An evaluation of RNA-seq differential analysis methods

Abstract

RNA-seq is a high-throughput sequencing technology widely used for gene transcript discovery and quantification under different biological or biomedical conditions. A fundamental research question in most RNA-seq experiments is the identification of differentially expressed genes among experimental conditions or sample groups. Numerous statistical methods for RNA-seq differential analysis have been proposed since the emergence of the RNA-seq assay. To evaluate popular differential analysis methods used in the open source R and Bioconductor packages, we conducted multiple simulation studies to compare the performance of eight RNA-seq differential analysis methods used in RNA-seq data analysis (edgeR, DESeq, DESeq2, baySeq, EBSeq, NOISeq, SAMSeq, Voom). The comparisons were across different scenarios with either equal or unequal library sizes, different distribution assumptions and sample sizes. We measured performance using false discovery rate (FDR) control, power, and stability. No significant differences were observed for FDR control, power, or stability across methods, whether with equal or unequal library sizes. For RNA-seq count data with negative binomial distribution, when sample size is 3 in each group, EBSeq performed better than the other methods as indicated by FDR control, power, and stability. When sample sizes increase to 6 or 12 in each group, DESeq2 performed slightly better than other methods. All methods have improved performance when sample size increases to 12 in each group except DESeq. For RNA-seq count data with log-normal distribution, both DESeq and DESeq2 methods performed better than other methods in terms of FDR control, power, and stability across all sample sizes. Real RNA-seq experimental data were also used to compare the total number of discoveries and stability of discoveries for each method. For RNA-seq data analysis, the EBSeq method is recommended for studies with sample size as small as 3 in each group, and the DESeq2 method is recommended for sample size of 6 or higher in each group when the data follow the negative binomial distribution. Both DESeq and DESeq2 methods are recommended when the data follow the log-normal distribution.

N-of-one differential gene expression without control samples using a deep generative model

“…However, NATs from cancer patients have been shown to display some of the same characteristics as the tumor (Aran et al 2017), implying that these samples are not truly normal. Inversely, normal samples from other individuals will display genetic heterogeneity and are thus unsuitable for direct comparison with classical methods, especially at low sample numbers (D. Li et al 2022;Vihinen 2022). Lastly, a general problem concerning bulk sequencing data is that samples differ in cell type composition.…”

Application of edgeR and DESeq2 methods in plant experiments based on RNA-seq technology

“…There are many publications comparing different methods. For instance in Li et al (2022) the authors compare eight methods: edgeR, DESeq, DESeq2, baySeq, EBSeq, NOISeq, SAMSeq and Voom, using both simulated and real datasets. The comparisons were across different scenarios with either equal or unequal library sizes, different distribution assumptions and sample sizes.…”