SSRE: Cell Type Detection Based on Sparse Subspace Representation and Similarity Enhancement.

ABSTRACT: Accurate identification of cell types from single-cell RNA sequencing (scRNA-seq) data plays a critical role in a variety of scRNA-seq analysis studies. This task corresponds to solving an unsupervised clustering problem, in which the similarity measurement between cells affects the result significantly. Although many approaches for cell type identification have been proposed, the accuracy still needs to be improved. In this study, we proposed a novel single-cell clustering framework based on similarity learning, called SSRE. SSRE models the relationships between cells based on subspace assumption, and generates a sparse representation of the cell-to-cell similarity. The sparse representation retains the most similar neighbors for each cell. Besides, three classical pairwise similarities a

SUBMITTER: Liang Z 

PROVIDER: S-EPMC8602764 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature