标题： 无监督检测和新兴SARS-CoV-2变种的适应性估计。 应用于污水样本（ANRS0160） 显示英文标题

标题： Unsupervised detection and fitness estimation of emerging SARS-CoV-2 variants. Application to wastewater samples (ANRS0160)

摘要： 在SARS-CoV-2大流行期间反复出现的变异株凸显了收集纵向基因组数据并开发基于时间序列分析的统计方法以早期检测新威胁谱系和估计其适应度的紧迫性。 大多数模型研究特定谱系随时间的流行程度，并需要将序列预先分类为谱系。 这一过程容易导致延迟和偏差。 最近，一些作者研究了突变随时间的流行程度，并采用了替代聚类方法，避免了特定谱系分类。 然而，上述大多数方法要么是非参数的，要么不适合池化数据，例如废水样本。 在此背景下，我们提出了一种替代的无监督方法，根据突变频率轨迹对突变进行聚类，并从池化突变流行度的时间序列数据中估计群体适应度。 我们的模型是观测计数数据和潜在群体分配的混合，并使用期望最大化算法进行模型选择和参数估计。 我们将该方法应用于2020年10月至2021年4月期间从法国污水处理厂收集的SARS-CoV-2测序数据时间序列，结果显示其能够无偏地根据突变属于B.1.160、Alpha、Beta、B.1.177变异株的概率对突变进行分组，并且每组的选择系数估计值与Nextstrain报告的法国病毒动态一致。 此外，我们的方法早在监督方法（跟踪特定突变随时间的变化）检测到Alpha变异株之前就将其识别为威胁，值得注意的是，由于是无监督的，它不需要任何关于突变集合的先验信息。 显示英文摘要

摘要： Repeated waves of emerging variants during the SARS-CoV-2 pandemics have highlighted the urge of collecting longitudinal genomic data and developing statistical methods based on time series analyses for detecting new threatening lineages and estimating their fitness early in time. Most models study the evolution of the prevalence of particular lineages over time and require a prior classification of sequences into lineages. Such process is prone to induce delays and bias. More recently, few authors studied the evolution of the prevalence of mutations over time with alternative clustering approaches, avoiding specific lineage classification. Most of the aforementioned methods are however either non parametric or unsuited to pooled data characterizing, for instance, wastewater samples. In this context, we propose an alternative unsupervised method for clustering mutations according to their frequency trajectory over time and estimating group fitness from time series of pooled mutation prevalence data. Our model is a mixture of observed count data and latent group assignment and we use the expectation-maximization algorithm for model selection and parameter estimation. The application of our method to time series of SARS-CoV-2 sequencing data collected from wastewater treatment plants in France from October 2020 to April 2021 shows its ability to agnostically group mutations according to their probability of belonging to B.1.160, Alpha, Beta, B.1.177 variants with selection coefficient estimates per group in coherence with the viral dynamics in France reported by Nextstrain. Moreover, our method detected the Alpha variant as threatening as early as supervised methods (which track specific mutations over time) with the noticeable difference that, since unsupervised, it does not require any prior information on the set of mutations.