A New Graph Autoencoder-Based Multi-level Kernel Subspace Fusion Framework for Single-cell Type Identification.

The advent of single-cell RNA sequencing (scRNA-seq) technology offers the opportunity to conduct biological research at the cellular level. Single-cell type identification based on unsupervised clustering is one of the fundamental tasks of scRNA-seq data analysis. Although many single-cell clustering methods have been developed recently, few can fully exploit the deep potential relationships between cells, resulting in suboptimal clustering. In this paper, we propose scGAMF, a graph autoencoder-based multi-level kernel subspace fusion framework for scRNA-seq data analysis. Based on multiple top feature sets, scGAMF unifies deep feature embedding and kernel space analysis into a single framework to learn an accurate clustering affinity matrix. First, we construct multiple top feature sets to avoid the high variability caused by single feature set learning. Second, scGAMF uses a graph autoencoder (GAEs) to extract deep information embedded in the data, and learn embeddings including gene expression patterns and cell-cell relationships. Third, to fully explore the deep potential relationships between cells, we design a multi-level kernel space fusion strategy. This strategy uses a kernel expression model with adaptive similarity preservation to learn a self-expression matrix shared by all embedding spaces of a given feature set, and a consensus affinity matrix across multiple top feature sets. Finally, the consensus affinity matrix is used for spectral clustering, visualization, and identification of gene markers. Extensive validation on real datasets shows that scGAMF achieves higher clustering accuracy than many popular single-cell analysis methods.

A Fusion Learning Model Based on Deep Learning for Single-Cell RNA Sequencing Data Clustering.

Single-cell RNA sequencing (scRNA-seq) technology provides a means for studying biology from a cellular perspective. The fundamental goal of scRNA-seq data analysis is to discriminate single-cell types using unsupervised clustering. Few single-cell clustering algorithms have taken into account both deep and surface information, despite the recent slew of suggestions. Consequently, this article constructs a fusion learning framework based on deep learning, namely scGASI. For learning a clustering similarity matrix, scGASI integrates data affinity recovery and deep feature embedding in a unified scheme based on various top feature sets. Next, scGASI learns the low-dimensional latent representation underlying the data using a graph autoencoder to mine the hidden information residing in the data. To efficiently merge the surface information from raw area and the deeper potential information from underlying area, we then construct a fusion learning model based on self-expression. scGASI uses this fusion learning model to learn the similarity matrix of an individual feature set as well as the clustering similarity matrix of all feature sets. Lastly, gene marker identification, visualization, and clustering are accomplished using the clustering similarity matrix. Extensive verification on actual data sets demonstrates that scGASI outperforms many widely used clustering techniques in terms of clustering accuracy.

A Personalized Low-Rank Subspace Clustering Method Based on Locality and Similarity Constraints for scRNA-seq Data Analysis.

Single-cell RNA sequencing (scRNA-seq) technology can provide expression profile of single cells, which propels biological research into a new chapter. Clustering individual cells based on their transcriptome is a critical objective of scRNA-seq data analysis. However, the high-dimensional, sparse and noisy nature of scRNA-seq data pose a challenge to single-cell clustering. Therefore, it is urgent to develop a clustering method targeting scRNA-seq data characteristics. Due to its powerful subspace learning capability and robustness to noise, the subspace segmentation method based on low-rank representation (LRR) is broadly used in clustering researches and achieves satisfactory results. In view of this, we propose a personalized low-rank subspace clustering method, namely PLRLS, to learn more accurate subspace structures from both global and local perspectives. Specifically, we first introduce the local structure constraint to capture the local structure information of the data, while helping our method to obtain better inter-cluster separability and intra-cluster compactness. Then, in order to retain the important similarity information that is ignored by the LRR model, we utilize the fractional function to extract similarity information between cells, and introduce this information as the similarity constraint into the LRR framework. The fractional function is an efficient similarity measure designed for scRNA-seq data, which has theoretical and practical implications. In the end, based on the LRR matrix learned from PLRLS, we perform downstream analyses on real scRNA-seq datasets, including spectral clustering, visualization and marker gene identification. Comparative experiments show that the proposed method achieves superior clustering accuracy and robustness.