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SKILL.md
Single-cell downstream analysis quick-reference
This skill sheet distills the OmicVerse single-cell downstream tutorials into an executable checklist. Each module
highlights prerequisites, the core API entry points, interpretation checkpoints, resource planning notes, and
any optional validation or export steps surfaced in the notebooks.
AUCell pathway scoring (t_aucell.ipynb)
Prerequisites
Download pathway collections (GO, KEGG, or custom) that match the organism under study before running the tutorial.
Ensure an AnnData object with clustering/embedding (adata.obsm['X_umap']) is prepared.
Core calls
ov.single.geneset_aucell for one pathway; ov.single.pathway_aucell for multiple pathways.
ov.single.pathway_aucell_enrichment to score all pathways in a library (set num_workers for parallelism).
Result checks
Interpret AUCell scores as expression-like values (0–1). Use sc.pl.embedding to confirm pathway activity patterns.
Run sc.tl.rank_genes_groups on the AUCell AnnData to find cluster-enriched pathways and visualize with
sc.pl.rank_genes_groups_dotplot.
Resources
Library-wide scoring can be CPU-intensive; allocate workers (num_workers=8 in tutorial) and sufficient memory for the
dense AUCell matrix.
Optional validation / exports
Persist scores with adata_aucs.write_h5ad('...') for reuse.
Plot enriched pathways via ov.single.pathway_enrichment and heatmaps.
ov.single.pathway_enrichment_plot
scRNA-seq DEG (bulk-style meta cell) (t_scdeg.ipynb)
Prerequisites
Run quality control and preprocessing (ov.pp.qc, ov.pp.preprocess, ov.pp.scale, ov.pp.pca).
Retain raw counts in adata.raw before HVG filtering.
Core calls
Construct differential objects with ov.bulk.pyDEG(test_adata.to_df(...).T) for full-cell and metacell views.
Build metacells via ov.single.MetaCell(..., use_gpu=True) when GPU is available for acceleration.
Result checks
Inspect volcano plots (dds.plot_volcano) and targeted boxplots (dds.plot_boxplot) for top DEGs.
Map DEG markers back to UMAP embeddings using ov.utils.embedding to confirm localization.
Resources
Metacell construction benefits from GPU but can fall back to CPU; ensure enough memory for transposed dense matrices
passed to pyDEG.
Optional validation / exports
Save metacell embeddings with matplotlib figures; adjust legend_* settings for publication-ready visuals.
Extract results: cnmf_obj.load_results(...), cnmf_obj.get_results(...), optional RF classifier via get_results_rfc.
Result checks
Evaluate stability via K-selection plot and local density histogram; confirm chosen K with consensus heatmaps.
Inspect topic usage embeddings (ov.pl.embedding), cluster labels, and dotplots of top genes.
Resources
Multiple iterations and components are CPU-heavy; consider distributing workers (total_workers) and verifying disk
space for intermediate factorization files.
Optional validation / exports
Visualizations include Euclidean distance heatmaps, density histograms, UMAP overlays for topics/clusters, and dotplots.
NOCD overlapping communities (t_nocd.ipynb)
Prerequisites
Prepare AnnData via ov.single.scanpy_lazy (automated preprocessing) before running NOCD.
Note: Tutorial warns NOCD implementation is under active development—expect variability.
Core calls
Pipeline wrapper: scbrca = ov.single.scnocd(adata) followed by chained methods (matrix_transform, matrix_normalize,
GNN_configure, GNN_preprocess, GNN_model, GNN_result, GNN_plot, cal_nocd, calculate_nocd).
Result checks
Compare standard Leiden clusters versus NOCD outputs on UMAP embeddings to identify multi-fate cells.
Resources
Graph neural network stages can be GPU-accelerated; ensure CUDA availability or be prepared for longer CPU runtimes.
Track memory usage when constructing large adjacency matrices.
Optional validation / exports
Generate multiple UMAP overlays (sc.pl.umap) for nocd, nocd_n, and Leiden labels using shared color maps.
Lazy pipeline & reporting (t_lazy.ipynb)
Prerequisites
Install OmicVerse ≥1.7.0 with lazy utilities; supported species currently human/mouse.
Prepare batch metadata (sample_key) and optionally initialize hybrid compute (ov.settings.cpu_gpu_mixed_init()).
Core calls
Turnkey preprocessing: ov.single.lazy(adata, species='mouse', sample_key='batch', ...) with optional reforce_steps
and module-specific kwargs.
Reporting: ov.single.generate_scRNA_report(...) to build HTML summary; ov.generate_reference_table(adata) for
citation tracking.
Result checks
Inspect generated embeddings (ov.pl.embedding) for quality and annotation alignment.
Review HTML report for QC metrics, normalization, batch correction, and embeddings.
Resources
Steps like Harmony or scVI may invoke GPU; confirm hardware availability or adjust reforce_steps accordingly.
Report generation writes to disk; ensure output path is writable.
Optional validation / exports
Customize embeddings by color key; store HTML report and reference table alongside project documentation.
