Cassol Ignacio, Ibañez Mauro, Bustamante Juan Pablo
Facultad de Ingeniería, Universidad Austral, LIDTUA, CIC, Buenos Aires, Argentina.
Facultad de Ingeniería, Universidad Nacional de Entre Ríos, Oro Verde, Argentina.
Sci Rep. 2025 Jan 3;15(1):622. doi: 10.1038/s41598-024-77864-y.
Studies of microbial communities vary widely in terms of analysis methods. In this growing field, the wide variety of diversity measures and lack of consistency make it harder to compare different studies. Most existing alpha diversity metrics are inherited from other disciplines and their assumptions are not always directly meaningful or true for microbiome data. Many existing microbiome studies apply one or some alpha diversity metrics with no fundamentals but also an unclear results interpretation. This work focuses on a theoretical, empirical, and comparative analysis of 19 frequently and less-frequently used microbial alpha diversity metrics grouped into 4 proposed categories, including key features of every analyzed metric with their mathematical assumptions, to provide a deeper understanding of the existing metrics and a practical implementation guide for future studies. Key metrics that should be required in microbiome analysis include richness, phylogenetic diversity, entropy, dominance of a few microbes over others, and an estimate of unobserved microbes. Collectively, these metrics contribute to a comprehensive set of analyses characterizing samples, allowing the determination of key aspects that might be otherwise obscured by partial or biased information. These guidelines enable further detailed analysis by each author according to their specific interests and clinical trials. Several practical examples are provided to illustrate how these recommendations improve the quality and depth of information obtained, facilitating better interpretation when working with microbiome data. These guidelines can be applied to both existing and future research studies, enhancing the standardization, consistency, and robustness of the analyses conducted. This approach aims to improve the capture of biological diversity, leading to better interpretations and insights.
微生物群落研究在分析方法上差异很大。在这个不断发展的领域中，多样的多样性测量方法以及缺乏一致性使得不同研究之间更难进行比较。大多数现有的α多样性指标是从其他学科继承而来的，其假设对于微生物组数据并不总是直接有意义或正确的。许多现有的微生物组研究应用了一个或一些α多样性指标，但既没有理论基础，结果解释也不清晰。这项工作聚焦于对19种常用和不常用的微生物α多样性指标进行理论、实证和比较分析，这些指标被分为4类，包括每个分析指标的关键特征及其数学假设，以便更深入地理解现有指标，并为未来研究提供实用的实施指南。微生物组分析中应必备的关键指标包括丰富度、系统发育多样性、熵、少数微生物相对于其他微生物的优势度以及未观察到的微生物的估计值。总体而言，这些指标有助于对样本进行全面的分析，从而确定可能因部分或有偏差的信息而被掩盖的关键方面。这些指南使每位作者能够根据其特定兴趣和临床试验进行进一步的详细分析。文中提供了几个实际例子来说明这些建议如何提高所获得信息的质量和深度，便于在处理微生物组数据时进行更好的解释。这些指南可应用于现有和未来的研究，提高所进行分析的标准化、一致性和稳健性。这种方法旨在更好地捕捉生物多样性，从而实现更好的解释和见解。
