domains / families / data / dimensionality-reduction-plots

dimensionality-reduction-plots

separate

Produce and interpret PCA, t-SNE, UMAP, and PHATE plots for high-dimensional omics data with rigor about which method preserves what (variance, local structure, manifold, transitions), hyperparameter sensitivity, and the well-documented limits of 2D embeddings. Covers PCA biplot/scree/loadings, t-SNE PCA initialization (Kobak-Berens 2019), UMAP n_neighbors/min_dist trade-offs, and the Chari-Pachter 2023 critique. Use when visualizing high-dimensional data — bulk PCA, single-cell embeddings, multi-omics integration projections.

When to use

• visualizing high-dimensional data
• bulk PCA
• single-cell embeddings
• multi-omics integration projections
• PCA biplot/scree/loadings
• t-SNE PCA initialization
• UMAP n_neighbors/min_dist trade-offs
• interpreting 2D embedding limits

When not to use

• over-interpreting 2D distances
• reading 2D layout as biology
• inferring trajectories from UMAP gaps without validation

Skill page

Source