scGen predicts single-cell perturbation responses
scGen predicts single-cell perturbation responses
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DOI：
10.1038/s41592-019-0494-8
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发表时间：
2019-08-01
期刊：
影响因子：
48
通讯作者：
Theis, Fabian J.
中科院分区：
文献类型：
--
作者：
Lotfollahi, Mohammad;Wolf, F. Alexander;Theis, Fabian J.
精确建模细胞对扰动的反应是计算生物学的中心目标。虽然这种建模是基于特定环境中的统计、机械和机器学习模型，但尚未证明对训练数据（样本外）中不存在的现象进行预测的泛化。在这里，我们提出了scGen（https：//github.com/theislab/scgen），这是一个结合了变分自编码器和潜在空间向量算法的模型，用于高维单细胞基因表达数据。我们表明，scGen准确地模拟了细胞类型，研究和物种之间的细胞扰动和感染反应。特别是，我们证明了scGen学习细胞类型和物种特异性反应，这意味着它捕获了区分响应与非响应基因和细胞的特征。随着健康状态下大规模器官图谱的即将推出，我们设想scGen通过在疾病和药物治疗的背景下对扰动反应进行计算机筛选而成为实验设计的工具。
Accurately modeling cellular response to perturbations is a central goal of computational biology. While such modeling has been based on statistical, mechanistic and machine learning models in specific settings, no generalization of predictions to phenomena absent from training data (out-of-sample) has yet been demonstrated. Here, we present scGen (https://github.com/theislab/scgen), a model combining variational autoencoders and latent space vector arithmetics for high-dimensional single-cell gene expression data. We show that scGen accurately models perturbation and infection response of cells across cell types, studies and species. In particular, we demonstrate that scGen learns cell-type and species-specific responses implying that it captures features that distinguish responding from non-responding genes and cells. With the upcoming availability of large-scale atlases of organs in a healthy state, we envision scGen to become a tool for experimental design through in silico screening of perturbation response in the context of disease and drug treatment.