Power analysis is essential to decide the sample size of metagenomic sequencing experiments in a case–control study for identifying differentially abundant (DA) microbes. However, the complexity of microbial data characteristics, such as excessive zeros, over-dispersion, compositionality, intrinsically microbial correlations and variable sequencing depths, makes the power analysis particularly challenging because the analytical form is usually unavailable. Here, we develop a simulation-based power assessment strategy and R package powmic, which considers the complexity of microbial data characteristics. A real data example demonstrates the usage of powmic.

中文翻译：

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powmic：用于微生物组病例对照研究的能力评估的R软件包。

功率分析对于确定病例对照研究中的宏基因组测序实验的样本量至关重要，以鉴定差异丰富的（DA）微生物。但是，微生物数据特征的复杂性，例如过多的零，过度分散，组成，固有的微生物相关性和可变的测序深度，使得功效分析特别具有挑战性，因为通常无法使用分析形式。在这里，我们开发了一种基于模拟的功率评估策略和R软件包，该软件包考虑了微生物数据特征的复杂性。一个真实的数据示例演示了powmic的用法。