微生物组数据分析

IF 7.4 1区 数学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS
Christine B. Peterson, Satabdi Saha, Kim-Anh Do
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引用次数: 4

摘要

微生物群代表了一个隐藏的世界,里面的微生物不仅存在于我们周围的环境中,也存在于我们自己的身体中。通过对这些看不见的生物进行全面的分析,现代基因组测序工具赋予了我们前所未有的能力来描述这些种群,并揭示它们对我们的环境和健康的巨大影响。微生物组数据的统计分析对于从观察到的丰度推断模式至关重要。分析方法在这一领域的应用和发展需要仔细考虑微生物组谱的独特方面。我们首先简要介绍微生物组数据的收集和处理,并描述由此产生的数据结构。然后,我们概述了微生物组数据分析中关键任务的统计方法,包括数据可视化、组间微生物丰度比较、回归建模和网络推理。最后,我们进行了讨论,并强调了有趣的未来方向。预计《统计年鉴及其应用》第11卷的最终在线出版日期为2024年3月。修订后的估计数请参阅http://www.annualreviews.org/page/journal/pubdates。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Analysis of Microbiome Data
The microbiome represents a hidden world of tiny organisms populating not only our surroundings but also our own bodies. By enabling comprehensive profiling of these invisible creatures, modern genomic sequencing tools have given us an unprecedented ability to characterize these populations and uncover their outsize impact on our environment and health. Statistical analysis of microbiome data is critical to infer patterns from the observed abundances. The application and development of analytical methods in this area require careful consideration of the unique aspects of microbiome profiles. We begin this review with a brief overview of microbiome data collection and processing and describe the resulting data structure. We then provide an overview of statistical methods for key tasks in microbiome data analysis, including data visualization, comparison of microbial abundance across groups, regression modeling, and network inference. We conclude with a discussion and highlight interesting future directions.Expected final online publication date for the Annual Review of Statistics and Its Application, Volume 11 is March 2024. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
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来源期刊
Annual Review of Statistics and Its Application
Annual Review of Statistics and Its Application MATHEMATICS, INTERDISCIPLINARY APPLICATIONS-STATISTICS & PROBABILITY
CiteScore
13.40
自引率
1.30%
发文量
29
期刊介绍: The Annual Review of Statistics and Its Application publishes comprehensive review articles focusing on methodological advancements in statistics and the utilization of computational tools facilitating these advancements. It is abstracted and indexed in Scopus, Science Citation Index Expanded, and Inspec.
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