Use and abuse of correlation analyses in microbial ecology
Use and abuse of correlation analyses in microbial ecology
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DOI：
10.1038/s41396-019-0459-z
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发表时间：
2019-11-01
期刊：
影响因子：
11
通讯作者：
Gibbons, Sean M.
中科院分区：
文献类型：
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作者：
Carr, Alex;Diener, Christian;Gibbons, Sean M.
相关性分析经常作为推断分类群-分类群相互作用的方法被包括在生物信息学管道中。从这个角度来看，我们强调了从协方差推断相互作用的陷阱，并提出了改进高通量相互作用推断的方法、研究设计考虑因素和其他数据类型。我们得出的结论是，即使有其他数据类型的增强，相关性也几乎无法提供真实生态系统中直接生物相互作用的可靠信息。这些生物信息学推断的关联有助于减少我们可能测试的潜在假设的数量，但永远不会排除实验验证的必要性。
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.