cosine distance
Definition
Cosine distance is a metric that measures the dissimilarity between two vectors by calculating one minus their cosine similarity. It ranges from 0 (identical direction) to 2 (opposite direction), focusing on the angle between vectors rather than their magnitude. In biological research, cosine distance is particularly valuable for comparing high-dimensional data like gene expression profiles, protein sequences, or chemical compound structures. Unlike Euclidean distance, it emphasizes pattern similarity over absolute values, making it robust for normalized data. This property is crucial when comparing biological samples where relative changes in multiple features matter more than absolute measurements, such as identifying functionally similar genes across different experimental conditions.
Visualize cosine distance in Nodes Bio
In Nodes Bio, cosine distance enables researchers to cluster and visualize similar biological entities in network space. Nodes positioned closer together represent entities with similar expression patterns or functional profiles. Users can apply cosine distance-based layouts to reveal functional modules in gene co-expression networks, identify drug candidates with similar mechanism profiles, or group patients with comparable molecular signatures, creating intuitive visual representations of complex similarity relationships.
Visualization Ideas:
- Gene co-expression networks where edge weights represent cosine distance between expression profiles
- Drug-target similarity networks clustering compounds by mechanism of action using cosine distance
- Patient stratification networks grouping individuals by multi-omics profile similarity
Example Use Case
A cancer researcher analyzing RNA-seq data from 500 tumor samples wants to identify patient subgroups with similar gene expression patterns. Using cosine distance to compare the 20,000-gene expression vectors for each patient, they discover three distinct clusters. One cluster shows elevated immune response genes, another displays metabolic dysregulation, and the third exhibits stem cell-like characteristics. This cosine distance-based classification reveals that patients in the immune-active cluster respond better to immunotherapy, directly informing treatment selection and improving clinical outcomes.