MPLasso: Inferring microbial association networks using prior microbial knowledge.
Due to the recent advances in high-throughput sequencing technologies, it becomes possible to directly analyze microbial communities in human body and environment. To understand how microbial communities adapt, develop, and interact with the human body and the surrounding environment, one of the fun...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2017-12-01
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| Series: | PLoS Computational Biology |
| Online Access: | http://europepmc.org/articles/PMC5760079?pdf=render |
| _version_ | 1818084930237759488 |
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| author | Chieh Lo Radu Marculescu |
| author_facet | Chieh Lo Radu Marculescu |
| author_sort | Chieh Lo |
| collection | DOAJ |
| description | Due to the recent advances in high-throughput sequencing technologies, it becomes possible to directly analyze microbial communities in human body and environment. To understand how microbial communities adapt, develop, and interact with the human body and the surrounding environment, one of the fundamental challenges is to infer the interactions among different microbes. However, due to the compositional and high-dimensional nature of microbial data, statistical inference cannot offer reliable results. Consequently, new approaches that can accurately and robustly estimate the associations (putative interactions) among microbes are needed to analyze such compositional and high-dimensional data. We propose a novel framework called Microbial Prior Lasso (MPLasso) which integrates graph learning algorithm with microbial co-occurrences and associations obtained from scientific literature by using automated text mining. We show that MPLasso outperforms existing models in terms of accuracy, microbial network recovery rate, and reproducibility. Furthermore, the association networks we obtain from the Human Microbiome Project datasets show credible results when compared against laboratory data. |
| first_indexed | 2024-12-10T20:01:42Z |
| format | Article |
| id | doaj.art-ed4e159eae3e496f8a352c7975306eb4 |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-12-10T20:01:42Z |
| publishDate | 2017-12-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-ed4e159eae3e496f8a352c7975306eb42022-12-22T01:35:31ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582017-12-011312e100591510.1371/journal.pcbi.1005915MPLasso: Inferring microbial association networks using prior microbial knowledge.Chieh LoRadu MarculescuDue to the recent advances in high-throughput sequencing technologies, it becomes possible to directly analyze microbial communities in human body and environment. To understand how microbial communities adapt, develop, and interact with the human body and the surrounding environment, one of the fundamental challenges is to infer the interactions among different microbes. However, due to the compositional and high-dimensional nature of microbial data, statistical inference cannot offer reliable results. Consequently, new approaches that can accurately and robustly estimate the associations (putative interactions) among microbes are needed to analyze such compositional and high-dimensional data. We propose a novel framework called Microbial Prior Lasso (MPLasso) which integrates graph learning algorithm with microbial co-occurrences and associations obtained from scientific literature by using automated text mining. We show that MPLasso outperforms existing models in terms of accuracy, microbial network recovery rate, and reproducibility. Furthermore, the association networks we obtain from the Human Microbiome Project datasets show credible results when compared against laboratory data.http://europepmc.org/articles/PMC5760079?pdf=render |
| spellingShingle | Chieh Lo Radu Marculescu MPLasso: Inferring microbial association networks using prior microbial knowledge. PLoS Computational Biology |
| title | MPLasso: Inferring microbial association networks using prior microbial knowledge. |
| title_full | MPLasso: Inferring microbial association networks using prior microbial knowledge. |
| title_fullStr | MPLasso: Inferring microbial association networks using prior microbial knowledge. |
| title_full_unstemmed | MPLasso: Inferring microbial association networks using prior microbial knowledge. |
| title_short | MPLasso: Inferring microbial association networks using prior microbial knowledge. |
| title_sort | mplasso inferring microbial association networks using prior microbial knowledge |
| url | http://europepmc.org/articles/PMC5760079?pdf=render |
| work_keys_str_mv | AT chiehlo mplassoinferringmicrobialassociationnetworksusingpriormicrobialknowledge AT radumarculescu mplassoinferringmicrobialassociationnetworksusingpriormicrobialknowledge |