|Title||Investigating microbial associations from sequencing survey data with co-correspondence analysis|
|Author(s)||Alric, Benjamin; Braak, Cajo J.F. ter; Desdevises, Yves; Lebredonchel, Hugo; Dray, Stéphane|
|Source||Molecular Ecology Resources 20 (2020)2. - ISSN 1755-098X - p. 468 - 480.|
|Publication type||Refereed Article in a scientific journal|
|Keyword(s)||co-correspondence analysis - co-occurrence network - Mamiellophyceae - microbial eukaryotes - next-generation sequencing - Prasinovirus|
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.