Beyond Samples: A Metric Revealing More Connections Of Gut Microbiota Between Individuals

Studies of gut microbiota explore their complicated connections between individuals of different characteristics by applying different metrics to abundance data obtained from fecal samples. Although classic metrics are capable to quantify differences between samples, the microbiome of fecal sample is not a good surrogate for the gut microbiome of individuals because the microbial populations of the distal colon does not adequately represent that of the entire gastrointestinal tract. To overcome the deficiency of classic metrics in which the differences can be measured between the samples analyzed, but not the corresponding populations, we propose a metric for representing composition differences in the gut microbiota of individuals. Our investigation shows this metric outperforms traditional measures for multiple scenarios. For gut microbiota in diverse geographic populations, this metric presents more explainable data variance than others, not only in regular variance analysis but also in principle component analysis and partition analysis of biologic characteristics. With time-series data, the metric further presents a strong correlation with the time interval of serial sampling. Our findings suggest that the metric is robust and powerfully detects the intrinsic variations in gut microbiota. The metric holds promise for revealing more relations between gut microbiota and human health. (C) 2021 The Authors. Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology.