An expectation-maximization algorithm enables accurate ecological modeling using longitudinal microbiome sequencing data
Abstract Background The dynamics of microbial communities is driven by a range of interactions from symbiosis to predator-prey relationships, the majority of which are poorly understood. With the increasing availability of high-throughput microbiome taxonomic profiling data, it is now conceivable to...
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doaj-16a263a29c304072960bc03e81730be42020-11-25T03:56:50ZengBMCMicrobiome2049-26182019-08-017111410.1186/s40168-019-0729-zAn expectation-maximization algorithm enables accurate ecological modeling using longitudinal microbiome sequencing dataChenhao Li0Kern Rei Chng1Junmei Samantha Kwah2Tamar V. Av-Shalom3Lisa Tucker-Kellogg4Niranjan Nagarajan5Computational and Systems Biology, Genome Institute of SingaporeComputational and Systems Biology, Genome Institute of SingaporeComputational and Systems Biology, Genome Institute of SingaporeComputational and Systems Biology, Genome Institute of SingaporeCentre for Computational Biology, Duke–NUS Graduate Medical SchoolComputational and Systems Biology, Genome Institute of SingaporeAbstract Background The dynamics of microbial communities is driven by a range of interactions from symbiosis to predator-prey relationships, the majority of which are poorly understood. With the increasing availability of high-throughput microbiome taxonomic profiling data, it is now conceivable to directly learn the ecological models that explicitly define microbial interactions and explain community dynamics. The applicability of these approaches is severely limited by the lack of accurate absolute cell density measurements (biomass). Methods We present a new computational approach that resolves this key limitation in the inference of generalized Lotka-Volterra models (gLVMs) by coupling biomass estimation and model inference with an expectation-maximization algorithm (BEEM). Results BEEM outperforms the state-of-the-art methods for inferring gLVMs, while simultaneously eliminating the need for additional experimental biomass data as input. BEEM’s application to previously inaccessible public datasets (due to the lack of biomass data) allowed us to construct ecological models of microbial communities in the human gut on a per-individual basis, revealing personalized dynamics and keystone species. Conclusions BEEM addresses a key bottleneck in “systems analysis” of microbiomes by enabling accurate inference of ecological models from high throughput sequencing data without the need for experimental biomass measurements.http://link.springer.com/article/10.1186/s40168-019-0729-z |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Chenhao Li Kern Rei Chng Junmei Samantha Kwah Tamar V. Av-Shalom Lisa Tucker-Kellogg Niranjan Nagarajan |
spellingShingle |
Chenhao Li Kern Rei Chng Junmei Samantha Kwah Tamar V. Av-Shalom Lisa Tucker-Kellogg Niranjan Nagarajan An expectation-maximization algorithm enables accurate ecological modeling using longitudinal microbiome sequencing data Microbiome |
author_facet |
Chenhao Li Kern Rei Chng Junmei Samantha Kwah Tamar V. Av-Shalom Lisa Tucker-Kellogg Niranjan Nagarajan |
author_sort |
Chenhao Li |
title |
An expectation-maximization algorithm enables accurate ecological modeling using longitudinal microbiome sequencing data |
title_short |
An expectation-maximization algorithm enables accurate ecological modeling using longitudinal microbiome sequencing data |
title_full |
An expectation-maximization algorithm enables accurate ecological modeling using longitudinal microbiome sequencing data |
title_fullStr |
An expectation-maximization algorithm enables accurate ecological modeling using longitudinal microbiome sequencing data |
title_full_unstemmed |
An expectation-maximization algorithm enables accurate ecological modeling using longitudinal microbiome sequencing data |
title_sort |
expectation-maximization algorithm enables accurate ecological modeling using longitudinal microbiome sequencing data |
publisher |
BMC |
series |
Microbiome |
issn |
2049-2618 |
publishDate |
2019-08-01 |
description |
Abstract Background The dynamics of microbial communities is driven by a range of interactions from symbiosis to predator-prey relationships, the majority of which are poorly understood. With the increasing availability of high-throughput microbiome taxonomic profiling data, it is now conceivable to directly learn the ecological models that explicitly define microbial interactions and explain community dynamics. The applicability of these approaches is severely limited by the lack of accurate absolute cell density measurements (biomass). Methods We present a new computational approach that resolves this key limitation in the inference of generalized Lotka-Volterra models (gLVMs) by coupling biomass estimation and model inference with an expectation-maximization algorithm (BEEM). Results BEEM outperforms the state-of-the-art methods for inferring gLVMs, while simultaneously eliminating the need for additional experimental biomass data as input. BEEM’s application to previously inaccessible public datasets (due to the lack of biomass data) allowed us to construct ecological models of microbial communities in the human gut on a per-individual basis, revealing personalized dynamics and keystone species. Conclusions BEEM addresses a key bottleneck in “systems analysis” of microbiomes by enabling accurate inference of ecological models from high throughput sequencing data without the need for experimental biomass measurements. |
url |
http://link.springer.com/article/10.1186/s40168-019-0729-z |
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