A scaling normalization method for differential expression analysis of RNA-seq data is introduced. RNA-seq data analysis requires normalization to account for technical biases and ensure accurate detection of differential expression (DE). The method, called TMM (trimmed mean of M values), adjusts for differences in RNA composition between samples by estimating scaling factors based on log-fold changes and absolute expression levels. This approach is more robust than traditional library size normalization, which can lead to biased results when RNA composition varies between samples. The TMM method uses a weighted trimmed mean of log-fold changes (M values) and absolute expression levels (A values) to estimate normalization factors. These factors are then applied to adjust the data for DE analysis. The method was tested on publicly available datasets, including liver and kidney RNA samples, and showed improved performance compared to standard normalization methods. For example, in the liver-kidney dataset, TMM normalization reduced the number of false positives and provided more accurate DE calls. The method is applicable to various RNA-seq datasets and is robust to different sequencing technologies and experimental conditions. It accounts for the sampling properties of RNA-seq data, ensuring that the results are not skewed by differences in RNA composition. The TMM normalization is implemented in the edgeR package and has been validated through simulation studies and real data analysis. The method is essential for accurate DE analysis in RNA-seq data, particularly when comparing samples with different RNA compositions.A scaling normalization method for differential expression analysis of RNA-seq data is introduced. RNA-seq data analysis requires normalization to account for technical biases and ensure accurate detection of differential expression (DE). The method, called TMM (trimmed mean of M values), adjusts for differences in RNA composition between samples by estimating scaling factors based on log-fold changes and absolute expression levels. This approach is more robust than traditional library size normalization, which can lead to biased results when RNA composition varies between samples. The TMM method uses a weighted trimmed mean of log-fold changes (M values) and absolute expression levels (A values) to estimate normalization factors. These factors are then applied to adjust the data for DE analysis. The method was tested on publicly available datasets, including liver and kidney RNA samples, and showed improved performance compared to standard normalization methods. For example, in the liver-kidney dataset, TMM normalization reduced the number of false positives and provided more accurate DE calls. The method is applicable to various RNA-seq datasets and is robust to different sequencing technologies and experimental conditions. It accounts for the sampling properties of RNA-seq data, ensuring that the results are not skewed by differences in RNA composition. The TMM normalization is implemented in the edgeR package and has been validated through simulation studies and real data analysis. The method is essential for accurate DE analysis in RNA-seq data, particularly when comparing samples with different RNA compositions.