Sparse and Compositionally Robust Inference of Microbial Ecological Networks
Zachary D. Kurtz,Christian L. Müller,Emily R. Miraldi,Dan R. Littman,Martin J. Blaser,Richard Bonneau +5 more
Reads0
Chats0
TLDR
SParse InversE Covariance Estimation for Ecological Association Inference is presented, a statistical method for the inference of microbial ecological networks from amplicon sequencing datasets that outperforms state-of-the-art methods to recover edges and network properties on synthetic data under a variety of scenarios.Abstract:
16S ribosomal RNA (rRNA) gene and other environmental sequencing techniques provide snapshots of microbial communities, revealing phylogeny and the abundances of microbial populations across diverse ecosystems. While changes in microbial community structure are demonstrably associated with certain environmental conditions (from metabolic and immunological health in mammals to ecological stability in soils and oceans), identification of underlying mechanisms requires new statistical tools, as these datasets present several technical challenges. First, the abundances of microbial operational taxonomic units (OTUs) from amplicon-based datasets are compositional. Counts are normalized to the total number of counts in the sample. Thus, microbial abundances are not independent, and traditional statistical metrics (e.g., correlation) for the detection of OTU-OTU relationships can lead to spurious results. Secondly, microbial sequencing-based studies typically measure hundreds of OTUs on only tens to hundreds of samples; thus, inference of OTU-OTU association networks is severely under-powered, and additional information (or assumptions) are required for accurate inference. Here, we present SPIEC-EASI (SParse InversE Covariance Estimation for Ecological Association Inference), a statistical method for the inference of microbial ecological networks from amplicon sequencing datasets that addresses both of these issues. SPIEC-EASI combines data transformations developed for compositional data analysis with a graphical model inference framework that assumes the underlying ecological association network is sparse. To reconstruct the network, SPIEC-EASI relies on algorithms for sparse neighborhood and inverse covariance selection. To provide a synthetic benchmark in the absence of an experimentally validated gold-standard network, SPIEC-EASI is accompanied by a set of computational tools to generate OTU count data from a set of diverse underlying network topologies. SPIEC-EASI outperforms state-of-the-art methods to recover edges and network properties on synthetic data under a variety of scenarios. SPIEC-EASI also reproducibly predicts previously unknown microbial associations using data from the American Gut project.read more
Citations
More filters
Journal ArticleDOI
Microbiome Datasets Are Compositional: And This Is Not Optional.
TL;DR: The purpose of this review is to alert investigators to the dangers inherent in ignoring the compositional nature of the data, and point out that HTS datasets derived from microbiome studies can and should be treated as compositions at all stages of analysis.
Journal ArticleDOI
Best practices for analysing microbiomes.
Rob Knight,Alison Vrbanac,Bryn C. Taylor,Alexander A. Aksenov,Chris Callewaert,Chris Callewaert,Justine W. Debelius,Antonio Gonzalez,Tomasz Kosciolek,Laura-Isobel McCall,Daniel McDonald,Alexey V. Melnik,James T. Morton,Jose Navas,Robert A. Quinn,Jon G. Sanders,Austin D. Swafford,Luke R. Thompson,Luke R. Thompson,Anupriya Tripathi,Zhenjiang Zech Xu,Jesse R. Zaneveld,Qiyun Zhu,J. Gregory Caporaso,Pieter C. Dorrestein,Pieter C. Dorrestein +25 more
TL;DR: This Review focuses on recent findings that suggest that operational taxonomic unit-based analyses should be replaced with new methods that are based on exact sequence variants, methods for integrating metagenomic and metabolomic data, and issues surrounding compositional data analysis.
Journal ArticleDOI
Using MicrobiomeAnalyst for comprehensive statistical, functional, and meta-analysis of microbiome data.
TL;DR: This protocol details MicrobiomeAnalyst, a user-friendly, web-based platform for comprehensive statistical, functional, and meta-analysis of microbiome data, a one-stop shop that enables microbiome researchers to thoroughly explore their preprocessed microbiome data via intuitive web interfaces.
Journal ArticleDOI
Fungal-bacterial diversity and microbiome complexity predict ecosystem functioning
Cameron Wagg,Cameron Wagg,Klaus Schlaeppi,Samiran Banerjee,Eiko E. Kuramae,Marcel G. A. van der Heijden +5 more
TL;DR: In this article, the authors manipulated the soil microbiome in experimental grassland ecosystems and observed that microbiome diversity and microbial network complexity positively influenced multiple ecosystem functions related to nutrient cycling (e.g. multifunctionality).
Journal ArticleDOI
Disentangling Interactions in the Microbiome: A Network Perspective.
TL;DR: Network-based analytical approaches have the potential to help disentangle complex polymicrobial and microbe–host interactions, and thereby further the applicability of microbiome research to personalized medicine, public health, environmental and industrial applications, and agriculture.
References
More filters
Journal Article
R: A language and environment for statistical computing.
TL;DR: Copyright (©) 1999–2012 R Foundation for Statistical Computing; permission is granted to make and distribute verbatim copies of this manual provided the copyright notice and permission notice are preserved on all copies.
Journal ArticleDOI
Regression Shrinkage and Selection via the Lasso
TL;DR: A new method for estimation in linear models called the lasso, which minimizes the residual sum of squares subject to the sum of the absolute value of the coefficients being less than a constant, is proposed.
Journal ArticleDOI
Emergence of Scaling in Random Networks
TL;DR: A model based on these two ingredients reproduces the observed stationary scale-free distributions, which indicates that the development of large networks is governed by robust self-organizing phenomena that go beyond the particulars of the individual systems.
Journal ArticleDOI
Introducing mothur: Open-Source, Platform-Independent, Community-Supported Software for Describing and Comparing Microbial Communities
Patrick D. Schloss,Patrick D. Schloss,Sarah L. Westcott,Sarah L. Westcott,Thomas Ryabin,Justine R. Hall,Martin Hartmann,Emily B. Hollister,Ryan A. Lesniewski,Brian B. Oakley,Donovan H. Parks,Courtney J. Robinson,Jason W. Sahl,Blaz Stres,Gerhard G. Thallinger,David J. Van Horn,Carolyn F. Weber +16 more
TL;DR: M mothur is used as a case study to trim, screen, and align sequences; calculate distances; assign sequences to operational taxonomic units; and describe the α and β diversity of eight marine samples previously characterized by pyrosequencing of 16S rRNA gene fragments.
Related Papers (5)
QIIME allows analysis of high-throughput community sequencing data.
J. Gregory Caporaso,Justin Kuczynski,Jesse Stombaugh,Kyle Bittinger,Frederic D. Bushman,Elizabeth K. Costello,Noah Fierer,Antonio Gonzalez Peña,Julia K. Goodrich,Jeffrey I. Gordon,Gavin A. Huttley,Scott T. Kelley,Dan Knights,Jeremy E. Koenig,Ruth E. Ley,Catherine A. Lozupone,Daniel McDonald,Brian D. Muegge,Meg Pirrung,Jens Reeder,Joel Sevinsky,Peter J. Turnbaugh,William A. Walters,Jeremy Widmann,Tanya Yatsunenko,Jesse R. Zaneveld,Rob Knight,Rob Knight +27 more
phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data.
Paul J. McMurdie,Susan Holmes +1 more