DESeq2 starts by normalizing raw count data to account for differences in sequencing depth and RNA composition. It then estimates the dispersion of counts for each gene, which reflects the variability of gene expression. By fitting a generalized linear model to the data, DESeq2 tests for differential expression and applies a multiple testing correction to control the false discovery rate.
