基于测序的微生物群落分析方法易受污染影响，这可能掩盖生物信号或产生虚假信号。通常使用控制组的体外去污染方法，但不能充分利用在多个样本之间共享的信息，并且不能处理只部分起源于污染或生物材料泄漏到控制组中的分类。在这里，我们介绍了一种名为“微生物群落污染来源追踪（SCRuB）”的概率体外去污染方法，它将多个样本和控制之间共享的信息精确地识别和去除污染。我们在多个数据驱动的模拟和实验中验证了SCRuB的准确性，包括人为污染，并证明它比最先进的方法平均表现提高了15-20倍。我们展示了SCRuB在多个生态系统、数据类型和测序深度中的稳健性。通过展示其在微生物组研究中的适用性，SCRuB有助于提高宿主表型的预测，特别是使用经过去污染的肿瘤微生物组数据预测黑色素瘤患者的治疗反应。©2023.作者(们)，在Springer Nature America, Inc.的独家许可下。

Sequencing-based approaches for the analysis of microbial communities are susceptible to contamination, which could mask biological signals or generate artifactual ones. Methods for in silico decontamination using controls are routinely used, but do not make optimal use of information shared across samples and cannot handle taxa that only partially originate in contamination or leakage of biological material into controls. Here we present Source tracking for Contamination Removal in microBiomes (SCRuB), a probabilistic in silico decontamination method that incorporates shared information across multiple samples and controls to precisely identify and remove contamination. We validate the accuracy of SCRuB in multiple data-driven simulations and experiments, including induced contamination, and demonstrate that it outperforms state-of-the-art methods by an average of 15-20 times. We showcase the robustness of SCRuB across multiple ecosystems, data types and sequencing depths. Demonstrating its applicability to microbiome research, SCRuB facilitates improved predictions of host phenotypes, most notably the prediction of treatment response in melanoma patients using decontaminated tumor microbiome data.© 2023. The Author(s), under exclusive licence to Springer Nature America, Inc.