An Adaptive Sparse Subspace Clustering for Cell Type Identification - Zheng, Ruiqing; Liang, Zhenlan; Chen, Xiang; Tian, Yu; Cao, Chen; Li, Min (Frontiers in Genetics, 2020)
The rapid development of single-cell transcriptome sequencing technology provides a cell-level perspective to study biological problems. Identification of cell types is one of the fundamental issues in computational analysis of single cell data. Due to the large amount of noise from single-cell technologies and high dimension of expression profiles, traditional clustering methods are not so applicable to solve it. To address the problem, we design an adaptive sparse subspace clustering method, called AdaptiveSSC, to identify cell types. AdaptiveSSC is based on the assumption that the expression of cells with the same type lie in the same subspace, so one cell can be expressed as a linear combination of the other cells. Moreover, it uses a data-driven adaptive sparse constraint to construct the similarity matrix. The comparison results on eight scRNA-seq datasets show that AdaptiveSSC outperforms original subspace clustering and other state-of-art methods. Moreover, the learned similarity matrix can also be integrated with a modified t-SNE to obtain better visualization result.