Cell Type Identification from Single-Cell Transcriptomic Data via Gene Embedding

摘要

Single-cell RNA sequencing (scRNAseq) enables the profiling of the transcriptomes of individual cells, thus characterizing the heterogeneity of biological samples since scRNAseq experiments are able to yield high volumes of data. Analyzing scRNAseq data will be beneficial for obtaining knowledge on cancer drug resistance, gene regulation in embryonic development, and mechanisms of stem cell differentiation and reprogramming. One common goal of scRNAseq data analytics is to identify the cell type of each individual cell that has been profiled. However, data sparsity is the main challenge due to limitations of current single-cell RNA sequencing techniques. In this paper, a novel method of representing the genes as gene embeddings is proposed to reduce data sparsity of scRNAseq data for cell type identification, which is inspired by similarities between gene system and natural language system. It contains two steps: 1) transform gene sequences into gene sentences by ranking genes in terms of their expression values; 2) employ the word2vec technique to learn gene embeddings on these gene sentences. Then we build three deep learning models, namely RNNs, Attention RNNs, and Bi-directional LSTM RNNs, for cell type classification. The proposed method is evaluated on macosko2015, a large scale scRNAseq dataset with ground truth of individual cell types. Experimental results show that the proposed method performs effectively and efficiently on identifying cell types on scRNAseq data, and it can achieve promising performance even learning on limited number of genes.