This paper compares eleven methods for differential expression analysis of RNA-seq data, focusing on their performance under various experimental conditions. The methods are evaluated using both simulated and real RNA-seq data. The evaluation measures include the ability to rank true differentially expressed (DE) genes correctly, control of type I error rate and false discovery rate, and computational time. The results show that small sample sizes (2 samples per condition) pose significant challenges for all methods, leading to inflated false discovery rates. Methods combining variance-stabilizing transformations with the 'limma' method, such as voom+limma and vst+limma, perform well under many conditions, but require at least 3 samples per condition. The non-parametric SAMseq method also performs well, especially with larger sample sizes, but requires 4-5 samples per condition. The study highlights the importance of considering the experimental context when choosing a method for differential expression analysis, as no single method is optimal under all circumstances.This paper compares eleven methods for differential expression analysis of RNA-seq data, focusing on their performance under various experimental conditions. The methods are evaluated using both simulated and real RNA-seq data. The evaluation measures include the ability to rank true differentially expressed (DE) genes correctly, control of type I error rate and false discovery rate, and computational time. The results show that small sample sizes (2 samples per condition) pose significant challenges for all methods, leading to inflated false discovery rates. Methods combining variance-stabilizing transformations with the 'limma' method, such as voom+limma and vst+limma, perform well under many conditions, but require at least 3 samples per condition. The non-parametric SAMseq method also performs well, especially with larger sample sizes, but requires 4-5 samples per condition. The study highlights the importance of considering the experimental context when choosing a method for differential expression analysis, as no single method is optimal under all circumstances.