The article emphasizes the compositional nature of microbiome datasets generated by high-throughput sequencing (HTS) and highlights the importance of treating these datasets as compositional data at all stages of analysis. Compositional data, characterized by an arbitrary total, pose unique challenges that are often overlooked or misunderstood by researchers. The authors argue that the total read count in an HTS run is a fixed-size, random sample of relative abundance, and thus, absolute abundance is not informative. They discuss the pitfalls of using non-compositional methods, such as count normalization and distance calculations, which can lead to misleading results. Instead, they advocate for compositional data analysis techniques, including ratio transformations, log-ratio transformations, and compositional principal component analysis (PCA) biplots. These methods better account for the compositional nature of the data, providing more accurate and reliable insights into microbial communities. The article also reviews specific tools and resources available for compositional data analysis in microbiome studies, emphasizing the need for researchers to adopt these methods to avoid common analytical issues.The article emphasizes the compositional nature of microbiome datasets generated by high-throughput sequencing (HTS) and highlights the importance of treating these datasets as compositional data at all stages of analysis. Compositional data, characterized by an arbitrary total, pose unique challenges that are often overlooked or misunderstood by researchers. The authors argue that the total read count in an HTS run is a fixed-size, random sample of relative abundance, and thus, absolute abundance is not informative. They discuss the pitfalls of using non-compositional methods, such as count normalization and distance calculations, which can lead to misleading results. Instead, they advocate for compositional data analysis techniques, including ratio transformations, log-ratio transformations, and compositional principal component analysis (PCA) biplots. These methods better account for the compositional nature of the data, providing more accurate and reliable insights into microbial communities. The article also reviews specific tools and resources available for compositional data analysis in microbiome studies, emphasizing the need for researchers to adopt these methods to avoid common analytical issues.