A robust single cell clustering method based on subspace learning and partial imputation

Cell heterogeneity analysis is an important and urgent task in single cell data research. Numerous cell type identification methods have been proposed to address the issue. Due to the high rate of dropout and complex biological background, it is still a challenging task to obtain the accurate clusters of cells. In this study, we propose a robust single cell clustering method based on subspace learning and partial imputation, called RCSLI. RCSLI incorporates a modified variable genes selection method and utilizes the self-expression of scRNA-seq data to learn sparse cell-to-cell similarity and impute part of missing expression values. To evaluate the clustering performance of RCSLI, we compare it with nine state-of-the-art single cell clustering methods on eight scRNA-seq datasets. The experimental results show that RCSLI gets more accurate and robust clustering results. The imputation impact on the specific gene markers is evaluated on PBMC data. The classification results by taking these marker genes as predictors show RCSLI recovers the real dropouts, meanwhile, introduces less noise.