Realistic in silico generation and augmentation of single-cell RNA-seq data using generative adversarial networks
Realistic in silico generation and augmentation of single-cell RNA-seq data using generative adversarial networks
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DOI：
10.1038/s41467-019-14018-z
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发表时间：
2020-01-09
影响因子：
16.6
通讯作者：
Bonn, Stefan
中科院分区：
文献类型：
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作者：
Marouf, Mohamed;Machart, Pierre;Bonn, Stefan
生物医学研究的一个基本问题是可用的观察数量较少，这主要是由于缺乏可用的生物样本、高昂的成本或伦理原因。使用计算机样本增强少量真实观察结果可能会产生更稳健的分析结果和更高的重现率。在这里，我们建议使用条件单细胞生成对抗神经网络（cscGAN）来实际生成单细胞 RNA-seq 数据。 cscGAN 从复杂的多细胞类型样本中学习非线性基因-基因依赖性，并使用此信息生成定义类型的真实细胞。使用 cscGAN 生成的细胞增强稀疏细胞群可以改善下游分析，例如标记基因的检测、分类器的鲁棒性和可靠性、新分析算法的评估，并可能减少动物实验的数量和成本。 cscGAN 在质量上优于现有的单细胞 RNA-seq 数据生成方法，并为其他生物医学数据类型的实际生成和增强带来了巨大希望。
A fundamental problem in biomedical research is the low number of observations available, mostly due to a lack of available biosamples, prohibitive costs, or ethical reasons. Augmenting few real observations with generated in silico samples could lead to more robust analysis results and a higher reproducibility rate. Here, we propose the use of conditional single-cell generative adversarial neural networks (cscGAN) for the realistic generation of single-cell RNA-seq data. cscGAN learns non-linear gene-gene dependencies from complex, multiple cell type samples and uses this information to generate realistic cells of defined types. Augmenting sparse cell populations with cscGAN generated cells improves downstream analyses such as the detection of marker genes, the robustness and reliability of classifiers, the assessment of novel analysis algorithms, and might reduce the number of animal experiments and costs in consequence. cscGAN outperforms existing methods for single-cell RNA-seq data generation in quality and hold great promise for the realistic generation and augmentation of other biomedical data types.