Résumé
Pathogens and the pathobiome they interact with can be characterized by 16S rRNA sequencing that reveal the microbial composition of a studied environment. The impact of microbial interactions on the properties of microbiota is a topic of key interest in disease ecology. Network analysis is often employed to characterise potential microbial interactions (Layeghifard et al., 2016). It typically requires identifying pairwise statistical associations between the occurrence or abundance of bacterial operational taxonomic units (OTUs) (Faust and Raes, 2012).Microbiota contain hundreds of OTUs, most of which are rare, including pathogens. This feature of community structure can lead to methodological difficulties to detect associations: simulations have shown that methods for detecting pairwise associations between OTUs (which presumably reflect interactions) yield problematic results (Weiss et al., 2016). Rare OTUs are commonly removed restrictively in an empirical filtering step resulting in a loss of information (Friedman and Alm, 2012; Kurtz et al., 2015). We explored the statistical testability ofsuch associations given occurrence and read abundance data. The goal was to understand the impact of OTU rarity on the testability of correlation coefficients. We found that a large proportion of pairwise associations, especially negative associations, cannot be reliably tested. Investigations of microbial agents for biological control purposes are therefore restrained. Consequently, identifying testable associations could serve as an objective method for trimming datasets (in lieu of current empirical approaches). Our threshold is constructed for presence‐absence data and read count data and it depends on number of samples and on OTU prevalence.