dendrogram
Definition
A dendrogram is a tree-like diagram that illustrates the hierarchical relationships between data points based on their similarity or distance. In biological research, dendrograms are commonly used to visualize clustering results from gene expression data, phylogenetic relationships between species, or protein family classifications. The vertical axis typically represents the distance or dissimilarity between clusters, while the horizontal axis shows individual samples or entities. Branch points (nodes) indicate where clusters merge, with the height of each branch reflecting the degree of similarity. Dendrograms are essential for identifying natural groupings in complex biological datasets and understanding evolutionary or functional relationships.
Visualize dendrogram in Nodes Bio
Researchers can transform dendrogram relationships into interactive network graphs in Nodes Bio, where hierarchical clusters become connected nodes. This allows exploration of how genes, proteins, or samples group together based on expression patterns or functional similarity. Users can overlay additional data layers onto clustered networks, identify hub nodes within clusters, and trace pathways connecting different branches to understand biological relationships beyond simple hierarchical visualization.
Visualization Ideas:
- Gene expression clustering networks showing sample relationships
- Phylogenetic networks displaying evolutionary distances between species
- Protein family classification networks based on sequence similarity
Example Use Case
A cancer researcher performs RNA-seq analysis on 100 tumor samples and generates a dendrogram showing three major clusters based on gene expression patterns. By converting this dendrogram into a network in Nodes Bio, they discover that samples in one cluster share mutations in specific signaling pathways. The network visualization reveals previously hidden connections between samples that appeared distant in the dendrogram but share common drug resistance markers, leading to refined patient stratification strategies for clinical trials.