The recently completed second phase of the Human Microbiome Project has highlighted the relationship between dynamic changes in the microbiome and disease, motivating new microbiome study designs based on longitudinal sampling. Yet, analysis of such data is hindered by presence of technical noise, high dimensionality, and data sparsity. Here, we introduce LUMINATE (longitudinal microbiome inference and zero detection), a fast and accurate method for inferring relative abundances from noisy read count data. We demonstrate that LUMINATE is orders of magnitude faster than current approaches, with better or similar accuracy. We further show that LUMINATE can accurately distinguish biological zeros, when a taxon is absent from the community, from technical zeros, when a taxon is below the detection threshold. We conclude by demonstrating the utility of LUMINATE on a real dataset, showing that LUMINATE smooths trajectories observed from noisy data. LUMINATE is freely available from https://github.com/tyjo/luminate.

最近完成的人类微生物组计划第二阶段强调了微生物组动态变化与疾病之间的关系，激发了基于纵向采样的新微生物组研究设计。然而，技术噪声、高维和数据稀疏性的存在阻碍了对此类数据的分析。在这里，我们介绍了 LUMINATE（纵向微生物组推断和零检测），这是一种从嘈杂的读取计数数据中推断相对丰度的快速准确的方法。我们证明 LUMINATE 比当前方法快几个数量级，具有更好或相似的精度。我们进一步表明，当分类单元不存在于群落中时，LUMINATE 可以准确地区分生物零点，以及当分类单元低于检测阈值时的技术零点。最后，我们展示了 LUMINATE 在真实数据集上的实用性，表明 LUMINATE 可以平滑从噪声数据中观察到的轨迹。LUMINATE 可从 https://github.com/tyjo/luminate 免费获得。