Truncated Rank-Based Tests for Two-Part Models with Excessive Zeros and Applications to Microbiome Data
含大量零值的两部分模型截断秩检验及微生物组应用AI 解读 ↓
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含大量零值的两部分模型截断秩检验及微生物组应用
- 研究问题
- 对于含零点质量且零值比例很高的非负数据，如何开展两组、多组和重复测量比较，改善标准秩检验的检验功效？
- 方法与思路
- 作者提出截断 Wilcoxon 秩和检验及两种截断 Kruskal–Wallis 检验，并以个体内置换处理重复测量的组成数据。
- 材料陈述的贡献
- 摘要称，渐近相对效率分析与模拟支持功效提高，尤其在零值较多时；微生物组应用发现了标准检验遗漏的若干菌属。
- 阅读关注点
- 应核对截断规则、零值比例差异的处理、个体内置换的有效条件，以及组成约束和多重检验是否影响结果解释。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未核实；摘要未给效率比较条件、显著性控制细节或数值，不能推广功效优势。
原文摘要
High-throughput sequencing technology allows us to test the compositional difference of bacteria in different populations. One important feature of human microbiome data is that it often includes a large number of zeros. Such data can be treated as being generated from a two-part model that includes a zero point-mass. Motivated by analysis of such non-negative data with excessive zeros, we introduce several truncated rank-based two-group and multi-group tests for such data, including a truncated rank-based Wilcoxon rank-sum test for two-group comparison and two truncated Kruskal-Wallis tests for multi-group comparison. We show both analytically through asymptotic relative efficiency analysis and by simulations that the proposed tests have higher power than the standard rank-based tests, especially when the proportion of zeros in the data is high. The tests can also be applied to repeated measurements of compositional data via simple within-subject permutations. In a simple before-and-after treatment experiment, the within-subject permutation is similar to the paired rank test. However, the proposed tests handle the excessive zeros, which leads to a better power. We apply the tests to the analysis of a gut microbiome data set to compare the microbiome compositions of healthy and pediatric Crohn's disease patients and to assess the treatment effects on microbiome compositions. We identify several bacterial genera that are missed by the standard rank-based tests.