Semi-Supervised Deep Learning for Cell Type Identification From Single-Cell Transcriptomic Data
Semi-Supervised Deep Learning for Cell Type Identification From Single-Cell Transcriptomic Data
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DOI：
10.1109/tcbb.2022.3173587
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发表时间：
2023-03-01
影响因子：
4.5
通讯作者：
Qian, Lijun
中科院分区：
文献类型：
--
作者：
Dong, Xishuang;Chowdhury, Shanta;Qian, Lijun
从单细胞转录组数据中识别细胞类型是单细胞RNA测序（scRNAseq）数据分析的共同目标。深度神经网络已被用于以高性能从scRNAseq数据中识别细胞类型。然而，它需要大量具有准确和无偏注释类型的单个细胞来训练识别模型。不幸的是，标记scRNAseq数据是繁琐和耗时的，因为它涉及标记基因的手动检查。为了克服这一挑战，我们提出了一个半监督学习模型“SemiRNet”，使用未标记的scRNAseq细胞和有限数量的标记的scRNAseq细胞来实现细胞识别。该模型基于递归卷积神经网络（RCNN），它包括一个共享网络，一个监督网络和一个无监督网络。该模型在两个大规模单细胞转录组数据集上进行了评估。据观察，所提出的模型能够通过学习非常有限量的标记的scRNAseq细胞以及大量未标记的scRNAseq细胞来实现令人鼓舞的性能。
Cell type identification from single-cell transcriptomic data is a common goal of single-cell RNA sequencing (scRNAseq) data analysis. Deep neural networks have been employed to identify cell types from scRNAseq data with high performance. However, it requires a large mount of individual cells with accurate and unbiased annotated types to train the identification models. Unfortunately, labeling the scRNAseq data is cumbersome and time-consuming as it involves manual inspection of marker genes. To overcome this challenge, we propose a semi-supervised learning model "SemiRNet" to use unlabeled scRNAseq cells and a limited amount of labeled scRNAseq cells to implement cell identification. The proposed model is based on recurrent convolutional neural networks (RCNN), which includes a shared network, a supervised network and an unsupervised network. The proposed model is evaluated on two large scale single-cell transcriptomic datasets. It is observed that the proposed model is able to achieve encouraging performance by learning on the very limited amount of labeled scRNAseq cells together with a large number of unlabeled scRNAseq cells.