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ScGSLC: An unsupervised graph similarity learning framework for single-cell RNA-seq data clustering.

Abstract：

:Accurate clustering of cells from single-cell RNA sequencing (scRNA-seq) data is an essential step for biological analysis such as putative cell type identification. However, scRNA-seq data has high dimension and high sparsity, which makes traditional clustering methods less effective to reflect the similarity between cells. Since genetic network fundamentally defines the functions of cell and deep learning shows strong advantages in network representation learning, we propose a novel scRNA-seq clustering framework ScGSLC based on graph similarity learning. ScGSLC effectively integrates scRNA-seq data and protein-protein interaction network to a graph. Then graph convolution network is employed by ScGSLC to embedding graph and clustering the cells by the calculated similarity between graphs. Unsupervised clustering results of nine public data sets demonstrate that ScGSLC shows better performance than the state-of-the-art methods.

journal_name

Comput Biol Chem

journal_title

Computational biology and chemistry

authors

Li J,Jiang W,Han H,Liu J,Liu B,Wang Y

doi

10.1016/j.compbiolchem.2020.107415

subject

Has Abstract

pub_date

2020-11-18 00:00:00

pages

107415

eissn

1476-9271

issn

1476-928X

pii

S1476-9271(20)31489-4

journal_volume

90

pub_type

杂志文章

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