DESeq2 is a method for differential analysis of RNA-seq data using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. It enables more quantitative analysis focused on the strength rather than the mere presence of differential expression. DESeq2 is available as an R/Bioconductor package. The method models read counts as a negative binomial distribution with mean and dispersion parameters. It uses generalized linear models (GLMs) with a logarithmic link function to estimate fold changes. Empirical Bayes shrinkage is used to estimate dispersion parameters, which helps to reduce variability in dispersion estimates and improve the accuracy of differential expression testing. DESeq2 also provides a regularized logarithm transformation (rlog) to stabilize variance and facilitate multivariate visualization and clustering. It includes methods for detecting and handling outliers, and for specifying thresholds on effect size for hypothesis testing. DESeq2 has been benchmarked against other methods for differential expression analysis, showing high sensitivity and precision, while controlling the false positive rate. It performs well in simulations and real data, with consistent results across different sample sizes and effect sizes. DESeq2 is particularly effective in detecting differentially expressed genes with small fold changes and in handling datasets with varying sequencing depths. It is also suitable for gene-level analysis and can be extended to isoform-specific analysis.DESeq2 is a method for differential analysis of RNA-seq data using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. It enables more quantitative analysis focused on the strength rather than the mere presence of differential expression. DESeq2 is available as an R/Bioconductor package. The method models read counts as a negative binomial distribution with mean and dispersion parameters. It uses generalized linear models (GLMs) with a logarithmic link function to estimate fold changes. Empirical Bayes shrinkage is used to estimate dispersion parameters, which helps to reduce variability in dispersion estimates and improve the accuracy of differential expression testing. DESeq2 also provides a regularized logarithm transformation (rlog) to stabilize variance and facilitate multivariate visualization and clustering. It includes methods for detecting and handling outliers, and for specifying thresholds on effect size for hypothesis testing. DESeq2 has been benchmarked against other methods for differential expression analysis, showing high sensitivity and precision, while controlling the false positive rate. It performs well in simulations and real data, with consistent results across different sample sizes and effect sizes. DESeq2 is particularly effective in detecting differentially expressed genes with small fold changes and in handling datasets with varying sequencing depths. It is also suitable for gene-level analysis and can be extended to isoform-specific analysis.