Benchmarking Algorithms for Gene Set Scoring of Single-cell ATAC-seq Data.

Autor:	Wang X; Pasteurien College, Suzhou Medical College of Soochow University, Soochow University, Suzhou 215000, China.; Department of Automation, Xiamen University, Xiamen 361005, China., Lian Q; Pasteurien College, Suzhou Medical College of Soochow University, Soochow University, Suzhou 215000, China.; Department of Automation, Xiamen University, Xiamen 361005, China., Dong H; Pasteurien College, Suzhou Medical College of Soochow University, Soochow University, Suzhou 215000, China., Xu S; Department of Automation, Xiamen University, Xiamen 361005, China., Su Y; College of Mathematics and Computer Science, Fuzhou University, Fuzhou 350116, China., Wu X; Pasteurien College, Suzhou Medical College of Soochow University, Soochow University, Suzhou 215000, China.
Jazyk:	angličtina
Zdroj:	Genomics, proteomics & bioinformatics [Genomics Proteomics Bioinformatics] 2024 Jul 03; Vol. 22 (2).
DOI:	10.1093/gpbjnl/qzae014
Abstrakt:	Gene set scoring (GSS) has been routinely conducted for gene expression analysis of bulk or single-cell RNA sequencing (RNA-seq) data, which helps to decipher single-cell heterogeneity and cell type-specific variability by incorporating prior knowledge from functional gene sets. Single-cell assay for transposase accessible chromatin using sequencing (scATAC-seq) is a powerful technique for interrogating single-cell chromatin-based gene regulation, and genes or gene sets with dynamic regulatory potentials can be regarded as cell type-specific markers as if in single-cell RNA-seq (scRNA-seq). However, there are few GSS tools specifically designed for scATAC-seq, and the applicability and performance of RNA-seq GSS tools on scATAC-seq data remain to be investigated. Here, we systematically benchmarked ten GSS tools, including four bulk RNA-seq tools, five scRNA-seq tools, and one scATAC-seq method. First, using matched scATAC-seq and scRNA-seq datasets, we found that the performance of GSS tools on scATAC-seq data was comparable to that on scRNA-seq, suggesting their applicability to scATAC-seq. Then, the performance of different GSS tools was extensively evaluated using up to ten scATAC-seq datasets. Moreover, we evaluated the impact of gene activity conversion, dropout imputation, and gene set collections on the results of GSS. Results show that dropout imputation can significantly promote the performance of almost all GSS tools, while the impact of gene activity conversion methods or gene set collections on GSS performance is more dependent on GSS tools or datasets. Finally, we provided practical guidelines for choosing appropriate preprocessing methods and GSS tools in different application scenarios. (© The Author(s) 2024. Published by Oxford University Press and Science Press on behalf of the Beijing Institute of Genomics, Chinese Academy of Sciences / China National Center for Bioinformation and Genetics Society of China.)
Databáze:	MEDLINE
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