Version Compatibility

Reference examples tested with: scanpy 1.10+, anndata 0.10+, scikit-learn 1.4+, umap-learn 0.5+, openTSNE 1.0+, phate 1.0+, ggplot2 3.5+, PCAtools 2.16+, matplotlib 3.8+.

Before using code patterns, verify installed versions match. If versions differ:

• Python: pip show <package> then help(module.function) to check signatures
• R: packageVersion('<pkg>') then ?function_name to verify parameters

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

Dimensionality-Reduction Plots

"Make a PCA / UMAP / t-SNE plot" -> Choose a projection method aligned with what the plot must reveal — variance explained (PCA), local neighborhood structure (t-SNE), manifold approximation with some global structure (UMAP), or continuous transitions (PHATE). Set hyperparameters deliberately. Communicate the projection's limits and refuse to over-interpret 2D distances.

• Python: sklearn.decomposition.PCA, openTSNE, umap-learn, phate, scanpy.tl.umap / scanpy.tl.tsne / scanpy.tl.pca
• R: prcomp, PCAtools::pca, Seurat::RunPCA / RunUMAP / RunTSNE, phateR

The Single Most Important Modern Insight -- 2D Embeddings Distort