组合组合数据集在协差网络重建中引入错误
James D Brunner1,2, Aaron J Robinson1, Patrick S G Chain1
1Biosciences Division, Los Alamos National Laboratory, Los Alamos, NM, USA.
ISME communications
|May 30, 2024
概括
推断跨王国 (细菌和真菌) 的微生物相互作用是具有挑战性的. 这项研究揭示了常见的方法与跨王国数据的斗争,影响了关键微生物参与者的识别.
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.
- 生态生态学 生态生态学
背景情况:
- 微生物群落包括跨越多个王国的多种类型.
- 细菌和真菌之间的相互作用显著影响社区结构.
- 推断跨王国的关联比国内的关联更复杂,因为数据的特点.
研究的目的:
- 量化跨王国网络推理的理论和实践挑战.
- 评估常见的网络推断技术,以评估它们处理组合组合数据集的能力 (例如16S和ITS测序).
- 评估从这些技术中得出的王国内部和王国间的关联的准确性和有用性.
主要方法:
- 利用合成和现实世界的微生物组数据.
- 详细介绍了从同一环境中组合组成数据集的理论问题.
- 调查并测试了使用模拟样本与已知的基准真理关联的常见网络推理技术.
- 在跨王国网络推断中识别了错误签名.
主要成果:
- 标准的网络推断技术在准确推断跨王国微生物关联方面存在局限性.
- 虽然方法减轻了一些跨王国推理错误,但对于实践应用,如识别强相关性或基石分类,差异是最小的.
- 在现实世界的环境微生物组数据中,从跨王国推断中确定并观察到一个明显的错误签名.
结论:
- 从组合的微生物组数据集中推断跨王国网络对当前的标准技术提出了重大挑战.
- 识别跨王国的微生物相互作用和关键类型的准确性和实用性仍然有限.
- 识别的错误签名提供了一种方法来识别跨王国微生物组网络分析中的潜在不准确性.
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