OpenClaw-Medical-Skills single-cell-annotation-skills-with-omicverse

Guide Claude through SCSA, MetaTiME, CellVote, CellMatch, GPTAnno, and weighted KNN transfer workflows for annotating single-cell modalities.

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 "$T/skills/single-annotation" ~/.claude/skills/freedomintelligence-openclaw-medical-skills-single-cell-annotation-skills-with-o && rm -rf "$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 "$T/skills/single-annotation" ~/.openclaw/skills/freedomintelligence-openclaw-medical-skills-single-cell-annotation-skills-with-o && rm -rf "$T"

manifest: skills/single-annotation/SKILL.md

Single-cell annotation skills with omicverse

Overview

Use this skill to reproduce and adapt the single-cell annotation playbook captured in omicverse tutorials: SCSA

t_cellanno.ipynb

, MetaTiME

t_metatime.ipynb

, CellVote

t_cellvote.md

t_cellvote_pbmc3k.ipynb

, CellMatch

t_cellmatch.ipynb

, GPTAnno

t_gptanno.ipynb

, and label transfer

t_anno_trans.ipynb

. Each section below highlights required inputs, training/inference steps, and how to read the outputs.

Instructions

SCSA automated cluster annotation
- Data requirements: PBMC3k raw counts from 10x Genomics (
```
pbmc3k_filtered_gene_bc_matrices.tar.gz
```
  ) or the processed
```
sample/rna.h5ad
```
  . Download instructions are embedded in the notebook; unpack to
```
data/filtered_gene_bc_matrices/hg19/
```
  . Ensure an SCSA SQLite database is available (e.g.
```
pySCSA_2024_v1_plus.db
```
  from the Figshare/Drive links listed in the tutorial) and point
```
model_path
```
  to its location.
- Preprocessing & model fit: Load with
```
sc.read_10x_mtx
```
  , run QC (
```
ov.pp.qc
```
  ), normalization and HVG selection (
```
ov.pp.preprocess
```
  ), scaling (
```
ov.pp.scale
```
  ), PCA (
```
ov.pp.pca
```
  ), neighbors, Leiden clustering, and compute rank markers (
```
sc.tl.rank_genes_groups
```
  ). Instantiate
```
scsa = ov.single.pySCSA(...)
```
  choosing
```
target='cellmarker'
```
  or
```
'panglaodb'
```
  , tissue scope, and thresholds (
```
foldchange
```
  ,
```
pvalue
```
  ).
- Inference & interpretation: Call
```
scsa.cell_anno(clustertype='leiden', result_key='scsa_celltype_cellmarker')
```
  or
```
scsa.cell_auto_anno
```
  to append predictions to
```
adata.obs
```
  . Compare to manual marker-based labels via
```
ov.utils.embedding
```
  or
```
sc.pl.dotplot
```
  , inspect marker dictionaries (
```
ov.single.get_celltype_marker
```
  ), and query supported tissues with
```
scsa.get_model_tissue()
```
  . Use the ROI/ROE helpers (
```
ov.utils.roe
```
  ,
```
ov.utils.plot_cellproportion
```
  ) to validate abundance trends.
MetaTiME tumour microenvironment states
- Data requirements: Batched TME AnnData with an scVI latent embedding. The tutorial uses
```
TiME_adata_scvi.h5ad
```
  from Figshare (
```
https://figshare.com/ndownloader/files/41440050
```
  ). If starting from counts, run scVI (
```
scvi.model.SCVI
```
  ) first to populate
```
adata.obsm['X_scVI']
```
  .
- Preprocessing & model fit: Optionally subset to non-malignant cells via
```
adata.obs['isTME']
```
  . Rebuild neighbors on the latent representation (
```
sc.pp.neighbors(adata, use_rep="X_scVI")
```
  ) and embed with pymde (
```
adata.obsm['X_mde'] = ov.utils.mde(...)
```
  ). Initialise
```
TiME_object = ov.single.MetaTiME(adata, mode='table')
```
  and, if finer granularity is desired, over-cluster with
```
TiME_object.overcluster(resolution=8, clustercol='overcluster')
```
  .
- Inference & interpretation: Run
```
TiME_object.predictTiME(save_obs_name='MetaTiME')
```
  to assign minor states and
```
Major_MetaTiME
```
  . Visualise using
```
TiME_object.plot
```
  or
```
sc.pl.embedding
```
  . Interpret the outputs by comparing cluster-level distributions and confirming that MetaTiME and Major_MetaTiME columns align with expected niches.
CellVote consensus labelling
- Data requirements: A clustered AnnData (e.g. PBMC3k stored as
```
CELLVOTE_PBMC3K
```
  env var or
```
data/pbmc3k.h5ad
```
  ) plus at least two precomputed annotation columns (simulated in the tutorial as
```
scsa_annotation
```
  ,
```
gpt_celltype
```
  ,
```
gbi_celltype
```
  ). Prepare per-cluster marker genes via
```
sc.tl.rank_genes_groups
```
  .
- Preprocessing & model fit: After standard preprocessing (normalize, log1p, HVGs, PCA, neighbors, Leiden) build a marker dictionary
```
marker_dict = top_markers_from_rgg(adata, 'leiden', topn=10)
```
  or via
```
ov.single.get_celltype_marker
```
  . Instantiate
```
cv = ov.single.CellVote(adata)
```
  .
- Inference & interpretation: Call
```
cv.vote(clusters_key='leiden', cluster_markers=marker_dict, celltype_keys=[...], species='human', organization='PBMC', provider='openai', model='gpt-4o-mini')
```
  . Offline examples monkey-patch arbitration to avoid API calls; online voting requires valid credentials. Final consensus labels live in
```
adata.obs['CellVote_celltype']
```
  . Compare each cluster’s majority vote with the input sources (
```
adata.obs[['leiden', 'scsa_annotation', ...]]
```
  ) to justify decisions.
CellMatch ontology mapping
- Data requirements: Annotated AnnData such as
```
pertpy.dt.haber_2017_regions()
```
  with
```
adata.obs['cell_label']
```
  . Download Cell Ontology JSON (
```
cl.json
```
  ) via
```
ov.single.download_cl(...)
```
  or manual links, and optionally Cell Taxonomy resources (
```
Cell_Taxonomy_resource.txt
```
  ). Ensure access to a SentenceTransformer model (
```
sentence-transformers/all-MiniLM-L6-v2
```
  ,
```
BAAI/bge-base-en-v1.5
```
  , etc.), downloading to
```
local_model_dir
```
  if offline.
- Preprocessing & model fit: Create the mapper with
```
ov.single.CellOntologyMapper(cl_obo_file='new_ontology/cl.json', model_name='sentence-transformers/all-MiniLM-L6-v2', local_model_dir='./my_models')
```
  . Run
```
mapper.map_adata(...)
```
  to assign ontology-derived labels/IDs, optionally enabling taxonomy matching (
```
use_taxonomy=True
```
  after calling
```
load_cell_taxonomy_resource
```
  ).
- Inference & interpretation: Explore mapping summaries (
```
mapper.print_mapping_summary_taxonomy
```
  ) and inspect embeddings coloured by
```
cell_ontology
```
  ,
```
cell_ontology_cl_id
```
  , or
```
enhanced_cell_ontology
```
  . Use helper queries such as
```
mapper.find_similar_cells('T helper cell')
```
  ,
```
mapper.get_cell_info(...)
```
  , and category browsing to validate ontology coverage.
GPTAnno LLM-powered annotation
- Data requirements: The same PBMC3k dataset (raw matrix or
```
.h5ad
```
  ) and cluster assignments. Access to an LLM endpoint—configure
```
AGI_API_KEY
```
  for OpenAI-compatible providers (
```
provider='openai'
```
  ,
```
'qwen'
```
  ,
```
'kimi'
```
  , etc.), or supply a local model path for
```
ov.single.gptcelltype_local
```
  .
- Preprocessing & model fit: Follow the QC, normalization, HVG, scaling, PCA, neighbor, Leiden, and marker discovery steps described above (reusing outputs from the SCSA workflow). Build the marker dictionary automatically with
```
ov.single.get_celltype_marker(adata, clustertype='leiden', rank=True, key='rank_genes_groups', foldchange=2, topgenenumber=5)
```
  .
- Inference & interpretation: Invoke
```
ov.single.gptcelltype(...)
```
  specifying tissue/species context and desired provider/model. Post-process responses to keep clean labels (
```
result[key].split(': ')[-1]...
```
  ) and write them to
```
adata.obs['gpt_celltype']
```
  . Compare embeddings (
```
ov.pl.embedding(..., color=['leiden','gpt_celltype'])
```
  ) to verify cluster identities. If operating offline, call
```
ov.single.gptcelltype_local
```
  with a downloaded instruction-tuned checkpoint.
Weighted KNN annotation transfer
- Data requirements: Cross-modal GLUE outputs with aligned embeddings, e.g.
```
data/analysis_lymph/rna-emb.h5ad
```
  (annotated RNA) and
```
data/analysis_lymph/atac-emb.h5ad
```
  (query ATAC) where both contain
```
obsm['X_glue']
```
  .
- Preprocessing & model fit: Load both modalities, optionally concatenate for QC plots, and compute a shared low-dimensional embedding with
```
ov.utils.mde
```
  . Train a neighbour model using
```
ov.utils.weighted_knn_trainer(train_adata=rna, train_adata_emb='X_glue', n_neighbors=15)
```
  .
- Inference & interpretation: Transfer labels via
```
labels, uncert = ov.utils.weighted_knn_transfer(query_adata=atac, query_adata_emb='X_glue', label_keys='major_celltype', knn_model=knn_transformer, ref_adata_obs=rna.obs)
```
  . Store predictions in
```
atac.obs['transf_celltype']
```
  and uncertainties in
```
atac.obs['transf_celltype_unc']
```
  ; copy to
```
major_celltype
```
  if you want consistent naming. Visualise (
```
ov.utils.embedding
```
  ) and inspect uncertainty to flag ambiguous cells.

