The article presents a scaling normalization method for differential expression analysis of RNA-seq data. The authors emphasize the importance of normalization in RNA-seq data analysis to remove systematic technical effects and ensure accurate estimation of differential expression (DE). They propose a method called Trimmed Mean of M-values (TMM) normalization, which estimates the relative RNA production levels between samples by adjusting for the total RNA output. This method is demonstrated to improve the accuracy of DE inference in simulated and publicly available datasets. The TMM normalization is shown to be robust and effective, especially in scenarios where the underlying distribution of expressed transcripts between samples is significantly different. The authors also compare TMM normalization with other methods and demonstrate its superior performance in controlling false positives. The article concludes that TMM normalization is a simple and effective approach for RNA-seq data normalization, crucial for inferring true differences in expression between samples.The article presents a scaling normalization method for differential expression analysis of RNA-seq data. The authors emphasize the importance of normalization in RNA-seq data analysis to remove systematic technical effects and ensure accurate estimation of differential expression (DE). They propose a method called Trimmed Mean of M-values (TMM) normalization, which estimates the relative RNA production levels between samples by adjusting for the total RNA output. This method is demonstrated to improve the accuracy of DE inference in simulated and publicly available datasets. The TMM normalization is shown to be robust and effective, especially in scenarios where the underlying distribution of expressed transcripts between samples is significantly different. The authors also compare TMM normalization with other methods and demonstrate its superior performance in controlling false positives. The article concludes that TMM normalization is a simple and effective approach for RNA-seq data normalization, crucial for inferring true differences in expression between samples.