Visualisation of 2D gene expression embeddings (t-SNE and UMAP)

An important part of presenting the summary of the clustering analysis is 2D visualisation via embedding.

UMAP and t-SNE, are 2 common dimensionality reduction methods that can be useful for displaying clustering results.

Running t-SNE

To run the t-SNE on the ds object: ds.run_tsne(pca_dims=-1,n_iter=5000, perplexity=70, early_exaggeration=10, metric="correlation", exclude_bad_clusters=True, random_state=0, tsne_kwargs={})

  • pca_dims: Number of PCA dimensions used for the tSNE embedding.
  • n_iter: Maximum number of iterations for the tSNE.
  • perplexity: The perplexity value of the tSNE (please refer to the section How should I set the perplexity in t-SNE? ).
  • early_exaggeration: Early exaggeration parameter for tSNE. Controls the tightness of the resulting tSNE plot.
  • metric: Metric for calculation of distance between vectors in gene expression space.
  • exclude_bad_clusters: If true, the vectors that are excluded by the clustering algorithm will not be considered for tSNE computation.
  • random_state: Random seed or scikit-learn’s random state object to replicate the same result
  • tsne_kwargs: Other keyward parameters for tSNE.

Running UMAP

To run the t-SNE on the ds object: ds.run_umap(self, pca_dims=-1, metric="correlation", min_dist=0.8, exclude_bad_clusters=True, random_state=0, umap_kwargs={})

  • pca_dims: Number of PCA dimensions used for the UMAP embedding.
  • metric: Metric for calculation of distance between vectors in gene expression space.
  • min_dist: ‘min_dist’ parameter for UMAP.
  • exclude_bad_clusters: If true, the vectors that are excluded by the clustering algorithm will not be considered for UMAP computation.
  • random_state: Random seed or scikit-learn’s random state object to replicate the same result
  • umap_kwargs: Other keyward parameters for UMAP.

Plotting embeddings

Plotting of the t-SNE and UMAP beddings can be performed by:

ds.plot_embedding(method='umap')
ds.plot_embedding(method='tSNE')

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