Critical API Reference - EXACT Function Signatures

pySCSA - IMPORTANT: Parameter is

clustertype

, NOT

cluster

CORRECT usage:

# Step 1: Initialize pySCSA
scsa = ov.single.pySCSA(
    adata,
    foldchange=1.5,
    pvalue=0.01,
    species='Human',
    tissue='All',
    target='cellmarker'  # or 'panglaodb'
)

# Step 2: Run annotation - NOTE: use clustertype='leiden', NOT cluster='leiden'!
anno_result = scsa.cell_anno(clustertype='leiden', cluster='all')

# Step 3: Add cell type labels to adata.obs
scsa.cell_auto_anno(adata, clustertype='leiden', key='scsa_celltype')
# Results are stored in adata.obs['scsa_celltype']

WRONG - DO NOT USE:

# WRONG! 'cluster' is NOT a valid parameter for cell_auto_anno!
# scsa.cell_auto_anno(adata, cluster='leiden')  # ERROR!

COSG Marker Genes - Results stored in adata.uns, NOT adata.obs

CORRECT usage:

# Step 1: Run COSG marker gene identification
ov.single.cosg(adata, groupby='leiden', n_genes_user=50)

# Step 2: Access results from adata.uns (NOT adata.obs!)
marker_names = adata.uns['rank_genes_groups']['names']  # DataFrame with cluster columns
marker_scores = adata.uns['rank_genes_groups']['scores']

# Step 3: Get top markers for specific cluster
cluster_0_markers = adata.uns['rank_genes_groups']['names']['0'][:10].tolist()

# Step 4: To create celltype column, manually map clusters to cell types
cluster_to_celltype = {
    '0': 'T cells',
    '1': 'B cells',
    '2': 'Monocytes',
}
adata.obs['cosg_celltype'] = adata.obs['leiden'].map(cluster_to_celltype)

WRONG - DO NOT USE:

# WRONG! COSG does NOT create adata.obs columns directly!
# adata.obs['cosg_celltype']  # This key does NOT exist after running COSG!
# adata.uns['cosg_celltype']  # This key also does NOT exist!

Common Pitfalls to Avoid

pySCSA parameter confusion:
- ```
clustertype
```
  = which obs column contains cluster labels (e.g., 'leiden')
- ```
cluster
```
  = which specific clusters to annotate ('all' or specific cluster IDs)
- These are DIFFERENT parameters!
COSG result access:
- COSG is a marker gene finder, NOT a cell type annotator
- Results are per-cluster gene rankings stored in
```
adata.uns['rank_genes_groups']
```
- To assign cell types, you must manually map clusters to cell types based on markers
Result storage patterns in OmicVerse:
- Cell type annotations →
```
adata.obs['<key>']
```
- Marker gene results →
```
adata.uns['<key>']
```
  (includes 'names', 'scores', 'logfoldchanges')
- Differential expression →
```
adata.uns['rank_genes_groups']
```

Examples

"Run SCSA with both CellMarker and PanglaoDB references on PBMC3k, then benchmark against manual marker assignments before feeding the results into CellVote."
"Annotate tumour microenvironment states in the MetaTiME Figshare dataset, highlight Major_MetaTiME classes, and export the label distribution per patient."
"Download Cell Ontology resources, map
```
haber_2017_regions
```
clusters to ontology terms, and enrich ambiguous clusters using Cell Taxonomy hints."
"Propagate RNA-derived
```
major_celltype
```
labels onto GLUE-integrated ATAC cells and report clusters with high transfer uncertainty."

References

Tutorials and notebooks:

t_cellanno.ipynb

t_metatime.ipynb

t_cellvote.md

t_cellvote_pbmc3k.ipynb

t_cellmatch.ipynb

t_gptanno.ipynb

t_anno_trans.ipynb

Sample data & assets: PBMC3k matrix from 10x Genomics, MetaTiME
```
TiME_adata_scvi.h5ad
```
(Figshare), SCSA database downloads, GLUE embeddings under
```
data/analysis_lymph/
```
, Cell Ontology
```
cl.json
```
, and Cell Taxonomy resource.
Quick copy commands:
```
reference.md
```
.