Tang Zijia, Zhou Minghao, Zhang Kai, Song Qianqian
Trinity College, Duke University, Durham, NC, USA.
Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, USA.
J Adv Res. 2024 Oct 31. doi: 10.1016/j.jare.2024.10.035.
Traditional methods for obtaining cellular responses after perturbation are usually labor-intensive and costly, especially when working with multiple different experimental conditions. Therefore, accurate prediction of cellular responses to perturbations is of great importance in computational biology. Existing methodologies, such as graph-based approaches, vector arithmetic, and neural networks, either mix perturbation-related variances with cell-type-specific patterns or implicitly distinguish them within black-box models.
This study aims to introduce and demonstrate a novel framework, scPerb, which explicitly extracts perturbation-related variances and transfers them from unperturbed to perturbed cells to accurately predict the effect of perturbation in single-cell level.
scPerb utilizes a style transfer strategy by incorporating a style encoder into the architecture of a variational autoencoder. The style encoder captures the differences in latent representations between unperturbed and perturbed cells, enabling accurate prediction of post-perturbation gene expression data.
Comprehensive comparisons with existing methods demonstrate that scPerb delivers improved performance and higher accuracy in predicting cellular responses to perturbations. Notably, scPerb outperforms other methods across multiple datasets, achieving superior R values of 0.98, 0.98, and 0.96 on three benchmarking datasets.
scPerb offers a significant advancement in predicting cellular responses by effectively separating and transferring perturbation-related variances. This framework not only enhances prediction accuracy but also provides a robust tool for computational biology, addressing the limitations of current methodologies.
传统的在扰动后获取细胞反应的方法通常劳动强度大且成本高，特别是在处理多种不同实验条件时。因此，在计算生物学中准确预测细胞对扰动的反应非常重要。现有的方法，如图基方法、向量算法和神经网络，要么将与扰动相关的方差与细胞类型特异性模式混合，要么在黑箱模型中隐式区分它们。
本研究旨在介绍并演示一种新颖的框架scPerb，该框架明确提取与扰动相关的方差，并将其从未受扰动的细胞转移到受扰动的细胞，以在单细胞水平上准确预测扰动的效果。
scPerb通过将风格编码器纳入变分自编码器的架构来利用风格迁移策略。风格编码器捕获未受扰动和受扰动细胞之间潜在表示的差异，从而能够准确预测扰动后的基因表达数据。
与现有方法的全面比较表明，scPerb在预测细胞对扰动的反应方面具有更好的性能和更高的准确性。值得注意的是，scPerb在多个数据集上优于其他方法，在三个基准数据集上分别实现了0.98、0.98和0.96的卓越R值。
scPerb通过有效分离和转移与扰动相关的方差，在预测细胞反应方面取得了重大进展。该框架不仅提高了预测准确性，还为计算生物学提供了一个强大的工具，克服了当前方法的局限性。
