UMAP Plot
Chart overview
UMAP produces 2D embeddings that preserve both local neighborhood structure and more global topological relationships compared with t-SNE.
Key points
- Researchers in genomics, chemistry, and deep learning use UMAP to explore high-dimensional datasets at scale, since it is substantially faster and produces embeddings where inter-cluster distances carry more meaning.
- It has become the standard dimensionality reduction visualization in modern single-cell biology pipelines.
Example Visualization
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Generate publication-ready umap plots with AI in seconds. No coding required – just describe your data and let AI do the work.
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"Create a UMAP scatter plot from my embedding data. Color points by category using a qualitative colormap, add a labeled legend, annotate cluster centroids with category names, and display n_neighbors and min_dist parameters in the subtitle."
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AI Generation
Our AI analyzes your data and generates the UMAP Plot code automatically.
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Python Code Example
Console Output
Figure saved: plotivy-umap-plot.png
Common Use Cases
- 1Visualizing protein sequence embeddings colored by functional family
- 2Exploring chemical compound space in drug discovery datasets
- 3Inspecting latent space structure of a variational autoencoder
- 4Comparing UMAP layouts with different n_neighbors values for cell atlas data
Pro Tips
Set min_dist=0.1 for tighter clusters and min_dist=0.5 for a more spread layout
Use larger point markers when plotting more than 100,000 points
Apply a density-based overlay to highlight the densest regions
Always fix random_state for reproducibility across runs and collaborators
Scientific Chart Selection Cheat Sheet
Not sure whether to use a Violin Plot, Box Plot, or Ridge Plot? Download our single-page reference mapping the most-used scientific chart types, exactly when to use them, and the core Matplotlib/Seaborn functions.