Wu Yahao, Liu Jing, Xiao Yanni, Zhang Shuqin, Li Limin
School of Mathematics and Statistics, Xi'an Jiaotong University, No. 28 Xianning West Road, Xi'an, Shaanxi 710049, China.
School of Mathematical Sciences, Center for Applied Mathematics, Research Institute of Intelligent Complex Systems, and Shanghai Key Laboratory for Contemporary Applied Mathematics, Fudan University, 220 Handan Road, 200433 Shanghai, China.
Brief Bioinform. 2025 Mar 4;26(2). doi: 10.1093/bib/bbaf126.
With the rapid advances in single-cell sequencing technology, it is now feasible to conduct in-depth genetic analysis in individual cells. Study on the dynamics of single cells in response to perturbations is of great significance for understanding the functions and behaviors of living organisms. However, the acquisition of post-perturbation cellular states via biological experiments is frequently cost-prohibitive. Predicting the single-cell perturbation responses poses a critical challenge in the field of computational biology. In this work, we propose a novel deep learning method called coupled variational autoencoders (CoupleVAE), devised to predict the postperturbation single-cell RNA-Seq data. CoupleVAE is composed of two coupled VAEs connected by a coupler, initially extracting latent features for controlled and perturbed cells via two encoders, subsequently engaging in mutual translation within the latent space through two nonlinear mappings via a coupler, and ultimately generating controlled and perturbed data by two separate decoders to process the encoded and translated features. CoupleVAE facilitates a more intricate state transformation of single cells within the latent space. Experiments in three real datasets on infection, stimulation and cross-species prediction show that CoupleVAE surpasses the existing comparative models in effectively predicting single-cell RNA-seq data for perturbed cells, achieving superior accuracy.
随着单细胞测序技术的迅速发展，现在对单个细胞进行深入的基因分析已成为可能。研究单细胞对扰动的动态响应对于理解生物体的功能和行为具有重要意义。然而，通过生物学实验获取扰动后的细胞状态通常成本过高。预测单细胞扰动响应是计算生物学领域的一项关键挑战。在这项工作中，我们提出了一种名为耦合变分自编码器（CoupleVAE）的新型深度学习方法，旨在预测扰动后的单细胞RNA测序数据。CoupleVAE由通过耦合器连接的两个耦合变分自编码器组成，首先通过两个编码器为对照细胞和扰动细胞提取潜在特征，随后通过耦合器经由两个非线性映射在潜在空间内进行相互转换，最后由两个单独的解码器生成对照数据和扰动数据以处理编码和转换后的特征。CoupleVAE有助于在潜在空间内实现单细胞更复杂的状态转换。在感染、刺激和跨物种预测这三个真实数据集上进行的实验表明，CoupleVAE在有效预测扰动细胞的单细胞RNA测序数据方面优于现有的对比模型，具有更高的准确性。
