将基因本体论与深度神经网络相结合，以增强单细胞 RNA-Seq 数据的聚类。
Combining gene ontology with deep neural networks to enhance the clustering of single cell RNA-Seq data.
机构信息
School of Computer Science, Northwestern Polytechnical University, Xi'an, 710072, China.
Key Laboratory of Big Data Storage and Management, Northwestern Polytechnical University, Ministry of Industry and Information Technology, Xi'an, 710072, China.
出版信息
BMC Bioinformatics. 2019 Jun 10;20(Suppl 8):284. doi: 10.1186/s12859-019-2769-6.
BACKGROUND
Single cell RNA sequencing (scRNA-seq) is applied to assay the individual transcriptomes of large numbers of cells. The gene expression at single-cell level provides an opportunity for better understanding of cell function and new discoveries in biomedical areas. To ensure that the single-cell based gene expression data are interpreted appropriately, it is crucial to develop new computational methods.
RESULTS
In this article, we try to re-construct a neural network based on Gene Ontology (GO) for dimension reduction of scRNA-seq data. By integrating GO with both unsupervised and supervised models, two novel methods are proposed, named GOAE (Gene Ontology AutoEncoder) and GONN (Gene Ontology Neural Network) respectively.
CONCLUSIONS
The evaluation results show that the proposed models outperform some state-of-the-art dimensionality reduction approaches. Furthermore, incorporating with GO, we provide an opportunity to interpret the underlying biological mechanism behind the neural network-based model.
背景
单细胞 RNA 测序（scRNA-seq）用于检测大量细胞的个体转录组。单细胞水平的基因表达为更好地理解细胞功能和生物医学领域的新发现提供了机会。为了确保基于单细胞的基因表达数据得到适当的解释，开发新的计算方法至关重要。
结果
在本文中，我们尝试基于基因本体论（GO）重新构建神经网络，以实现 scRNA-seq 数据的降维。通过将 GO 与无监督和监督模型相结合，我们分别提出了两种新方法，分别命名为 GOAE（基因本体论自动编码器）和 GONN（基因本体论神经网络）。
结论
评估结果表明，所提出的模型优于一些最先进的降维方法。此外，通过与 GO 结合，我们为解释基于神经网络的模型背后的潜在生物学机制提供了机会。
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