OpenClaw-Medical-Skills single-cell-downstream-analysis

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

install

source · Clone the upstream repo

git clone https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/single-downstream-analysis" ~/.claude/skills/freedomintelligence-openclaw-medical-skills-single-cell-downstream-analysis && rm -rf "$T"

OpenClaw · Install into ~/.openclaw/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills "$T" && mkdir -p ~/.openclaw/skills && cp -r "$T/skills/single-downstream-analysis" ~/.openclaw/skills/freedomintelligence-openclaw-medical-skills-single-cell-downstream-analysis && rm -rf "$T"

manifest: skills/single-downstream-analysis/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
```
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.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.

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

)

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:

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.

OpenClaw-Medical-Skills single-cell-downstream-analysis

Single-cell downstream analysis quick-reference

AUCell pathway scoring (
`t_aucell.ipynb`
)

scRNA-seq DEG (bulk-style meta cell) (
`t_scdeg.ipynb`
)

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

scDrug response prediction (
`t_scdrug.ipynb`
)

SCENIC regulon discovery (
`t_scenic.ipynb`
)

cNMF program discovery (
`t_cnmf.ipynb`
)

NOCD overlapping communities (
`t_nocd.ipynb`
)

Lazy pipeline & reporting (
`t_lazy.ipynb`
)

OpenClaw-Medical-Skills single-cell-downstream-analysis

Single-cell downstream analysis quick-reference

AUCell pathway scoring (t_aucell.ipynb)

scRNA-seq DEG (bulk-style meta cell) (t_scdeg.ipynb)

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

scDrug response prediction (t_scdrug.ipynb)

SCENIC regulon discovery (t_scenic.ipynb)

cNMF program discovery (t_cnmf.ipynb)

NOCD overlapping communities (t_nocd.ipynb)

Lazy pipeline & reporting (t_lazy.ipynb)

AUCell pathway scoring (
`t_aucell.ipynb`
)

scRNA-seq DEG (bulk-style meta cell) (
`t_scdeg.ipynb`
)

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

scDrug response prediction (
`t_scdrug.ipynb`
)

SCENIC regulon discovery (
`t_scenic.ipynb`
)

cNMF program discovery (
`t_cnmf.ipynb`
)

NOCD overlapping communities (
`t_nocd.ipynb`
)

Lazy pipeline & reporting (
`t_lazy.ipynb`
)