Difficulty in inferring microbial community structure based on co-occurrence network approaches
Abstract Background Co-occurrence networks—ecological associations between sampled populations of microbial communities inferred from taxonomic composition data obtained from high-throughput sequencing techniques—are widely used in microbial ecology. Several co-occurrence network methods have been p...
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| Format: | Article |
| Language: | English |
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BMC
2019-06-01
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| Series: | BMC Bioinformatics |
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| Online Access: | http://link.springer.com/article/10.1186/s12859-019-2915-1 |
| _version_ | 1818850518911418368 |
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| author | Hokuto Hirano Kazuhiro Takemoto |
| author_facet | Hokuto Hirano Kazuhiro Takemoto |
| author_sort | Hokuto Hirano |
| collection | DOAJ |
| description | Abstract Background Co-occurrence networks—ecological associations between sampled populations of microbial communities inferred from taxonomic composition data obtained from high-throughput sequencing techniques—are widely used in microbial ecology. Several co-occurrence network methods have been proposed. Co-occurrence network methods only infer ecological associations and are often used to discuss species interactions. However, validity of this application of co-occurrence network methods is currently debated. In particular, they simply evaluate using parametric statistical models, even though microbial compositions are determined through population dynamics. Results We comprehensively evaluated the validity of common methods for inferring microbial ecological networks through realistic simulations. We evaluated how correctly nine widely used methods describe interaction patterns in ecological communities. Contrary to previous studies, the performance of the co-occurrence network methods on compositional data was almost equal to or less than that of classical methods (e.g., Pearson’s correlation). The methods described the interaction patterns in dense and/or heterogeneous networks rather inadequately. Co-occurrence network performance also depended upon interaction types; specifically, the interaction patterns in competitive communities were relatively accurately predicted while those in predator–prey (parasitic) communities were relatively inadequately predicted. Conclusions Our findings indicated that co-occurrence network approaches may be insufficient in interpreting species interactions in microbiome studies. However, the results do not diminish the importance of these approaches. Rather, they highlight the need for further careful evaluation of the validity of these much-used methods and the development of more suitable methods for inferring microbial ecological networks. |
| first_indexed | 2024-12-19T06:50:25Z |
| format | Article |
| id | doaj.art-76a1217589944912b49ff9ab3c82d32e |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-12-19T06:50:25Z |
| publishDate | 2019-06-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-76a1217589944912b49ff9ab3c82d32e2022-12-21T20:31:45ZengBMCBMC Bioinformatics1471-21052019-06-0120111410.1186/s12859-019-2915-1Difficulty in inferring microbial community structure based on co-occurrence network approachesHokuto Hirano0Kazuhiro Takemoto1Department of Bioscience and Bioinformatics, Kyushu Institute of TechnologyDepartment of Bioscience and Bioinformatics, Kyushu Institute of TechnologyAbstract Background Co-occurrence networks—ecological associations between sampled populations of microbial communities inferred from taxonomic composition data obtained from high-throughput sequencing techniques—are widely used in microbial ecology. Several co-occurrence network methods have been proposed. Co-occurrence network methods only infer ecological associations and are often used to discuss species interactions. However, validity of this application of co-occurrence network methods is currently debated. In particular, they simply evaluate using parametric statistical models, even though microbial compositions are determined through population dynamics. Results We comprehensively evaluated the validity of common methods for inferring microbial ecological networks through realistic simulations. We evaluated how correctly nine widely used methods describe interaction patterns in ecological communities. Contrary to previous studies, the performance of the co-occurrence network methods on compositional data was almost equal to or less than that of classical methods (e.g., Pearson’s correlation). The methods described the interaction patterns in dense and/or heterogeneous networks rather inadequately. Co-occurrence network performance also depended upon interaction types; specifically, the interaction patterns in competitive communities were relatively accurately predicted while those in predator–prey (parasitic) communities were relatively inadequately predicted. Conclusions Our findings indicated that co-occurrence network approaches may be insufficient in interpreting species interactions in microbiome studies. However, the results do not diminish the importance of these approaches. Rather, they highlight the need for further careful evaluation of the validity of these much-used methods and the development of more suitable methods for inferring microbial ecological networks.http://link.springer.com/article/10.1186/s12859-019-2915-1MicrobiomeCorrelation network analysisMicrobial ecologyComplex networks |
| spellingShingle | Hokuto Hirano Kazuhiro Takemoto Difficulty in inferring microbial community structure based on co-occurrence network approaches BMC Bioinformatics Microbiome Correlation network analysis Microbial ecology Complex networks |
| title | Difficulty in inferring microbial community structure based on co-occurrence network approaches |
| title_full | Difficulty in inferring microbial community structure based on co-occurrence network approaches |
| title_fullStr | Difficulty in inferring microbial community structure based on co-occurrence network approaches |
| title_full_unstemmed | Difficulty in inferring microbial community structure based on co-occurrence network approaches |
| title_short | Difficulty in inferring microbial community structure based on co-occurrence network approaches |
| title_sort | difficulty in inferring microbial community structure based on co occurrence network approaches |
| topic | Microbiome Correlation network analysis Microbial ecology Complex networks |
| url | http://link.springer.com/article/10.1186/s12859-019-2915-1 |
| work_keys_str_mv | AT hokutohirano difficultyininferringmicrobialcommunitystructurebasedoncooccurrencenetworkapproaches AT kazuhirotakemoto difficultyininferringmicrobialcommunitystructurebasedoncooccurrencenetworkapproaches |