How is differential gene expression analysis performed using RNA-Seq data?

Differential expression analysis from RNA-Seq involves: 1) Read alignment - map reads to reference genome/transcriptome using STAR or HISAT2. 2) Quantification - count reads per gene using featureCounts, HTSeq, or estimate TPM/FPKM using Salmon/Kallisto. 3) Normalization - account for library size and composition using methods like TMM, DESeq2 normalization, or TPM. 4) Statistical testing - identify differentially expressed genes using negative binomial models in DESeq2, edgeR, or limma-voom, accounting for overdispersion in count data. 5) Multiple testing correction - apply FDR/Benjamini-Hochberg to control false positives. 6) Downstream analysis - gene set enrichment (GSEA), pathway analysis, clustering, and visualization with heatmaps and volcano plots.