Single-cell classification using graph convolutional networks.
Single-cell classification using graph convolutional networks.
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DOI：
10.1186/s12859-021-04278-2
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发表时间：
2021-07-08
影响因子：
3
通讯作者：
Nabavi S
中科院分区：
文献类型：
--
作者：
Wang T;Bai J;Nabavi S
分析单细胞RNA测序（scRNAseq）数据在理解生物学和生物医学研究中的内在和外在细胞过程中起着重要作用。这一领域的一项重要工作是识别细胞类型。随着大量单细胞测序数据的可用性和越来越多的细胞类型的发现，将细胞分类为已知的细胞类型已经成为当今的优先事项。已经引入了几种方法来利用基因表达数据对细胞进行分类。然而，结合生物基因相互作用网络已被证明是有价值的细胞分类程序。在这项研究中，我们提出了一个多模式端到端深度学习模型，名为sigGCN，用于细胞分类，它结合了图卷积网络（GCN）和神经网络来利用基因相互作用网络。我们使用标准的分类指标来评估所提出的方法的性能上的数据集内分类和跨数据集分类。我们比较了所提出的方法与现有的细胞分类工具和传统的机器学习分类方法的性能。实验结果表明，该方法在分类精度和F1得分方面优于其他常用方法。这项研究表明，使用GCN方法整合有关基因与基因表达相互作用的先验知识可以提取有效的特征，从而提高细胞分类的性能。在线版本包含补充材料，可通过10.1186/s12859-021-04278-2获得。
Analyzing single-cell RNA sequencing (scRNAseq) data plays an important role in understanding the intrinsic and extrinsic cellular processes in biological and biomedical research. One significant effort in this area is the identification of cell types. With the availability of a huge amount of single cell sequencing data and discovering more and more cell types, classifying cells into known cell types has become a priority nowadays. Several methods have been introduced to classify cells utilizing gene expression data. However, incorporating biological gene interaction networks has been proved valuable in cell classification procedures. In this study, we propose a multimodal end-to-end deep learning model, named sigGCN, for cell classification that combines a graph convolutional network (GCN) and a neural network to exploit gene interaction networks. We used standard classification metrics to evaluate the performance of the proposed method on the within-dataset classification and the cross-dataset classification. We compared the performance of the proposed method with those of the existing cell classification tools and traditional machine learning classification methods. Results indicate that the proposed method outperforms other commonly used methods in terms of classification accuracy and F1 scores. This study shows that the integration of prior knowledge about gene interactions with gene expressions using GCN methodologies can extract effective features improving the performance of cell classification. The online version contains supplementary material available at 10.1186/s12859-021-04278-2.