differential expression
Definition
Differential expression refers to the quantitative comparison of gene or protein expression levels between different biological conditions, such as diseased versus healthy tissue, treated versus untreated cells, or different developmental stages. This analysis identifies which genes are significantly upregulated or downregulated in response to experimental conditions. Statistical methods like DESeq2, edgeR, or limma are used to determine fold changes and significance values (p-values, adjusted p-values). Differential expression analysis is fundamental to understanding molecular mechanisms underlying biological processes, identifying biomarkers, and discovering therapeutic targets. It transforms raw transcriptomic data into actionable biological insights by revealing which genes drive phenotypic differences.
Visualize differential expression in Nodes Bio
Researchers can visualize differentially expressed genes as network nodes, with edges representing functional relationships, protein-protein interactions, or pathway memberships. Node size or color can encode fold change magnitude and statistical significance. This enables identification of co-regulated gene clusters, upstream regulators, and downstream effectors. Network visualization reveals how differentially expressed genes interconnect within biological pathways, facilitating hypothesis generation about disease mechanisms or drug responses.
Visualization Ideas:
- Volcano plot networks showing fold change versus significance with pathway overlays
- Gene regulatory networks highlighting transcription factors controlling differentially expressed genes
- Multi-condition heatmap networks showing expression patterns across experimental groups
Example Use Case
A cancer researcher compares tumor samples to normal tissue using RNA-seq and identifies 500 differentially expressed genes. By importing this data into Nodes Bio, they visualize these genes within protein interaction networks and discover that upregulated genes cluster around three hub proteins: MYC, TP53, and EGFR. The network reveals that many downregulated genes are tumor suppressors connected to TP53, suggesting p53 pathway dysfunction. This visualization guides the selection of combination therapies targeting multiple dysregulated hubs simultaneously.