对混的微生物群体社区概况进行批次校正的警告故事
Alicia J Foxx1,2,3, Adam R Rivers4
1Department of Plant Biology and Conservation Northwestern University Evanston Illinois USA.
概括
批量效应校正算法 (BECA) 在种子微生物组数据中显示不完全删除混变量. 为了可靠的微生物组分析,需要进一步研究和仔细应用这些方法.
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.
- 数据科学数据科学数据科学
背景情况:
- 批量效应在微生物组研究中很常见,可能会混结果.
- 精确分析种子微生物组需要有效纠正这些批量效应.
研究的目的:
- 评估五个批量效应校正算法 (BECAs) 的性能.
- 评估BECA在混杂种子微生物群数据集上的有效性,其中植物物种不在所有批次中.
主要方法:
- 利用种子微生物组的案例研究.
- 应用了五种BECA:零平均中心化 (ZMC),Ratio-A,条件量子力回归 (ConQuR),部分最小平方区分分析 (PLSDA) 和加权的PLSDA.
- 使用了一个混杂的数据集,其中感兴趣的共变量 (植物物种) 在各批次中分布不均.
主要成果:
- 所有五个测试的BECA都显示不完全消除批量效应.
- 性能在各个算法中各不相同,但没有一个算法实现了完整的校正.
结论:
- 目前的BECA显示在完全消除批量效应方面存在局限性,特别是在复杂,混的数据集中.
- 仔细考虑和进一步开发BECA对于强大的种子微生物组研究至关重要.
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