Abstract
Single-cell RNA sequencing(scRNA-seq) technology has boomed in the past decade which makes it possible to study biological problems at the resolution of cellular-level. Currently, the research mainly focuses on exploring the cellular heterogeneity, involving studies about identifying cell type identification, cell lineage tracing, spatial model reconstruction of complex organizations, etc. Clustering analysis is always the most effective way in grouping single cells in previous studies. However, existing scRNA-seq clustering methods separate pre-processing and clustering tasks that complicated the problem. In addition, the emergence of big data further limits the traditional clustering algorithms' application on scRNA-seq data. Therefore, developing novel clustering methods and improving clustering accuracy for growing scRNA-seq data is a continuous task. In this paper, we propose a highly integrated Deep Subspace Clustering Denoise Network named DSCD, which integrates denoise, dimension reduction and clustering in a unified framework. Based on the neural network architecture of autoencoder, DSCD discovers the low dimensional latent structure within scRNA-seq data from the compressed representation. Furthermore, we add a novel self-expressive denoise layer to learning the global relationships between single cells, which is the main innovation of DSCD. Experimental results on the synthetic data demonstrate the effectiveness of the novel denoise layer. From the clustering results on 5 real scRNA-seq datasets, we find that DSCD outperforms the related subspace clustering algorithms and state of the art methods. In conclusion, DSCD responds well to the rapidly increasing scRNA-seq data scale, greatly reduces human interference in dimension reduction and handles the noisy scRNA-seq data in proper way thus obtain a higher clustering accuracy.
Highlights
Traditional RNA sequencing (RNA-seq) technology measures the average expression of genes across many cells of different cell types, which may mask the real functional capacities of each cell type and hinder the study of cell heterogeneity
The emergence of scRNA-seq technology overcame the limitations in traditional bulk RNA-seq, which enables researchers to investigate the cellular heterogeneity from many aspects including determine cell types and predict cell fates, presenting more potential benefits for cell biology and clinical applications
Single-cell RNA sequencing technology is widely used in the quantitative study of single-cell RNA expression,which can help us to improve the understanding of human diseases
Summary
Traditional RNA sequencing (RNA-seq) technology measures the average expression of genes across many cells of different cell types, which may mask the real functional capacities of each cell type and hinder the study of cell heterogeneity. The emergence of scRNA-seq technology overcame the limitations in traditional bulk RNA-seq, which enables researchers to investigate the cellular heterogeneity from many aspects including determine cell types and predict cell fates, presenting more potential benefits for cell biology and clinical applications. To mining valuable information from scRNA-seq data, researchers solve multiple tasks of single cell data from a computational perspective. Characteristics of scRNA-seq data, tailored approaches involved in feature selection, feature reduction, clustering, visualization an differentiated genes identification have been developed in recent years. Thereinto, clustering is the most effective way to study the cellular heterogeneity. It is of great significance to improve the accuracy of clustering algorithm [1]
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have