MulCNN: An efficient and accurate deep learning method based on gene embedding for cell type identification in single-cell RNA-seq data.
MulCNN: An efficient and accurate deep learning method based on gene embedding for cell type identification in single-cell RNA-seq data.
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DOI：
10.3389/fgene.2023.1179859
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发表时间：
2023
影响因子：
3.7
通讯作者：
Song, Tao
中科院分区：
文献类型：
--
作者：
Jiao, Linfang;Ren, Yongqi;Wang, Lulu;Gao, Changnan;Wang, Shuang;Song, Tao
关键词：
单细胞测序研究的进步通过分析单细胞转录组和基因组彻底改变了我们对细胞异质性和功能多样性的理解。单细胞RNA测序（scRNA-seq）分析的关键步骤是识别细胞类型。然而，scRNA-seq数据通常是高维和稀疏的，手工细胞类型鉴定可能耗时、主观且缺乏可重复性。因此，分析scRNA-seq数据仍然是一个计算挑战。随着注释良好的scRNA-seq数据集的不断增加，利用这些信息来帮助细胞类型鉴定的先进方法正在出现。深度学习神经网络在分析单细胞数据方面具有巨大的潜力。本文提出了MulCNN，这是一种使用独特的细胞类型特异性基因表达特征提取方法的多级卷积神经网络。该方法通过多尺度卷积提取关键特征，同时滤波噪声。利用不同物种的数据集进行的广泛测试以及与流行的分类方法的比较表明，MulCNN具有出色的性能，为scRNA-seq分析提供了一个新的、可扩展的方向。
Advancements in single-cell sequencing research have revolutionized our understanding of cellular heterogeneity and functional diversity through the analysis of single-cell transcriptomes and genomes. A crucial step in single-cell RNA sequencing (scRNA-seq) analysis is identifying cell types. However, scRNA-seq data are often high dimensional and sparse, and manual cell type identification can be time-consuming, subjective, and lack reproducibility. Consequently, analyzing scRNA-seq data remains a computational challenge. With the increasing availability of well-annotated scRNA-seq datasets, advanced methods are emerging to aid in cell type identification by leveraging this information. Deep learning neural networks have great potential for analyzing single-cell data. This paper proposes MulCNN, a multi-level convolutional neural network that uses a unique cell type-specific gene expression feature extraction method. This method extracts critical features through multi-scale convolution while filtering noise. Extensive testing using datasets from various species and comparisons with popular classification methods show that MulCNN has outstanding performance and offers a new and scalable direction for scRNA-seq analysis.
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