scholarly journals Inferring microbial interaction network from microbiome data using RMN algorithm

2015 ◽  
Vol 9 (1) ◽  
Author(s):  
Kun-Nan Tsai ◽  
Shu-Hsi Lin ◽  
Wei-Chung Liu ◽  
Daryi Wang
Keyword(s):  
Interaction Network ◽  
Microbial Interaction ◽  
Microbiome Data

scholarly journals Identification of microbial interaction network: zero-inflated latent Ising model based approach

2020 ◽  
Vol 13 (1) ◽  
Author(s):  
Jie Zhou ◽  
Weston D. Viles ◽  
Boran Lu ◽  
Zhigang Li ◽  
Juliette C. Madan ◽  
...  
Keyword(s):  
Ising Model ◽  
Gut Microbiota ◽  
Relative Abundance ◽  
Interaction Network ◽  
Interaction Networks ◽  
Endocrine Regulation ◽  
Microbial Interaction ◽  
Conditional Correlation ◽  
Zero Inflation ◽  
Step Algorithm

Abstract Background Throughout their lifespans, humans continually interact with the microbial world, including those organisms which live in and on the human body. Research in this domain has revealed the extensive links between the human-associated microbiota and health. In particular, the microbiota of the human gut plays essential roles in digestion, nutrient metabolism, immune maturation and homeostasis, neurological signaling, and endocrine regulation. Microbial interaction networks are frequently estimated from data and are an indispensable tool for representing and understanding the conditional correlation between the microbes. In this high-dimensional setting, zero-inflation and unit-sum constraint for relative abundance data pose challenges to the reliable estimation of microbial interaction networks. Methods and Results To identify the microbial interaction network, the zero-inflated latent Ising (ZILI) model is proposed which assumes the distribution of relative abundance relies only on finite latent states and provides a novel way to solve issues induced by the unit-sum and zero-inflation constrains. A two-step algorithm is proposed for the model selection of ZILI. ZILI is evaluated through simulated data and subsequently applied to an infant gut microbiota dataset from New Hampshire Birth Cohort Study. The results are compared with results from Gaussian graphical model (GGM) and dichotomous Ising model (DIS). Providing ZILI is the true data-generating model, the simulation studies show that the two-step algorithm can identify the graphical structure effectively and is robust to a range of parameter settings. For the infant gut microbiota dataset, the final estimated networks from GGM and ZILI turn out to have significant overlap in which the ZILI tends to select the sparser network than those from GGM. From the shared subnetwork, a hub taxon Lachnospiraceae is identified whose involvement in human disease development has been discovered recently in literature. Conclusions Constrains induced by relative abundance of microbiota such as zero inflation and unit sum render the conditional correlation analysis unreliable for conventional methods such as GGM. The proposed optimal categoricalization based ZILI model provides an alternative yet elegant way to deal with these difficulties. The results from ZILI have reasonable biological interpretation. This model can also be used to study the microbial interaction in other body parts.

scholarly journals Phytobeneficial bacteria improve saline stress tolerance in Vicia faba and modulate microbial interaction network

2020 ◽  
Vol 729 ◽  
pp. 139020 ◽  
Author(s):  
Loubna Benidire ◽  
Fatima El Khalloufi ◽  
Khalid Oufdou ◽  
Mohamed Barakat ◽  
Joris Tulumello ◽  
...  
Keyword(s):  
Stress Tolerance ◽  
Vicia Faba ◽  
Interaction Network ◽  
Saline Stress ◽  
Microbial Interaction

scholarly journals Dynamic interaction network inference from longitudinal microbiome data

2018 ◽  
Author(s):  
Jose Lugo-Martinez ◽  
Daniel Ruiz-Perez ◽  
Giri Narasimhan ◽  
Ziv Bar-Joseph
Keyword(s):  
Network Inference ◽  
Dynamic Models ◽  
Dynamic Bayesian Network ◽  
Interaction Network ◽  
Predictive Performance ◽  
Dynamic Bayesian Networks ◽  
Data Sets ◽  
Computational Pipeline ◽  
Novel Interactions ◽  
Microbiome Data

AbstractBackgroundSeveral studies have focused on the microbiota living in environmental niches including human body sites. In many of these studies researchers collect longitudinal data with the goal of understanding not just the composition of the microbiome but also the interactions between the different taxa. However, analysis of such data is challenging and very few methods have been developed to reconstruct dynamic models from time series microbiome data.ResultsHere we present a computational pipeline that enables the integration of data across individuals for the reconstruction of such models. Our pipeline starts by aligning the data collected for all individuals. The aligned profiles are then used to learn a dynamic Bayesian network which represents causal relationships between taxa and clinical variables. Testing our methods on three longitudinal microbiome data sets we show that our pipeline improve upon prior methods developed for this task. We also discuss the biological insights provided by the models which include several known and novel interactions.ConclusionsWe propose a computational pipeline for analyzing longitudinal microbiome data. Our results provide evidence that microbiome alignments coupled with dynamic Bayesian networks improve predictive performance over previous methods and enhance our ability to infer biological relationships within the microbiome and between taxa and clinical factors.

scholarly journals Identifying Microbial Interaction Networks Based on Irregularly Spaced Longitudinal 16S rRNA sequence data

