Correlation analyses are often included in bioinformatic pipelines as methods for inferring taxon–taxon interactions. In this perspective, we highlight the pitfalls of inferring interactions from covariance and suggest methods, study design considerations, and additional data types for improving high-throughput interaction inferences. We conclude that correlation, even when augmented by other data types, almost never provides reliable information on direct biotic interactions in real-world ecosystems. These bioinformatically inferred associations are useful for reducing the number of potential hypotheses that we might test, but will never preclude the necessity for experimental validation.
Difficulties in dissecting interaction networks in model communities inspired @avcarrology to write our recent Perspective Article in @ISME_microbes on the 'Use and abuse of correlation analyses in microbial ecology.' Check out his blog post here https://t.co/ZfRwClsBJs @isbsci— Sean Gibbons 🦠💩 (@gibbological) July 8, 2019