Xing Hanwen, Yau Christopher
Nuffield Department for Women's and Reproductive Health, University of Oxford, Oxford, UK.
Health Data Research UK, London, UK.
Nat Commun. 2025 Jul 1;16(1):5423. doi: 10.1038/s41467-025-61165-7.
Single-cell RNA sequencing and CRISPR screening enable high-throughput analysis of genetic perturbations at single-cell resolution. Understanding combinatorial perturbation effects is essential but challenging due to data sparsity and complex biological mechanisms. We present GPerturb, a Gaussian process-based sparse perturbation regression model designed to estimate gene-level perturbation effects. GPerturb employs an additive structure to separate signal from noise and captures sparse, interpretable effects from both discrete and continuous responses. It also provides uncertainty estimates for the presence and strength of perturbation effects on individual genes. We demonstrate the use GPerturb on both simulated and real-world datasets, characterising its competitive performance with current state-of-the-art methods. Furthermore, the model reveals meaningful gene-perturbation interactions and identifies effects consistent with known biology. GPerturb offers a novel approach for uncovering complex dependencies between gene expression and perturbations and advancing our understanding of gene regulation at the single-cell level.
单细胞RNA测序和CRISPR筛选能够在单细胞分辨率下对基因扰动进行高通量分析。由于数据稀疏性和复杂的生物学机制，理解组合扰动效应至关重要但具有挑战性。我们提出了GPerturb，这是一种基于高斯过程的稀疏扰动回归模型，旨在估计基因水平的扰动效应。GPerturb采用加法结构将信号与噪声分离，并从离散和连续响应中捕获稀疏、可解释的效应。它还提供了对单个基因扰动效应的存在和强度的不确定性估计。我们展示了GPerturb在模拟数据集和真实世界数据集上的应用，表征了其与当前最先进方法相比的竞争性能。此外，该模型揭示了有意义的基因-扰动相互作用，并识别出与已知生物学一致的效应。GPerturb为揭示基因表达与扰动之间的复杂依赖性以及推进我们对单细胞水平基因调控的理解提供了一种新方法。
