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数据与复现单细胞与空间组学FreedomIntelligence/OpenClaw-Medical-Skills数据与复现
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single-cell-downstream-analysis

维护者 FreedomIntelligence · 最近更新 2026年4月1日

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

OpenClawNanoClaw分析处理复现实验single-downstream-analysis🔬 omics & computational biologysingle-cell & spatial omicschecklist

原始来源

FreedomIntelligence/OpenClaw-Medical-Skills

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

维护者
FreedomIntelligence
许可
MIT
最近更新
2026年4月1日

技能摘要

来自 SKILL.md 的关键信息

2 min

核心说明

  • This skill sheet distills OmicVerse single-cell downstream tutorials into executable checklist. Each module highlights prerequisites，core API entry points，interpretation checkpoints，resource planning notes，、 any optional validation 或 export steps surfaced in notebooks。
  • Prerequisites。
  • Download pathway collections (GO，KEGG，或 custom) that match organism under study before running tutorial。
  • Ensure AnnData object ，支持 聚类/embedding (adata.obsm['X_umap']) is prepared。
  • Core calls。

原始文档

SKILL.md 摘录

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.

适用场景

  • Use single-cell-downstream-analysis ，用于 single-cell 或 spatial omics analysis。
  • Apply single-cell-downstream-analysis to 聚类，integration，或 trajectory workflows。

不适用场景

  • Do not rely on this catalog entry alone ，用于 installation 或 maintenance details。

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