Differential expression analysis identifies genes whose expression differs between biological conditions.
After RNA-seq reads are processed and quantified, gene-level counts or transcript-level estimates can be compared between groups. The goal is to determine which genes show expression changes larger than expected from biological and technical variation.
Raw read counts are influenced by sequencing depth and composition differences between samples. Normalization adjusts for these differences so that samples can be compared more fairly. Without normalization, highly sequenced samples may appear to have higher expression simply because they have more reads.
Log2 fold change describes the magnitude and direction of expression difference. A positive value means a gene is higher in one condition, while a negative value means it is lower. The biological meaning depends on how the comparison is defined.
RNA-seq tests thousands of genes at once. Adjusted p-values help control false discoveries across many simultaneous tests. A gene with a large fold change but weak statistical support should be interpreted cautiously.
A significant gene list is not the final answer. Researchers should review expression patterns, replicate consistency, pathway context, known marker genes, and possible confounding factors. Visualization tools such as volcano plots, heatmaps, and PCA plots can help detect overall patterns.
This guide is provided for research and educational purposes. RNA-seq results should be interpreted with appropriate experimental design, quality control, statistical review, and biological validation.