德国计算生物学研究所Fabian J. Theis和F. Alexander Wolf研究小组宣布他们的新研究成果，scGen预测单细胞扰动响应。2019年8月2日出版的《自然—方法学》发表了这项成果。

该课题组人员提出scGen (https://github.com/theislab/scgen)，这是一个结合变分自动编码器和潜在空间矢量算法的模型，用于高维度单细胞基因表达数据。研究团队表明，scGen可以精确地模拟细胞类型、研究和物种间的扰动和感染反应。研究人员进一步证明了scGen在学习细胞类型和物种特异性反应的能力，这意味着它捕获了区分反应基因和无反应细胞的特。随着大型健康状态的器官图谱即将问世，可以设想scGen将成为一种实验设计工具，通过在疾病和药物治疗的背景下筛选微扰响应。

据了解，精确模拟细胞对微扰的反应是计算生物学的核心目标。虽然这种模型是建立在特定环境下的统计、机械和机器学习模型的基础上的，但尚未证实该模型对训练数据（样本外）中不存在的现象具备同样的预测和概括能力。

附：英文原文

Title: scGen predicts single-cell perturbation responses

Author: Mohammad Lotfollahi, F. Alexander Wolf, Fabian J. Theis

Issue&Volume:  Volume 16 Issue 8

Abstract: 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.

DOI: 10.1038/s41592-019-0494-8

Source: https://www.nature.com/articles/s41592-019-0494-8