Ordination and significance testing of microbial community composition derived from terminal restriction fragment length polymorphisms: application of multivariate statistics
Ordination and significance testing of microbial community composition derived from terminal restriction fragment length polymorphisms: application of multivariate statistics
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DOI：
10.1007/s10482-004-0498-x
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发表时间：
2004-11-01
影响因子：
2.6
通讯作者：
Nielsen, DL
中科院分区：
文献类型：
--
作者：
Rees, GN;Baldwin, DS;Nielsen, DL
末端限制性片段长度多态性(T-RFLP)越来越多地被用来检查微生物群落结构，因此，一系列方法被用来分析数据集。一些已发表的报告所包含的数据和结果在统计上存在缺陷或缺乏严格的统计检验。有一系列简单而强大的技术可以用来检查群落数据，但它们的使用在微生物文献中很少讨论。我们描述了一种方法，它克服了与分析社区数据集相关的一些问题，并提供了一种使数据解释简单而有效的方法。建议将Bray-Curtis系数作为构造相似矩阵的理想系数。它的优势包括能够以有意义的方式处理包含多个零块的数据集。非度量多维尺度被描述为一种基于T-RFLP数据检验群落格局的强大且易于解释的方法。重要的是，我们描述了使用数据集的重要性测试来允许对相似性进行定量评估，从而消除了比较复杂数据集的主观性。最后，我们介绍了一种样本分散的定量度量，并指出它在描述站点异质性方面的有效性。
Terminal restriction fragment length polymorphism (T-RFLP) is increasingly being used to examine microbial community structure and accordingly, a range of approaches have been used to analyze data sets. A number of published reports have included data and results that were statistically flawed or lacked rigorous statistical testing. A range of simple, yet powerful techniques are available to examine community data, however their use is seldom, if ever, discussed in microbial literature. We describe an approach that overcomes some of the problems associated with analyzing community datasets and offer an approach that makes data interpretation simple and effective. The Bray-Curtis coefficient is suggested as an ideal coefficient to be used for the construction of similarity matrices. Its strengths include its ability to deal with data sets containing multiple blocks of zeros in a meaningful manner. Non-metric multi-dimensional scaling is described as a powerful, yet easily interpreted method to examine community patterns based on T-RFLP data. Importantly, we describe the use of significance testing of data sets to allow quantitative assessment of similarity, removing subjectivity in comparing complex data sets. Finally, we introduce a quantitative measure of sample dispersion and suggest its usefulness in describing site heterogeneity.