A clustering method for small scRNA-seq data based on subspace and weighted distance

PeerJ

article

Zilan Ning¹  Zhijun Dai¹  Hongyan Zhang²  Yuan Chen¹  Zheming Yuan¹ 

[1] Hunan Engineering & Technology Research Centre for Agricultural Big Data Analysis & Decision-Making, Hunan Agricultural University;Hunan Agricultural University, College of Information and Intelligence

关键词: scRNA-seq;    Consensus clustering;    Subspace;    EP_dis;    Marker gene;   

DOI  :  10.7717/peerj.14706

学科分类：社会科学、人文和艺术（综合）

来源: Inra

【 摘 要 】

BackgroundIdentifying the cell types using unsupervised methods is essential for scRNA-seq research. However, conventional similarity measures introduce challenges to single-cell data clustering because of the high dimensional, high noise, and high dropout.MethodsWe proposed a clustering method for small ScRNA-seq data based on Subspace and Weighted Distance (SSWD), which follows the assumption that the sets of gene subspace composed of similar density-distributing genes can better distinguish cell groups. To accurately capture the intrinsic relationship among cells or genes, a new distance metric that combines Euclidean and Pearson distance through a weighting strategy was proposed. The relative Calinski-Harabasz (CH) index was used to estimate the cluster numbers instead of the CH index because it is comparable across degrees of freedom.ResultsWe compared SSWD with seven prevailing methods on eight publicly scRNA-seq datasets. The experimental results show that the SSWD has better clustering accuracy and the partitioning ability of cell groups. SSWD can be downloaded at https://github.com/ningzilan/SSWD.

【 授权许可】

CC BY