OpenClaw-Medical-Skills bulk-rna-seq-batch-correction-with-combat

Use omicverse's pyComBat wrapper to remove batch effects from merged bulk RNA-seq or microarray cohorts, export corrected matrices, and benchmark pre/post correction visualisations.

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 "$T/skills/bulk-combat-correction" ~/.claude/skills/freedomintelligence-openclaw-medical-skills-bulk-rna-seq-batch-correction-with-c && rm -rf "$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 "$T/skills/bulk-combat-correction" ~/.openclaw/skills/freedomintelligence-openclaw-medical-skills-bulk-rna-seq-batch-correction-with-c && rm -rf "$T"

manifest: skills/bulk-combat-correction/SKILL.md

Bulk RNA-seq batch correction with ComBat

Overview

Apply this skill when a user has multiple bulk expression matrices measured across different batches and needs to harmonise them before downstream analysis. It follows

t_bulk_combat.ipynb

, w hich demonstrates the pyComBat workflow on ovarian cancer microarray cohorts.

Instructions

Import core libraries
- Load
```
omicverse as ov
```
  ,
```
anndata
```
  ,
```
pandas as pd
```
  , and
```
matplotlib.pyplot as plt
```
  .
- Call
```
ov.ov_plot_set()
```
  (aliased
```
ov.plot_set()
```
  in some releases) to align figures with omicverse styling.
Load each batch separately
- Read the prepared pickled matrices (or user-provided expression tables) with
```
pd.read_pickle(...)
```
  /
```
pd.read_csv(...)
```
  .
- Transpose to gene × sample before wrapping them in
```
anndata.AnnData
```
  objects so
```
adata.obs
```
  stores sample metadata.
- Assign a
```
batch
```
  column for every cohort (
```
adata.obs['batch'] = '1'
```
  ,
```
'2'
```
  , ...). Encourage descriptive labels when availa ble.
Concatenate on shared genes
- Use
```
anndata.concat([adata1, adata2, adata3], merge='same')
```
  to retain the intersection of genes across batches.
- Confirm the combined
```
adata
```
  reports balanced sample counts per batch; if not, prompt users to re-check inputs.
Run ComBat batch correction
- Execute
```
ov.bulk.batch_correction(adata, batch_key='batch')
```
  .
- Explain that corrected values are stored in
```
adata.layers['batch_correction']
```
  while the original counts remain in
```
adata.X
```
  .
Export corrected and raw matrices
- Obtain DataFrames via
```
adata.to_df().T
```
  (raw) and
```
adata.to_df(layer='batch_correction').T
```
  (corrected).
- Encourage saving both tables (
```
.to_csv(...)
```
  ) plus the harmonised AnnData (
```
adata.write_h5ad('adata_batch.h5ad', compressio n='gzip')
```
  ).
Benchmark the correction
- For per-sample variance checks, draw before/after boxplots and recolour boxes using
```
ov.utils.red_color
```
  ,
```
blue_color
```
  ,
```
gree n_color
```
  palettes to match batches.
- Copy raw counts to a named layer with
```
adata.layers['raw'] = adata.X.copy()
```
  before PCA.
- Run
```
ov.pp.pca(adata, layer='raw', n_pcs=50)
```
  and
```
ov.pp.pca(adata, layer='batch_correction', n_pcs=50)
```
  .
- Visualise embeddings with
```
ov.utils.embedding(..., basis='raw|original|X_pca', color='batch', frameon='small')
```
  and repeat fo r the corrected layer to verify mixing.
Troubleshooting tips
- Mismatched gene identifiers cause dropped features—remind users to harmonise feature names (e.g., gene symbols) before conca tenation.
- pyComBat expects log-scale intensities or similarly distributed counts; recommend log-transforming strongly skewed matrices.
- If
```
batch_correction
```
  layer is missing, ensure the
```
batch_key
```
  matches the column name in
```
adata.obs
```
  .

Examples

"Combine three GEO ovarian cohorts, run ComBat, and export both the raw and corrected CSV matrices."
"Plot PCA embeddings before and after batch correction to confirm that batches 1–3 overlap."
"Save the harmonised AnnData file so I can reload it later for downstream DEG analysis."

References

Tutorial notebook:
```
t_bulk_combat.ipynb
```

Example inputs:

omicverse_guide/docs/Tutorials-bulk/data/combat/

Quick copy/paste commands:
```
reference.md
```