differential expression analysis
Definition
Differential expression analysis is a computational method used to identify genes, transcripts, or proteins that show statistically significant differences in expression levels between experimental conditions, such as diseased versus healthy tissue, treated versus untreated samples, or different developmental stages. This technique typically involves RNA-sequencing (RNA-seq) or microarray data, applying statistical tests to account for biological variability and multiple testing corrections. Key concepts include fold change (magnitude of difference), adjusted p-values (statistical significance), and normalization methods to ensure fair comparisons. Differential expression analysis is fundamental for understanding molecular mechanisms underlying biological processes, identifying biomarkers, and discovering therapeutic targets in disease research.
Visualize differential expression analysis in Nodes Bio
Researchers can visualize differentially expressed genes as network nodes, with connections representing functional relationships, protein-protein interactions, or pathway memberships. Color-coding nodes by fold change and sizing by significance enables rapid identification of key regulatory hubs. Network clustering reveals coordinated expression patterns and functional modules, while pathway enrichment overlays highlight which biological processes are most affected by experimental perturbations.
Visualization Ideas:
- Volcano plot networks showing fold change versus significance with connected gene relationships
- Gene regulatory networks highlighting transcription factors controlling differentially expressed genes
- Pathway interaction maps with differentially expressed genes overlaid on KEGG or Reactome pathways
Example Use Case
A cancer researcher performs RNA-seq on tumor samples versus adjacent normal tissue from 50 patients with triple-negative breast cancer. Differential expression analysis identifies 2,847 significantly altered genes. By mapping these genes onto protein interaction networks and pathway databases, the researcher discovers a cluster of upregulated genes centered around the PI3K/AKT signaling pathway, suggesting this pathway as a potential therapeutic target. Network visualization reveals that three hub genes show consistent overexpression across all patient samples, making them promising biomarker candidates.