t-SNE Plot
Chart overview
t-SNE maps high-dimensional data into 2D by preserving local neighborhood structure, making clusters visible that are otherwise hidden.
Key points
- It is a standard exploratory visualization in single-cell genomics, NLP embedding analysis, and computer vision feature space inspection.
- The resulting scatter plots help researchers confirm whether learned representations separate meaningful biological, linguistic, or visual categories.
Example Visualization
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Generate publication-ready t-sne plots with AI in seconds. No coding required – just describe your data and let AI do the work.
View example prompt
"Create a t-SNE scatter plot from my high-dimensional data. Color points by class label using a qualitative colormap, add a legend, set perplexity and random seed in the title, and remove axis ticks since the axes have no physical meaning."
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Python Code Example
Console Output
Figure saved: plotivy-t-sne-plot.png
Common Use Cases
- 1Exploring cell type clusters in single-cell RNA-seq data
- 2Visualizing word or sentence embedding spaces from language models
- 3Inspecting feature separation in the final layer of a trained image classifier
- 4Comparing cluster compactness across different data preprocessing pipelines
Pro Tips
Run multiple perplexity values (5, 30, 50) and compare before choosing one for publication
Use the same random seed for reproducibility and include it in the figure caption
Do not interpret distances between clusters as meaningful; only local structure is preserved
Pre-reduce to 50 PCA components before t-SNE to speed up computation on large datasets
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.