single-cell-downstream-analysis

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.

Defensive Validation Patterns

Before running any downstream module, verify prerequisites:

# Before AUCell: verify embeddings exist
assert 'X_umap' in adata.obsm or 'X_pca' in adata.obsm, \
    "Embedding required. Run ov.pp.umap(adata) or ov.pp.pca(adata) first."

# Before metacell DEG: verify raw counts are preserved
assert adata.raw is not None, "adata.raw required. Set adata.raw = adata.copy() before HVG filtering."

# Before SCENIC: verify raw counts (not log-transformed) are available
if hasattr(adata.X, 'max') and adata.X.max() < 20:
    print("WARNING: SCENIC expects raw counts. Data may be log-transformed.")

# Before scDrug: verify tumor annotations
# assert 'cell_type' in adata.obs.columns, "Cell type annotation required for scDrug"

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 ov.single.pathway_enrichment_plot heatmaps.

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.pl.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.

scRNA-seq DEG (cell-type & composition) (t_deg_single.ipynb)

• Prerequisites
  • Annotated adata with condition, cell_label, and optional batch metadata.
  • Initialize mixed CPU/GPU resources when using graph-based DA methods (ov.settings.cpu_gpu_mixed_init()).
• Core calls
  • ov.single.DEG(..., method='wilcoxon'|'t-test'|'memento-de') with deg_obj.run(...) to target cell types.
  • ov.single.DCT(..., method='sccoda'|'milo') for differential composition testing.
  • Graph setup for Milo: ov.pp.preprocess, ov.single.batch_correction, ov.pp.neighbors, ov.pp.umap.
• Result checks
  • Review DEG tables from deg_obj (Wilcoxon / memento) and adjust capture rate / bootstraps for stability.
  • For scCODA, tune FDR via sim_results.set_fdr(); interpret boxplots with condition-level shifts.
  • Milo diagnostics: histogram of P-values, logFC vs –log10 FDR scatter, beeswarm of differential abundance.
• Resources
  • Memento and Milo require multiple CPUs (num_cpus, num_boot, high k); ensure adequate compute time.
  • Harmony/scVI batch correction needs GPU memory when enabled; plan for VRAM usage.
• Optional validation / exports
  • Visual diagnostics include UMAP overlays (ov.pl.embedding), Milo beeswarm plots, and custom color palettes.

scDrug response prediction (t_scdrug.ipynb)

• Prerequisites
  • Fetch tumor-focused dataset (e.g., infercnvpy.datasets.maynard2020_3k).
  • Download reference assets before running predictions:
    • Gene annotations via ov.utils.get_gene_annotation (requires GTF from GENCODE or T2T-CHM13).
    • ov.utils.download_GDSC_data() and ov.utils.download_CaDRReS_model() for drug-response models.
    • Clone CaDRReS-Sc repo (git clone https://github.com/CSB5/CaDRReS-Sc).
• Core calls
  • Tumor resolution detection: ov.single.autoResolution(adata, cpus=4).
  • Drug response runner: ov.single.Drug_Response(adata, scriptpath='CaDRReS-Sc', modelpath='models/', output='result').
• Result checks
  • Inspect clustering and IC50 outputs stored under output; cross-reference with inferred CNV states.
• Resources
  • Requires external CaDRReS-Sc environment (Python/R dependencies) and storage for model downloads.
  • Running inferCNV preprocessing may need multiple CPUs and substantial RAM.
• Optional validation / exports
  • Persist intermediate AnnData (adata.write('scanpyobj.h5ad')) to reuse for downstream analyses or re-runs.

SCENIC regulon discovery (t_scenic.ipynb)

For comprehensive SCENIC guidance (database downloads, RegDiffusion tuning, RSS interpretation, GRN visualization), use search_skills('SCENIC regulon GRN') to load the dedicated SCENIC skill.

• Prerequisites
  • Mouse hematopoiesis dataset loaded via ov.single.mouse_hsc_nestorowa16() (or provide preprocessed data with raw counts).
  • Download cisTarget ranking databases (*.feather) and motif annotations (motifs-*.tbl) for the species; allocate

    3 GB disk space and verify paths (db_glob, motif_path).

• Core calls
  • Initialize analysis: ov.single.SCENIC(adata, db_glob=..., motif_path=..., n_jobs=12).
  • Run RegDiffusion-based GRN inference, regulon pruning, and AUCell scoring via the SCENIC object methods.
• Result checks
  • Examine regulon activity matrices (scenic_obj.auc_mtx.head()), RSS scores, and embeddings colored by regulon activity.
  • Use RSS plots, dendrograms, and AUCell distributions to interpret TF specificity and activity thresholds.
• Resources
  • Multi-core CPU recommended (n_jobs matches available cores); ensure enough RAM for motif enrichment.
  • Large downloads and intermediate objects (pickle/h5ad) require disk space.
• Optional validation / exports
  • Save scenic_obj (ov.utils.save) and regulon AnnData (regulon_ad.write).
  • Optional plots: RSS per cell type, regulon embeddings, AUC histograms with threshold lines, GRN network visualizations.

cNMF program discovery (t_cnmf.ipynb)

• Prerequisites
  • Preprocess with HVG selection (ov.pp.preprocess), scaling (ov.pp.scale), PCA, and have UMAP embeddings for inspection.
  • Select component range (e.g., np.arange(5, 11)) and iterations; ensure output directory exists.
• Core calls
  • Instantiate analysis: ov.single.cNMF(..., output_dir='...', name='...').
  • Factorization workflow: cnmf_obj.factorize(...), cnmf_obj.combine(...), cnmf_obj.k_selection_plot(), cnmf_obj.consensus(...).
  • 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:

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