Hefei University of Technology, Hefei, China.
PeerJ. 2023 Aug 16;11:e15862. doi: 10.7717/peerj.15862. eCollection 2023.
Automatic cell type identification has been an urgent task for the rapid development of single-cell RNA-seq techniques. Generally, the current approach for cell type identification is to generate cell clusters by unsupervised clustering and later assign labels to each cell cluster with manual annotation.
Here, we introduce LIDER (celL embeddIng based Deep nEural netwoRk classifier), a deep supervised learning method that combines cell embedding and deep neural network classifier for automatic cell type identification. Based on a stacked denoising autoencoder with a tailored and reconstructed loss function, LIDER identifies cell embedding and predicts cell types with a deep neural network classifier. LIDER was developed upon a stacked denoising autoencoder to learn encoder-decoder structures for identifying cell embedding.
LIDER accurately identifies cell types by using stacked denoising autoencoder. Benchmarking against state-of-the-art methods across eight types of single-cell data, LIDER achieves comparable or even superior enhancement performance. Moreover, LIDER suggests comparable robust to batch effects. Our results show a potential in deep supervised learning for automatic cell type identification of single-cell RNA-seq data. The LIDER codes are available at https://github.com/ShiMGLab/LIDER.
单细胞 RNA-seq 技术的快速发展使得自动细胞类型识别成为一项紧迫的任务。通常，目前的细胞类型识别方法是通过无监督聚类生成细胞簇，然后通过手动注释为每个细胞簇分配标签。
在这里，我们介绍了 LIDER（基于细胞嵌入的深度神经网络分类器），这是一种深度监督学习方法，它将细胞嵌入和深度神经网络分类器结合起来用于自动细胞类型识别。基于具有定制和重构损失函数的堆叠去噪自动编码器，LIDER 识别细胞嵌入并使用深度神经网络分类器预测细胞类型。LIDER 是在堆叠去噪自动编码器的基础上开发的，用于学习用于识别细胞嵌入的编码器-解码器结构。
LIDER 通过堆叠去噪自动编码器准确地识别细胞类型。在八种单细胞数据类型的最新方法基准测试中，LIDER 实现了可比甚至更好的增强性能。此外，LIDER 还表现出对批次效应的稳健性。我们的结果表明，深度监督学习在单细胞 RNA-seq 数据的自动细胞类型识别方面具有潜力。LIDER 代码可在 https://github.com/ShiMGLab/LIDER 上获得。
