对稀疏的微生物组数据的差异丰度分析策略,以小组智能结构的零为准
Fentaw Abegaz1,2, Davar Abedini3, Fred White3
1Swammerdam Institute for Life Sciences, University of Amsterdam, 1098 XH, Amsterdam, The Netherlands. f.a.yazew@uva.nl.
Scientific reports
|May 30, 2024
概括
这项研究引入了一种新的方法,可以准确地比较微生物群落在序列数据中的丰度. 该方法有效地解决了零通胀和结构化零等挑战,改善了微生物组研究的差异丰度测试.
科学领域:
- 微生物学 微生物学
- 生物信息学是一种生物信息学.
- 统计分析 统计分析
背景情况:
- 使用计数序列数据的微生物组分析面临着零通货膨胀,过度分散和非正常性等挑战.
- 现有的差异丰度测试方法与膨胀的零数和小组结构的零数扎,即使在预处理后也是如此.
研究的目的:
- 开发和验证一种可靠的方法来比较微生物社区的丰富性.
- 在微生物组数据集中专门解决零通货膨胀和分组结构化的零.
主要方法:
- 提出了一种综合方法,将DESeq2-ZINBWaVE集成为零通货膨胀和DESeq2集成为结构化零.
- 通过广泛的模拟验证了该方法.
- 将组合方法应用于两个植物微生物组数据集.
主要成果:
- 综合方法有效地处理零通胀和群体结构化的零.
- 对植物微生物组数据的成功应用确定了候选种群.
- 在模拟中证明了比现有方法更好的性能.
结论:
- 拟议的综合差异丰度测试方法增强了微生物群计数数据的分析.
- 这种方法提供了一种更可靠的方法来识别复杂微生物群落中差异丰富的种类.
- 促进了微生物组研究中关键微生物种群的进一步实验验证.
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