Brain Mind Institute, Faculty of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
Michael Smith Laboratories, University of British Columbia, Vancouver, British Columbia, Canada.
Nat Biotechnol. 2021 Jan;39(1):30-34. doi: 10.1038/s41587-020-0605-1. Epub 2020 Jul 20.
We present Augur, a method to prioritize the cell types most responsive to biological perturbations in single-cell data. Augur employs a machine-learning framework to quantify the separability of perturbed and unperturbed cells within a high-dimensional space. We validate our method on single-cell RNA sequencing, chromatin accessibility and imaging transcriptomics datasets, and show that Augur outperforms existing methods based on differential gene expression. Augur identified the neural circuits restoring locomotion in mice following spinal cord neurostimulation.
我们提出了 Augur 方法，用于在单细胞数据中优先考虑对生物扰动反应最敏感的细胞类型。Augur 采用机器学习框架在高维空间中量化受扰和未受扰细胞的可分离性。我们在单细胞 RNA 测序、染色质可及性和成像转录组学数据集上验证了我们的方法，并表明 Augur 优于基于差异基因表达的现有方法。Augur 确定了在脊髓神经刺激后恢复小鼠运动的神经回路。
