Comparison of methods to detect differentially expressed genes between single-cell populations. (2nd July 2016)

• Record Type: Journal Article
• Authors: Jaakkola, Maria K; Seyednasrollah, Fatemeh; Mehmood, Arfa; Elo, Laura L
• Is Part Of: Briefings in bioinformatics. Volume 18:Number 5(2017:Sep.)
• Page Start: 735
• Page End: 743
• Publication Date: 2016-07-02
• Subjects: single-cell -- comparison -- differential expression -- RNA-seq -- reproducibility; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bbw057
• Languages: English
• ISSNs: 1467-5463

Abstract

Abstract: We compared five statistical methods to detect differentially expressed genes between two distinct single-cell populations. Currently, it remains unclear whether differential expression methods developed originally for conventional bulk RNA-seq data can also be applied to single-cell RNA-seq data analysis. Our results in three diverse comparison settings showed marked differences between the different methods in terms of the number of detections as well as their sensitivity and specificity. They, however, did not reveal systematic benefits of the currently available single-cell-specific methods. Instead, our previously introduced reproducibility-optimization method showed good performance in all comparison settings without any single-cell-specific modifications.