Application of multivariate statistical techniques in microbial ecology.
Application of multivariate statistical techniques in microbial ecology.
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DOI：
10.1111/mec.13536
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发表时间：
2016-03
影响因子：
4.9
通讯作者：
Shankar V
中科院分区：
文献类型：
--
作者：
Paliy O;Shankar V
高通量分子分析方法的最新进展导致了大规模生态数据集研究的爆发。在微生物生态学领域取得了特别显著的效果，新的实验方法为复杂微生物群落的组成、功能和动态变化提供了深入的评估。因为即使是一个单一的高通量实验也会产生大量的数据，强大的多元分析统计技术非常适合分析和解释这些数据集。有许多不同的多变量技术可用，通常不清楚应该将哪种方法应用于特定的数据集。在这篇综述中，我们描述和比较了最广泛使用的多元统计技术，包括探索性、解释性和歧视性程序。我们考虑了这些方法的几个重要的局限性和假设，并提出了这些方法在最近的研究中如何被利用的例子，以提供对微生物世界生态学的见解。最后，根据研究问题和数据集结构提出了选择合适方法的建议。
Recent advances in high-throughput methods of molecular analyses have led to an explosion of studies generating large scale ecological datasets. Especially noticeable effect has been attained in the field of microbial ecology, where new experimental approaches provided in-depth assessments of the composition, functions, and dynamic changes of complex microbial communities. Because even a single high-throughput experiment produces large amounts of data, powerful statistical techniques of multivariate analysis are well suited to analyze and interpret these datasets. Many different multivariate techniques are available, and often it is not clear which method should be applied to a particular dataset. In this review we describe and compare the most widely used multivariate statistical techniques including exploratory, interpretive, and discriminatory procedures. We consider several important limitations and assumptions of these methods, and we present examples of how these approaches have been utilized in recent studies to provide insight into the ecology of the microbial world. Finally, we offer suggestions for the selection of appropriate methods based on the research question and dataset structure.