Introduction

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.

Objectives

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.

Methods

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.

Results

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 R² values of 0.98, 0.98, and 0.96 on three benchmarking datasets.

Conclusion

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.

中文翻译：

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scPerb：通过基于样式迁移的变分自动编码器预测单单元扰动

 介绍

扰动后获得细胞反应的传统方法通常是劳动密集型且成本高昂的，尤其是在处理多种不同的实验条件时。因此，准确预测细胞对扰动的反应在计算生物学中非常重要。现有的方法，例如基于图的方法、向量算术和神经网络，要么将与扰动相关的方差与单元类型特定的模式混合，要么在黑盒模型中隐式区分它们。

 目标

本研究旨在介绍和演示一种新的框架 scPerb，它显式提取与扰动相关的方差，并将它们从未扰动的细胞转移到受扰动的细胞，以准确预测扰动在单细胞水平上的影响。

 方法

scPerb 通过将样式编码器合并到变分自动编码器的架构中来利用样式传输策略。样式编码器捕获未扰动细胞和受扰动细胞之间潜在表征的差异，从而能够准确预测扰动后的基因表达数据。

 结果

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 在多个数据集中的性能优于其他方法，在三个基准测试数据集上实现了 0.98、0.98 和 0.96 的优异 R² 值。

 结论

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的准确性，还为计算生物学提供了强大的工具，解决了当前方法的局限性。