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Updated: Jul 12, 2025

Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing

Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing

Published on: October 15, 2019

在人类微生物组研究中,用于机器学习应用的数据预处理概述.

Eliana Ibrahimi¹, Marta B Lopes^2,3, Xhilda Dhamo⁴

¹Department of Biology, Faculty of Natural Sciences, University of Tirana, Tirana, Albania.

Frontiers in microbiology

|October 23, 2023

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由于其独特的统计特性,分析人类微生物组测序数据具有挑战性. 本次审查强调了适当数据转换方法的有限使用,影响了研究的可复制性和可比性.

科学领域:

微生物组研究微生物组研究
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:

大基因组测序是微生物组与宿主相互作用研究的关键.
微生物组数据带来了独特的统计挑战 (稀疏性,组成性).
标准分析方法可能无法充分应对这些挑战.

研究的目的:

在人类微生物组研究中审查预处理和转化方法.
评估采用处理微生物组数据统计特征的方法的情况.
为选择合适的数据转换技术提供指导.

主要方法:

对最近人类微生物组出版物的文献综述.
报告了预加工和转化方法的分析.
对微生物组数据特征的方法适宜性的评估.

主要成果:

仅限于采用适合微生物组数据统计特征的转换方法.
缺乏具体考虑的相对和基于规范化的转换的普遍性.
在许多研究中,关于数据预处理的信息不一致或缺失.

结论:

关键词:

构成性的组成性.数据预处理数据预处理.人类微生物组的人类微生物组机器学习是机器学习.基因组学数据基因组学数据规范化的正常化.

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Last Updated: Jul 12, 2025

Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing

Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing

Published on: October 15, 2019

A Method for Targeted 16S Sequencing of Human Milk Samples

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Published on: March 23, 2018

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Published on: August 25, 2018

目前在微生物组数据转换方面的做法可能会阻碍可靠的分析.
方法的不完整报告引发了对可复制性和可比性的担忧.
需要指导来选择适合微生物组研究的转变.