DOI: 10.1111/1755-0998.13126
论文题名: Investigating microbial associations from sequencing survey data with co-correspondence analysis
作者: Alric B. ; ter Braak C.J.F. ; Desdevises Y. ; Lebredonchel H. ; Dray S.
刊名: Molecular Ecology Resources
ISSN: 1755098X
出版年: 2020
卷: 20, 期: 2 语种: 英语
英文关键词: co-correspondence analysis
; co-occurrence network
; Mamiellophyceae
; microbial eukaryotes
; next-generation sequencing
; Prasinovirus
Scopus关键词: article
; correspondence analysis
; eukaryote
; microalga
; microbial community
; next generation sequencing
; nonhuman
; virus
英文摘要: Microbial communities, which drive major ecosystem functions, consist of a wide range of interacting species. Understanding how microbial communities are structured and the processes underlying this is crucial to interpreting ecosystem responses to global change but is challenging as microbial interactions cannot usually be directly observed. Multiple efforts are currently focused to combine next-generation sequencing (NGS) techniques with refined statistical analysis (e.g., network analysis, multivariate analysis) to characterize the structures of microbial communities. However, most of these approaches consider a single table of sequencing data measured for several samples. Technological advances now make it possible to collect NGS data on different taxonomic groups simultaneously for the same samples, allowing us to analyse a pair of tables. Here, an analytical framework based on co-correspondence analysis (CoCA) is proposed to study the distributions, assemblages and interactions between two microbial communities. We show the ability of this approach to highlight the relationships between two microbial communities, using two data sets exhibiting various types of interactions. CoCA identified strong association patterns between autotrophic and heterotrophic microbial eukaryote assemblages, on the one hand, and between microalgae and viruses, on the other. We demonstrate also how CoCA can be used, complementary to network analysis, to reorder co-occurrence networks and thus investigate the presence of patterns in ecological networks. © 2019 John Wiley & Sons Ltd Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/159041
Appears in Collections: 气候变化与战略
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作者单位: CNRS, UMR 5558, Laboratoire de Biométrie et Biologie Evolutive, Université de Lyon, Villeurbanne, France; Biometris, Wageningen University and Research, Wageningen, Netherlands; CNRS, UMR 7232, BIOM, Biologie Intégrative des Organismes Marins, Observatoire Océanologique, Sorbonne Université, Banyuls sur Mer, France
Recommended Citation:
Alric B.,ter Braak C.J.F.,Desdevises Y.,et al. Investigating microbial associations from sequencing survey data with co-correspondence analysis[J]. Molecular Ecology Resources,2020-01-01,20(2)