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t-SNE (t-Distributed Stochastic Neighbor Embedding) is a non-linear dimensionality reduction method. Unlike PCA, which rotates the data linearly, t-SNE is designed to preserve local neighbourhoods: samples that are similar in the original space stay close together in the 2D map, while dissimilar samples get pushed apart. t-SNE is useful for:
  • Spotting non-linear clusters that PCA misses
  • Quick visual check for natural groupings
  • Communicating data structure to non-technical stakeholders
t-SNE is for visualization, not modelling. The 2D coordinates are not directly comparable across runs (different random seeds produce different layouts). Don’t use t-SNE coordinates as features for prediction.

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Configuring a t-SNE run

In the Configure run card on the analysis detail page, select t-SNE.
t-SNE configuration showing method selection, run name, and perplexity slider

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Parameters

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Choosing perplexity

If the result looks like a single blob, lower the perplexity. If it shatters into many tiny groups, raise it. Try a few values: there’s no single “right” answer.
Click Launch run. Status goes through “Queueing”, “Analysing”, “Done”.

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Reading the results

t-SNE run detail showing 2D embedding scatter plot coloured by a property

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Embedding plot

A 2D scatter plot. Dim 1 is the horizontal axis, Dim 2 is the vertical axis. Each point is one spectrum. The axes themselves have no inherent meaning in t-SNE. Only relative distances between points matter:
  • Points close together = similar spectra
  • Points far apart = dissimilar spectra
  • Point clusters = natural groupings

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Colour by

Pick a property to colour the points. If samples coloured by a property segregate into distinct regions of the map, that property has a strong signal in your spectra.

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Common patterns

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Limitations

  • Stochastic: every run produces a different layout, even with the same parameters
  • No global meaning: distances between far-apart clusters are not meaningful
  • Slow on large datasets: 10,000+ samples take noticeably longer
  • Sensitive to parameters: small perplexity changes can produce very different maps
For these reasons, use t-SNE alongside PCA, not as a replacement. PCA gives you reproducible global structure; t-SNE gives you visual cluster intuition.

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Comparing t-SNE runs

t-SNE embeddings are independent across runs (different random initializations). When you compare runs, the comparison shows each run’s embedding side by side. Don’t expect points in run A to land in the same place as run B; only the cluster structure is meaningful.