Department of Computer Science, Brown University, Providence, RI 02906, United States.
Center for Computational Molecular Biology, Brown University, Providence, RI 02912, United States.
Bioinformatics. 2024 Sep 1;40(Suppl 2):ii146-ii154. doi: 10.1093/bioinformatics/btae393.
Measurement of single-cell gene expression at different timepoints enables the study of cell development. However, due to the resource constraints and technical challenges associated with the single-cell experiments, researchers can only profile gene expression at discrete and sparsely sampled timepoints. This missing timepoint information impedes downstream cell developmental analyses. We propose scNODE, an end-to-end deep learning model that can predict in silico single-cell gene expression at unobserved timepoints. scNODE integrates a variational autoencoder with neural ordinary differential equations to predict gene expression using a continuous and nonlinear latent space. Importantly, we incorporate a dynamic regularization term to learn a latent space that is robust against distribution shifts when predicting single-cell gene expression at unobserved timepoints. Our evaluations on three real-world scRNA-seq datasets show that scNODE achieves higher predictive performance than state-of-the-art methods. We further demonstrate that scNODE's predictions help cell trajectory inference under the missing timepoint paradigm and the learned latent space is useful for in silico perturbation analysis of relevant genes along a developmental cell path.
The data and code are publicly available at https://github.com/rsinghlab/scNODE.
在不同时间点测量单细胞基因表达水平能够帮助研究细胞的发育过程。然而，由于单细胞实验相关的资源限制和技术挑战，研究人员只能在离散且稀疏的时间点上进行基因表达谱分析。这些缺失的时间点信息阻碍了下游的细胞发育分析。我们提出了 scNODE，这是一种端到端的深度学习模型，可以预测未观察到的时间点的单细胞基因表达情况。scNODE 将变分自动编码器与神经常微分方程相结合，使用连续非线性潜在空间来预测基因表达。重要的是，我们引入了一个动态正则化项，以学习一个潜在空间，该空间在预测未观察到的时间点的单细胞基因表达时能够抵抗分布偏移。我们在三个真实 scRNA-seq 数据集上的评估表明，scNODE 比最先进的方法具有更高的预测性能。我们进一步证明，scNODE 的预测有助于在缺失时间点范式下进行细胞轨迹推断，并且学习到的潜在空间可用于沿着发育细胞路径对相关基因进行虚拟扰动分析。
数据和代码可在 https://github.com/rsinghlab/scNODE 上公开获取。
