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single-cell-downstream-analysis

Maintainer FreedomIntelligence · Last updated April 1, 2026

Checklist-style reference for OmicVerse downstream tutorials covering AUCell scoring, metacell DEG, and related exports.

OpenClawNanoClawAnalysisReproductionsingle-downstream-analysis🔬 omics & computational biologysingle-cell & spatial omicschecklist

Original source

FreedomIntelligence/OpenClaw-Medical-Skills

https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/single-downstream-analysis

Maintainer
FreedomIntelligence
License
MIT
Last updated
April 1, 2026

Skill Snapshot

Key Details From SKILL.md

2 min

Key Notes

  • 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.
  • 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.

Source Doc

Excerpt From SKILL.md

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.

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.

Use cases

  • Use single-cell-downstream-analysis for single-cell or spatial omics analysis.
  • Apply single-cell-downstream-analysis to clustering, integration, or trajectory workflows.

Not for

  • Do not rely on this catalog entry alone for installation or maintenance details.

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