High throughput sequencing (HTS)-based technology enables identifying and quantifying non-culturable microbial organisms in all environments. Microbial sequences have enhanced our understanding of the human microbiome, the soil and plant environment, and the marine environment. All molecular microbial data pose statistical challenges due to contamination sequences from reagents, batch effects, unequal sampling, and undetected taxa. Technical biases and heteroscedasticity have the strongest effects, but different strains across subjects and environments also make direct differential abundance testing unwieldy. We provide an introduction to a few statistical tools that can overcome some of these difficulties and demonstrate those tools on an example. We show how standard statistical methods, such as simple hierarchical mixture and topic models, can facilitate inferences on latent microbial communities. We also review some nonparametric Bayesian approaches that combine visualization and uncertainty quantification. The intersection of molecular microbial biology and statistics is an exciting new venue. Finally, we list some of the important open problems that would benefit from more careful statistical method development.

基于高通量测序 (HTS) 的技术能够识别和量化所有环境中的不可培养微生物。微生物序列增强了我们对人类微生物组、土壤和植物环境以及海洋环境的理解。由于来自试剂的污染序列、批次效应、不平等采样和未检测到的分类单元，所有分子微生物数据都带来了统计挑战。技术偏差和异方差性具有最强的影响，但受试者和环境之间的不同压力也使得直接差异丰度测试变得笨拙。我们介绍了一些可以克服其中一些困难的统计工具，并在示例中演示了这些工具。我们展示了标准的统计方法，例如简单的层次混合和主题模型，可以促进对潜在微生物群落的推断。我们还回顾了一些结合了可视化和不确定性量化的非参数贝叶斯方法。分子微生物生物学和统计学的交叉点是一个令人兴奋的新场所。最后，我们列出了一些重要的开放性问题，这些问题将受益于更谨慎的统计方法开发。