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.
Python Tutorial
How to create a t-sne plot in Python
Use the full tutorial for implementation details, troubleshooting, and chart variations in matplotlib, seaborn, and plotly.
Python Scatter Plot TutorialExample Visualization
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"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
Long-tail keyword opportunities
High-intent chart variations
Library comparison for this chart
matplotlib
Best when you need full control over axis formatting, annotation placement, and journal-specific styling for t-sne-plot.
numpy
Useful in specialized workflows that complement core Python plotting libraries for t-sne-plot analysis tasks.
sklearn
Useful in specialized workflows that complement core Python plotting libraries for t-sne-plot analysis tasks.
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.