Jaccard/Tanimoto similarity test and estimation methods for biological presence-absence data
Jaccard/Tanimoto similarity test and estimation methods for biological presence-absence data
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DOI：
10.1186/s12859-019-3118-5
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发表时间：
2019-12-24
影响因子：
3
通讯作者：
Gambin, Anna
中科院分区：
文献类型：
--
作者：
Chung, Neo Christopher;Miasojedow, Blazej;Gambin, Anna
背景资料：跨多个地理单元（或生物区）的特定物种的存在和不存在的调查被用于从生态学到微生物学的广泛生物学研究领域。使用二进制存在-不存在的数据，我们评估物种共存，有助于阐明生物和环境之间的关系。为了总结物种出现之间的相似性，我们通常使用Jaccard/Tanimoto系数，这是它们的交集与联合的比率。这是自然的，然后，确定统计上显着的Jaccard/Tanimoto系数，这表明非随机的物种共存。然而，使用该相似性系数的统计假设检验很少被使用或研究。结果：我们使用Jaccard/Tanimoto系数引入了生物存在-不存在数据相似性的假设检验。提出了几个关键的改进，包括无偏估计的期望和集中的Jaccard/Tanimoto系数，占发生概率。导出了精确解和渐近解。为了克服由于高维的计算负担，我们提出了自举和测量浓度算法，以有效地估计二进制相似性的统计显著性。全面的模拟研究表明，我们提出的方法产生准确的p值和错误发现率。所提出的估计方法比精确解快几个数量级，特别是在维数增加的情况下。我们展示了他们的应用程序，在瓦努阿图的28个岛屿和鱼类物种在3347淡水栖息地在法国的鸟类物种的共同出现的评估。所提出的方法是在一个开源的R包，称为jaccard（https：//cran.r-project.org/package=jaccardhold.Conclusion：我们介绍了一套统计方法的Jaccard/Tanimoto相似系数的二进制数据，使直接纳入概率措施的分析物种共现。由于它们的一般性，所提出的方法和实施方式适用于从基因组学、生物化学和其他科学领域产生的广泛的二进制数据。
Background: A survey of presences and absences of specific species across multiple biogeographic units (or bioregions) are used in a broad area of biological studies from ecology to microbiology. Using binary presence-absence data, we evaluate species co-occurrences that help elucidate relationships among organisms and environments. To summarize similarity between occurrences of species, we routinely use the Jaccard/Tanimoto coefficient, which is the ratio of their intersection to their union. It is natural, then, to identify statistically significant Jaccard/Tanimoto coefficients, which suggest non-random co-occurrences of species. However, statistical hypothesis testing using this similarity coefficient has been seldom used or studied.Results: We introduce a hypothesis test for similarity for biological presence-absence data, using the Jaccard/Tanimoto coefficient. Several key improvements are presented including unbiased estimation of expectation and centered Jaccard/Tanimoto coefficients, that account for occurrence probabilities. The exact and asymptotic solutions are derived. To overcome a computational burden due to high-dimensionality, we propose the bootstrap and measurement concentration algorithms to efficiently estimate statistical significance of binary similarity. Comprehensive simulation studies demonstrate that our proposed methods produce accurate p-values and false discovery rates. The proposed estimation methods are orders of magnitude faster than the exact solution, particularly with an increasing dimensionality. We showcase their applications in evaluating co-occurrences of bird species in 28 islands of Vanuatu and fish species in 3347 freshwater habitats in France. The proposed methods are implemented in an open source R package called jaccard (https://cran.r-project.org/package=jaccard).Conclusion: We introduce a suite of statistical methods for the Jaccard/Tanimoto similarity coefficient for binary data, that enable straightforward incorporation of probabilistic measures in analysis for species co-occurrences. Due to their generality, the proposed methods and implementations are applicable to a wide range of binary data arising from genomics, biochemistry, and other areas of science.