Identifying cell types from single-cell data based on similarities and dissimilarities between cells.

ABSTRACT:

Background

With the development of the technology of single-cell sequence, revealing homogeneity and heterogeneity between cells has become a new area of computational systems biology research. However, the clustering of cell types becomes more complex with the mutual penetration between different types of cells and the instability of gene expression. One way of overcoming this problem is to group similar, related single cells together by the means of various clustering analysis methods. Although some methods such as spectral clustering can do well in the identification of cell types, they only consider the similarities between cells and ignore the influence of dissimilarities on clustering results. This methodology may limit the performance of most of the conventional clustering a

SUBMITTER: Li Y 

PROVIDER: S-EPMC8132444 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature