bulk-rna-seq-batch-correction-with-combat

name	bulk-rna-seq-batch-correction-with-combat
title	Bulk RNA-seq batch correction with ComBat
description	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.

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

1. 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.
2. 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.
3. 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.
4. 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.
5. 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')).
6. 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.
7. 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