Correlation detection strategies in microbial data sets vary widely in sensitivity and precision
Sophie Weiss,Will Van Treuren,Catherine A. Lozupone,Karoline Faust,Karoline Faust,Jonathan Friedman,Ye Deng,Ye Deng,Li C. Xia,Li C. Xia,Zhenjiang Zech Xu,Luke K. Ursell,Eric J. Alm,Amanda Birmingham,Jacob A. Cram,Jed A. Fuhrman,Jeroen Raes,Jeroen Raes,Fengzhu Sun,Jizhong Zhou,Jizhong Zhou,Jizhong Zhou,Rob Knight +22 more
TLDR
This work benchmarks the performance of eight correlation techniques on simulated and real data in response to challenges specific to microbiome studies: fractional sampling of ribosomal RNA sequences, uneven sampling depths, rare microbes and a high proportion of zero counts.Abstract:
Disruption of healthy microbial communities has been linked to numerous diseases, yet microbial interactions are little understood. This is due in part to the large number of bacteria, and the much larger number of interactions (easily in the millions), making experimental investigation very difficult at best and necessitating the nascent field of computational exploration through microbial correlation networks. We benchmark the performance of eight correlation techniques on simulated and real data in response to challenges specific to microbiome studies: fractional sampling of ribosomal RNA sequences, uneven sampling depths, rare microbes and a high proportion of zero counts. Also tested is the ability to distinguish signals from noise, and detect a range of ecological and time-series relationships. Finally, we provide specific recommendations for correlation technique usage. Although some methods perform better than others, there is still considerable need for improvement in current techniques.read more
Citations
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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
Keystone taxa as drivers of microbiome structure and functioning
TL;DR: A definition of keystone taxa in microbial ecology is proposed and over 200 microbial keystoneTaxa that have been identified in soil, plant and marine ecosystems, as well as in the human microbiome are summarized.
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
How to make more out of community data? A conceptual framework and its implementation as models and software.
Otso Ovaskainen,Otso Ovaskainen,Gleb Tikhonov,Anna Norberg,F. Guillaume Blanchet,F. Guillaume Blanchet,Leo L. Duan,David B. Dunson,Tomas Roslin,Nerea Abrego,Nerea Abrego +10 more
TL;DR: HMSC is operationalise the HMSC framework as a hierarchical Bayesian joint species distribution model, and is implemented as R- and Matlab-packages which enable computationally efficient analyses of large data sets.
Journal ArticleDOI
The Human Gut Microbiome: From Association to Modulation
TL;DR: The type of studies that will be essential for translating microbiome research into targeted modulations with dedicated benefits for the human host are discussed.
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