Differentialexpression

Differential expression refers to genes whose expression levels show statistically significant differences between two or more biological conditions, such as treated versus untreated samples, diseased versus healthy tissues, or different developmental stages. It is a central concept in transcriptomics and is typically studied using high-throughput data from RNA sequencing or microarrays.

Analyses commonly treat count data from RNA sequencing as following a negative binomial distribution, accounting for biological variability and overdispersion. Methods such as DESeq2, edgeR, and limma (with appropriate data transformations) model gene counts or intensities using generalized linear models and estimate dispersion parameters to test for differential expression. Normalization steps adjust for library size, composition biases, and other technical factors before statistical testing.

A typical workflow includes data preprocessing and quality control, normalization, fitting a model to compare conditions, and statistical testing to obtain p-values. Because thousands of genes are tested, p-values are adjusted to control the false discovery rate. The results are usually summarized as log2 fold changes and adjusted p-values, identifying upregulated genes (positive fold change) and downregulated genes (negative fold change). Visualization tools such as MA plots, volcano plots, and heatmaps help interpret patterns and relationships among samples and genes.

Key considerations include experimental design, replication, and potential confounders or batch effects. Low-count genes can be noisy and may be filtered out, and proper normalization is essential for reliable results. Limitations include assumptions about distribution, sample size, and the need for careful validation, often with independent experiments or complementary assays.

Applications of differential expression analysis include identifying candidate biomarkers, elucidating disease mechanisms, and informing functional enrichment and pathway analyses to interpret biological responses.