| # Define UMAP | |
| brain_umap = umap.UMAP(random_state=999, n_neighbors=30, min_dist=.25) | |
| # Fit UMAP and extract latent vars 1-2 | |
| embedding = pd.DataFrame(brain_umap.fit_transform(matrix), columns = ['UMAP1','UMAP2']) | |
| # Produce sns.scatterplot and pass metadata.subclasses as color | |
| sns_plot = sns.scatterplot(x='UMAP1', y='UMAP2', data=embedding, | |
| hue=metadata.subclass_label.to_list(), | |
| alpha=.1, linewidth=0, s=1) | |
| # Adjust legend | |
| sns_plot.legend(loc='center left', bbox_to_anchor=(1, .5)) | |
| # Save PNG | |
| sns_plot.figure.savefig('umap_scatter.png', bbox_inches='tight', dpi=500) |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment