ScCODE: an R package for data-specific differentially expressed gene detection on single-cell RNA-sequencing data. Issue 5 (23rd May 2022)

• Record Type: Journal Article
• Authors: Zou, Jiawei; Deng, Fulan; Wang, Miaochen; Zhang, Zhen; Liu, Zheqi; Zhang, Xiaobin; Hua, Rong; Chen, Ke; Zou, Xin; Hao, Jie
• Is Part Of: Briefings in bioinformatics. Volume 23:Issue 5(2022)
• Publication Date: 2022-05-23
• Subjects: scRNA-seq data -- differentially expressed gene detection -- evaluation -- gene filtering; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bbac180
• Languages: English
• ISSNs: 1467-5463

Abstract

Abstract: Differential expression (DE) gene detection in single-cell ribonucleic acid (RNA)-sequencing (scRNA-seq) data is a key step to understand the biological question investigated. Filtering genes is suggested to improve the performance of DE methods, but the influence of filtering genes has not been demonstrated. Furthermore, the optimal methods for different scRNA-seq datasets are divergent, and different datasets should benefit from data-specific DE gene detection strategies. However, existing tools did not take gene filtering into consideration. There is a lack of metrics for evaluating the optimal method on experimental datasets. Based on two new metrics, we propose single-cell Consensus Optimization of Differentially Expressed gene detection, an R package to automatically optimize DE gene detection for each experimental scRNA-seq dataset.