Single-cell technologies offer unprecedented opportunities to dissect gene regulatory mechanisms in context-specific ways. Although there are computational methods for extracting gene regulatory relationships from scRNA-seq and scATAC-seq data, the data integration problem, essential for accurate cell type identification, has been mostly treated as a standalone challenge. Here we present scTIE, a unified method that integrates temporal multimodal data and infers regulatory relationships predictive of cellular state changes. scTIE uses an autoencoder to embed cells from all time points into a common space by using iterative optimal transport, followed by extracting interpretable information to predict cell trajectories. Using a variety of synthetic and real temporal multimodal data sets, we show scTIE achieves effective data integration while preserving more biological signals than existing methods, particularly in the presence of batch effects and noise. Furthermore, on the exemplar multiome data set we generated from differentiating mouse embryonic stem cells over time, we show scTIE captures regulatory elements highly predictive of cell transition probabilities, providing new potentials to understand the regulatory landscape driving developmental processes.

中文翻译：

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使用单细胞时间多模态数据和 scTIE 进行数据整合和基因调控推断

单细胞技术提供了前所未有的机会，可以根据具体情况剖析基因调控机制。尽管有一些计算方法可以从 scRNA-seq 和 scATAC-seq 数据中提取基因调控关系，但对于准确的细胞类型识别至关重要的数据集成问题大多被视为一个独立的挑战。在这里，我们提出了 scTIE，这是一种整合时间多模态数据并推断预测细胞状态变化的调控关系的统一方法。 scTIE 使用自动编码器通过迭代最优传输将所有时间点的细胞嵌入到公共空间中，然后提取可解释的信息来预测细胞轨迹。使用各种合成和实时多模态数据集，我们表明 scTIE 实现了有效的数据集成，同时比现有方法保留了更多的生物信号，特别是在存在批次效应和噪声的情况下。此外，在我们通过分化小鼠胚胎干细胞随时间推移生成的示例性多组数据集上，我们表明 scTIE 捕获了高度预测细胞转变概率的调控元件，为理解驱动发育过程的调控景观提供了新的潜力。