Impact of data preprocessing on cell-type clustering based on single-cell RNA-seq data

BMC Bioinformatics

Xin Gao¹  Juntao Liu²  Chunxiang Wang² 

[1] Computer, Electrical and Mathematical Sciences and Engineering Division, Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST);School of Mathematics and Statistics, Shandong University (Weihai);

关键词: Preprocessing method;    Single-cell RNA-seq data;    Gene expression data;    Single-cell clustering;    SC3;   

DOI  :  10.1186/s12859-020-03797-8

来源: DOAJ

【 摘 要 】

Abstract 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.

【 授权许可】

Unknown