2021 ◽  
Author(s):  
Jie Zhou ◽  
Jiang Gui ◽  
Weston D Viles ◽  
Anne G Hoen
Keyword(s):  
16S Rrna ◽  
Permutation Test ◽  
Sequence Data ◽  
Interaction Network ◽  
Microbial Interactions ◽  
Microbial Interaction ◽  
Rrna Sequence ◽  
16S Rrna Sequence ◽  
Data Set ◽  
Graphical Lasso

Though being vital for human health, microbial interactions with their host and with each other are still largely obscure for researchers. To deepen the understanding, the analyses based on longitudinal data are a better choice than the cross-sectional data since the information provided by the former is usually more stable. To this end, in this paper, we first propose an EM-type algorithm to identify microbial interaction network for the irregularly spaced longitudinal measurements. Correlation functions are employed to account for the correlation across the temporal measurements for a given subject. The algorithms take advantage of the efficiency of the popular graphical lasso algorithm and can be implemented straightforwardly. Simulation studies show that the proposed algorithms can significantly outperform the conventional algorithms such as graphical lasso or neighborhood method when the correlation between measurements grows larger. In second part of the paper, based on a 16S rRNA sequence data set of gut microbiome, module-preserving permutation test is proposed to test the independence of the estimated network and the phylogeny of the microbe species. The results demonstrate evidences of strong association between the interaction network and the phylogenetic tree which indicates that the taxa closer in their genomes tend to have more/stronger interactions in their functions. The proposed algorithms can be implemented through R package lglasso at \url{https://github.com/jiezhou-2/lglasso

Florfenicol restructured the microbial interaction network for wastewater treatment by microbial electrolysis cells

2020 ◽  
Vol 183 ◽  
pp. 109145 ◽  
Author(s):  
Zhaojing Zhang ◽  
Yuanyuan Qu ◽  
Shuzhen Li ◽  
Kai Feng ◽  
Weiwei Cai ◽  
...  
Keyword(s):  
Wastewater Treatment ◽  
Interaction Network ◽  
Microbial Interaction ◽  
Microbial Electrolysis Cells ◽  
Electrolysis Cells ◽  
Microbial Electrolysis

High-order Organization of Weighted Microbial Interaction Network

2018 ◽  
Author(s):  
Xianjun Shen ◽  
Xue Gong ◽  
Xingpeng Jiang ◽  
Jincai Yang ◽  
Tingting He ◽  
...  
Keyword(s):  
Interaction Network ◽  
High Order ◽  
Microbial Interaction

scholarly journals Dynamic interaction network inference from longitudinal microbiome data

Microbiome ◽  
2019 ◽  
Vol 7 (1) ◽  
Author(s):  
Jose Lugo-Martinez ◽  
Daniel Ruiz-Perez ◽  
Giri Narasimhan ◽  
Ziv Bar-Joseph
Keyword(s):  
Network Inference ◽  
Interaction Network ◽  
Dynamic Interaction ◽  
Microbiome Data

scholarly journals Estimating Microbial Interaction Network:Zero-inflated Latent Ising Model Based Approach

2020 ◽  
Author(s):  
Jie Zhou ◽  
Weston D. Viles ◽  
Boran Lu ◽  
Zhigang Li ◽  
Juliette C. Madan ◽  
...  
Keyword(s):  
Ising Model ◽  
Gut Microbiome ◽  
Interaction Network ◽  
Interaction Networks ◽  
Supplementary Information ◽  
Accurate Estimation ◽  
Endocrine Regulation ◽  
Microbial Interaction ◽  
Infant Gut Microbiome ◽  
Step Algorithm

AbstractMotivationThroughout their lifespans, humans continually interact with the microbial world, including those organisms which live in and on the human body. Research in this domain has revealed the extensive links between the human-associated microbiota and health. In particular, the microbiota of the human gut plays essential roles in digestion, nutrient metabolism, immune maturation and homeostasis, neurological signaling, and endocrine regulation. Microbial interaction networks are frequently estimated from data and are an indispensable tool for representing and understanding the relationships among the microbes of a microbiota. In this high-dimensional setting, the zero-inflated and compositional data structure (subject to unit-sum constraint) pose challenges to the accurate estimation of microbial interaction networks.MethodWe propose the zero-inflated latent Ising (ZILI) model for microbial interaction network which assumes that the distribution of relative abundance of microbiota is determined by finite latent states. This assumption is partly supported by the existing findings in literature [20]. The ZILI model can circumvents the unit-sum constraint and alleviates the zero-inflation problem under given assumptions. As for the model selection of ZILI, a two-step algorithm is proposed. ZILI and two-step algorithm are evaluated through simulated data and subsequently applied in our investigation of an infant gut microbiome dataset from New Hampshire Birth Cohort Study. The results are compared with results from traditional Gaussian graphical model (GGM) and dichotomous Ising model (DIS).ResultsThrough the simulation studies, provided that the ZILI model is the true generative model for the data, it is shown that the two-step algorithm can estimate the graphical structure effectively and is robust to a range of alternative settings of the related factors. Both GGM and DIS can not achieve a satisfying performance in these settings. For the infant gut microbiome dataset, we use both ZILI and GGM to estimate microbial interaction network. The final estimated networks turn out to share a statistically significant overlap in which the ZILI and two-step algorithm tend to select the sparser network than those modeled by GGM. From the shared subnetwork, a hub taxon Lachnospiraceae is identified whose involvement in human disease development has been discovered recently in literature.AvailabilityThe data and programs involved in Section 4 and 5 are available on request from the correspondence [email protected] informationSupplementary materials are available at Bioinformatics

Sign in / Sign up

Export Citation Format

Share Document