A zero inflated log-normal model for inference of sparse microbial association networks.

The advent of high-throughput metagenomic sequencing has prompted the development of efficient taxonomic profiling methods allowing to measure the presence, abundance and phylogeny of organisms in a wide range of environmental samples. Multivariate sequence-derived abundance data further has the pot...

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Autores principales:	Vincent Prost, Stéphane Gazut, Thomas Brüls
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/78a53343eda847c2b92665a0743b75b5
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spelling	oai:doaj.org-article:78a53343eda847c2b92665a0743b75b52021-11-25T05:40:36ZA zero inflated log-normal model for inference of sparse microbial association networks.1553-734X1553-735810.1371/journal.pcbi.1009089https://doaj.org/article/78a53343eda847c2b92665a0743b75b52021-06-01T00:00:00Zhttps://doi.org/10.1371/journal.pcbi.1009089https://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358The advent of high-throughput metagenomic sequencing has prompted the development of efficient taxonomic profiling methods allowing to measure the presence, abundance and phylogeny of organisms in a wide range of environmental samples. Multivariate sequence-derived abundance data further has the potential to enable inference of ecological associations between microbial populations, but several technical issues need to be accounted for, like the compositional nature of the data, its extreme sparsity and overdispersion, as well as the frequent need to operate in under-determined regimes. The ecological network reconstruction problem is frequently cast into the paradigm of Gaussian Graphical Models (GGMs) for which efficient structure inference algorithms are available, like the graphical lasso and neighborhood selection. Unfortunately, GGMs or variants thereof can not properly account for the extremely sparse patterns occurring in real-world metagenomic taxonomic profiles. In particular, structural zeros (as opposed to sampling zeros) corresponding to true absences of biological signals fail to be properly handled by most statistical methods. We present here a zero-inflated log-normal graphical model (available at https://github.com/vincentprost/Zi-LN) specifically aimed at handling such "biological" zeros, and demonstrate significant performance gains over state-of-the-art statistical methods for the inference of microbial association networks, with most notable gains obtained when analyzing taxonomic profiles displaying sparsity levels on par with real-world metagenomic datasets.Vincent ProstStéphane GazutThomas BrülsPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 17, Iss 6, p e1009089 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Vincent Prost Stéphane Gazut Thomas Brüls A zero inflated log-normal model for inference of sparse microbial association networks.
description	The advent of high-throughput metagenomic sequencing has prompted the development of efficient taxonomic profiling methods allowing to measure the presence, abundance and phylogeny of organisms in a wide range of environmental samples. Multivariate sequence-derived abundance data further has the potential to enable inference of ecological associations between microbial populations, but several technical issues need to be accounted for, like the compositional nature of the data, its extreme sparsity and overdispersion, as well as the frequent need to operate in under-determined regimes. The ecological network reconstruction problem is frequently cast into the paradigm of Gaussian Graphical Models (GGMs) for which efficient structure inference algorithms are available, like the graphical lasso and neighborhood selection. Unfortunately, GGMs or variants thereof can not properly account for the extremely sparse patterns occurring in real-world metagenomic taxonomic profiles. In particular, structural zeros (as opposed to sampling zeros) corresponding to true absences of biological signals fail to be properly handled by most statistical methods. We present here a zero-inflated log-normal graphical model (available at https://github.com/vincentprost/Zi-LN) specifically aimed at handling such "biological" zeros, and demonstrate significant performance gains over state-of-the-art statistical methods for the inference of microbial association networks, with most notable gains obtained when analyzing taxonomic profiles displaying sparsity levels on par with real-world metagenomic datasets.
format	article
author	Vincent Prost Stéphane Gazut Thomas Brüls
author_facet	Vincent Prost Stéphane Gazut Thomas Brüls
author_sort	Vincent Prost
title	A zero inflated log-normal model for inference of sparse microbial association networks.
title_short	A zero inflated log-normal model for inference of sparse microbial association networks.
title_full	A zero inflated log-normal model for inference of sparse microbial association networks.
title_fullStr	A zero inflated log-normal model for inference of sparse microbial association networks.
title_full_unstemmed	A zero inflated log-normal model for inference of sparse microbial association networks.
title_sort	zero inflated log-normal model for inference of sparse microbial association networks.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/78a53343eda847c2b92665a0743b75b5
work_keys_str_mv	AT vincentprost azeroinflatedlognormalmodelforinferenceofsparsemicrobialassociationnetworks AT stephanegazut azeroinflatedlognormalmodelforinferenceofsparsemicrobialassociationnetworks AT thomasbruls azeroinflatedlognormalmodelforinferenceofsparsemicrobialassociationnetworks AT vincentprost zeroinflatedlognormalmodelforinferenceofsparsemicrobialassociationnetworks AT stephanegazut zeroinflatedlognormalmodelforinferenceofsparsemicrobialassociationnetworks AT thomasbruls zeroinflatedlognormalmodelforinferenceofsparsemicrobialassociationnetworks
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A zero inflated log-normal model for inference of sparse microbial association networks.

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