Adversarial dense graph convolutional networks for single-cell classification

Kangwei Wang, Zhengwei Li, Zhu Hong You, Pengyong Han, Ru Nie

科研成果: 期刊稿件文章同行评审

10 引用 (Scopus)

摘要

Motivation: In single-cell transcriptomics applications, effective identification of cell types in multicellular organisms and in-depth study of the relationships between genes has become one of the main goals of bioinformatics research. However, data heterogeneity and random noise pose significant difficulties for scRNA-seq data analysis. Results: We have proposed an adversarial dense graph convolutional network architecture for single-cell classification. Specifically, to enhance the representation of higher-order features and the organic combination between features, dense connectivity mechanism and attention-based feature aggregation are introduced for feature learning in convolutional neural networks. To preserve the features of the original data, we use a feature reconstruction module to assist the goal of single-cell classification. In addition, HNNVAT uses virtual adversarial training to improve the generalization and robustness. Experimental results show that our model outperforms the existing classical methods in terms of classification accuracy on benchmark datasets.

源语言英语
文章编号btad043
期刊Bioinformatics
39
2
DOI
出版状态已出版 - 1 2月 2023

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