Seki Misato, Zhang Yao-Zhong, Imoto Seiya
Division of Health Medical Intelligence, Human Genome Center, The Institute of Medical Science, The University of Tokyo, Tokyo 108-8639, Japan.
Bioinform Adv. 2025 Jul 28;5(1):vbaf181. doi: 10.1093/bioadv/vbaf181. eCollection 2025.
The gut microbiota interacts closely with the host, playing crucial roles in maintaining health. Analysing time-series genomic data enables the investigation of dynamic microbiota changes. However, missing values create significant analytical challenges.
We propose a microbiome imputation framework based on a conditional score-based diffusion model, tailored to microbiome data by incorporating phylogenetic convolutional layers. Our method effectively reduces mean absolute error across various missing data ratios for both 16S rRNA and whole-genome shotgun profiles. The imputed datasets enhance downstream predictive tasks, achieving area under the curve scores that exceed or are comparable with those of the existing methods. To further improve the performance, we embedded host metadata into the model using a tabular encoding approach, which yielded additional improvements particularly under higher missing ratios. Our findings underscore the potential of the diffusion model for processing time-series microbiome data with missing values.
Related codes and dataset can be found at: https://github.com/misatoseki/metag_time_impute_phylo.git.
肠道微生物群与宿主密切相互作用，在维持健康方面发挥着关键作用。分析时间序列基因组数据有助于研究微生物群的动态变化。然而，缺失值带来了重大的分析挑战。
我们提出了一种基于条件分数的扩散模型的微生物组插补框架，通过纳入系统发育卷积层对微生物组数据进行了定制。我们的方法有效降低了16S rRNA和全基因组鸟枪法图谱在各种缺失数据比例下的平均绝对误差。插补后的数据集增强了下游预测任务，获得的曲线下面积分数超过或与现有方法相当。为了进一步提高性能，我们使用表格编码方法将宿主元数据嵌入模型，这在较高缺失比例下尤其带来了额外的改进。我们的研究结果强调了扩散模型在处理具有缺失值的时间序列微生物组数据方面的潜力。
相关代码和数据集可在以下网址找到：https://github.com/misatoseki/metag_time_impute_phylo.git。
