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Cell type identification from single-cell transcriptomic data is a commongoal of single-cell RNA sequencing (scRNAseq) data analysis. Neural networkshave been employed to identify cell types from scRNAseq data with highperformance. However, it requires a large mount of individual cells withaccurate and unbiased annotated types to build the identification models.Unfortunately, labeling the scRNAseq data is cumbersome and time-consuming asit involves manual inspection of marker genes. To overcome this challenge, wepropose a semi-supervised learning model to use unlabeled scRNAseq cells andlimited amount of labeled scRNAseq cells to implement cell identification.Firstly, we transform the scRNAseq cells to "gene sentences", which is inspiredby similarities between natural language system and gene system. Then genes inthese sentences are represented as gene embeddings to reduce data sparsity.With these embeddings, we implement a semi-supervised learning model based onrecurrent convolutional neural networks (RCNN), which includes a sharednetwork, a supervised network and an unsupervised network. The proposed modelis evaluated on macosko2015, a large scale single-cell transcriptomic datasetwith ground truth of individual cell types. It is observed that the proposedmodel is able to achieve encouraging performance by learning on very limitedamount of labeled scRNAseq cells together with a large number of unlabeledscRNAseq cells.

Cell Type Identification from Single-Cell Transcriptomic Data via Semi-supervised Learning