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SSRE: Cell Type Detection Based on Sparse Subspace Representation and Similarity Enhancement
[article]
2020
bioRxiv
pre-print
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. It corresponds to solving an unsupervised clustering problem, in which the similarity measurement between cells in a high dimensional space affects the result significantly. Although many approaches have been proposed recently, the accuracy of cell type identification still needs to be improved. In this study, we proposed a novel single-cell
doi:10.1101/2020.04.08.028779
fatcat:zj7du2nkp5arfah2jfkewj3jru