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.

中文翻译：

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微生物组数据分析


微生物组代表了一个隐藏的微小生物世界，这些微小生物不仅存在于我们的周围环境中，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分析，现代基因组测序工具为我们提供了前所未有的能力来表征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。分析方法在该领域的应用和开发需要仔细考虑微生物组谱的独特方面。我们首先简要概述了微生物组数据收集和处理，并描述了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、各组微生物丰度比较、回归建模和网络推断。我们以讨论结束，并重点介绍有趣的未来方向。