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.