bio-data-visualization-dimensionality-reduction-plots
PKU-YuanGroup/OpenAI4SProduce and interpret PCA, t-SNE, UMAP, and PHATE plots for high-dimensional omics data with rigor about which method preserves what (variance, local…
Scores out of 100 · grade A
2026-08-22Works
40% of the score100/100
- Loads cleanly: valid frontmatter, required fields present, no dangling references.
Maintained
25% of the score100/100
- no commits in the last 12 weeks
Adopted
20% of the score35/100
- 335 stars on the source repo.
Documented
15% of the score100/100
- 2,512 words with worked examples.
- Ships 2 bundled files.
Install
npx skills add PKU-YuanGroup/OpenAI4S/bio-data-visualization-dimensionality-reduction-plotsWhat it says it does
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.
Also in PKU-YuanGroup/OpenAI4S
| Artifact | Score | What the check found | Type | Reach | Last commit |
|---|---|---|---|---|---|
| bio-atac-seq-allele-specific-accessibilityPKU-YuanGroup/OpenAI4S | clean | Skill | 335 stars | today | |
| bio-atac-seq-atac-peak-callingPKU-YuanGroup/OpenAI4S | clean | Skill | 335 stars | today | |
| bio-atac-seq-consensus-peaksetPKU-YuanGroup/OpenAI4S | clean | Skill | 335 stars | today | |
| bio-atac-seq-deep-learning-atacPKU-YuanGroup/OpenAI4S | clean | Skill | 335 stars | today | |
| bio-atac-seq-footprintingPKU-YuanGroup/OpenAI4S | clean | Skill | 335 stars | today | |
| bio-causal-genomics-effector-gene-prioritizationPKU-YuanGroup/OpenAI4S | clean | Skill | 335 stars | today |
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