分类单元
协方差
相关性
生态学
透视图(图形)
数据科学
计算机科学
生物
心理学
人工智能
统计
数学
几何学
标识
DOI:10.3410/f.736064318.793578278
摘要
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. PMID: 31253856
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