Bulk Rna Seq Batch Correction With Combat

• 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.

• 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.

• 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')).

• For per-sample variance checks, draw before/after boxplots and recolour boxes using ov.pl.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.pl.embedding(..., basis='raw|original|X_pca', color='batch', frameon='small') and repeat fo r the corrected layer to verify mixing.

# Before ComBat: verify batch column exists and has >1 batch
assert 'batch' in adata.obs.columns, "adata.obs must contain a 'batch' column"
n_batches = adata.obs['batch'].nunique()
assert n_batches > 1, f"Only {n_batches} batch — need >1 for batch correction"
# Verify gene overlap after concatenation
if adata.n_vars < 100:
    print(f"WARNING: Only {adata.n_vars} shared genes after concat — check gene ID harmonization")

• 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.