标题:Impact of data preprocessing on cell-type clustering based on single-cell RNA-seq data
作者:Wang C.; Gao X.; Liu J.
作者机构:[Wang, C] School of Mathematics and Statistics, Shandong University (Weihai), Weihai, 264209, China;[ Gao, X] Computer, Electrical and Mathematical Sc 更多
通讯作者:Liu, J(juntaosdu@126.com)
通讯作者地址:[Liu, J] School of Mathematics and Statistics, Shandong University (Weihai), Computer, Electrical and Mathematical Sciences and Engineering Division, 更多
来源:BMC Bioinformatics
出版年:2020
卷:21
期:1
DOI:10.1186/s12859-020-03797-8
关键词:Gene expression data; Preprocessing method; SC3; Single-cell clustering; Single-cell RNA-seq data
摘要:Background: Advances in single-cell RNA-seq technology have led to great opportunities for the quantitative characterization of cell types, and many clustering algorithms have been developed based on single-cell gene expression. However, we found that different data preprocessing methods show quite different effects on clustering algorithms. Moreover, there is no specific preprocessing method that is applicable to all clustering algorithms, and even for the same clustering algorithm, the best preprocessing method depends on the input data. Results: We designed a graph-based algorithm, SC3-e, specifically for discriminating the best data preprocessing method for SC3, which is currently the most widely used clustering algorithm for single cell clustering. When tested on eight frequently used single-cell RNA-seq data sets, SC3-e always accurately selects the best data preprocessing method for SC3 and therefore greatly enhances the clustering performance of SC3. Conclusion: The SC3-e algorithm is practically powerful for discriminating the best data preprocessing method, and therefore largely enhances the performance of cell-type clustering of SC3. It is expected to play a crucial role in the related studies of single-cell clustering, such as the studies of human complex diseases and discoveries of new cell types. © 2020 The Author(s).
收录类别:SCOPUS
资源类型:期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092478414&doi=10.1186%2fs12859-020-03797-8&partnerID=40&md5=16b6ac7ace14bff45b34e86500c2b1d0
TOP