Detection of Cell Types from Single-cell RNA-seq Data using Similarity via Kernel Preserving Learning Embedding

Abstract

The recent advances in single-cell sequencing techniques allow us to study biological issues on cell levels. Detecting cell types from scRNA-seq data analysis is important and meaningful. However, high-level noise and the nonlinearity and sparsity of scRNA-seq data are great challenges. In this paper, we propose a cell-type detection algorithm preserving the overall cell relations named POCR to analyze scRNA-seq data. POCR utilizes a kernel embedding similarity measure to calculate cell-to-cell similarity, by minimizing the reconstruction error of a kernel matrix, rather than the reconstruction error of the original data adopted by other similarity metrics. According to the scale of scRNA-seq datasets, we select Gaussian kernel or linear kernel to calculate the embedding. We then adopt spectral clustering to detect the cell types based on the learned cell-to-cell similarity. The results are further visualized to demonstrate the effectiveness of the cell-type detection algorithm POCR. Further analysis shows that the learned similarity could improve the clustering and visualization of cell types in scRNA-seq data. Our proposed algorithm is compared with five other state-of-the-art cell subtype detection methods. The effectiveness of the algorithms is evaluated by two criteria: ARI and NMI. The experiments show that POCR achieves accurate and robust performance across different scRNA-seq data. Our python implementation of POCR is available at https://github.com/ZeMing-Liu/POCR.