiology, Oregon State University, Corvallis, OR, United StatesDepartment of Statistics, Oregon State University, Corvallis, OR, United StatesThe advent of large-scale microbiome studies affords newfound analytical opportunities to understand how these communities of microbes operate and relate to their environment. However, the analytical methodology needed to model microbiome data and integrate them with other data constructs remains nascent. This emergent analytical toolset frequently ports over techniques developed in other multi-omics investigations, especially the growing array of statistical and computational techniques for integrating and representing data through networks. While network analysis has emerged as a powerful approach to modeling microbiome data, oftentimes by integrating these data with other types of omics data to discern their functional linkages, it is not always evident if the statistical details of the approach being applied are consistent with the assumptions of microbiome data or how they impact data interpretation. In this review, we overview some of the most important network methods for integrative analysis, with an emphasis on methods that have been applied or have great potential to be applied to the analysis of multi-omics integration of microbiome data. We compare advantages and disadvantages of various statistical tools, assess their applicability to microbiome data, and discuss their biological interpretability. We also highlight on-going statistical challenges and opportunities for integrative network analysis of microbiome data.https://www.frontiersin.org/article/10.3389/fgene.2019.00995/fullcompositionalityheterogeneitymicrobiome networksmulti-omics data integrationnetwork analysisnormalization |
| spellingShingle | Duo Jiang Courtney R. Armour Chenxiao Hu Meng Mei Chuan Tian Thomas J. Sharpton Thomas J. Sharpton Yuan Jiang Microbiome Multi-Omics Network Analysis: Statistical Considerations, Limitations, and Opportunities Frontiers in Genetics compositionality heterogeneity microbiome networks multi-omics data integration network analysis normalization |
| title | Microbiome Multi-Omics Network Analysis: Statistical Considerations, Limitations, and Opportunities |
| title_full | Microbiome Multi-Omics Network Analysis: Statistical Considerations, Limitations, and Opportunities |
| title_fullStr | Microbiome Multi-Omics Network Analysis: Statistical Considerations, Limitations, and Opportunities |
| title_full_unstemmed | Microbiome Multi-Omics Network Analysis: Statistical Considerations, Limitations, and Opportunities |
| title_short | Microbiome Multi-Omics Network Analysis: Statistical Considerations, Limitations, and Opportunities |
| title_sort | microbiome multi omics network analysis statistical considerations limitations and opportunities |
| topic | compositionality heterogeneity microbiome networks multi-omics data integration network analysis normalization |
| url | https://www.frontiersin.org/article/10.3389/fgene.2019.00995/full |
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