我的空间 / Skill 合集
金色时刻 · 15:48
让好资源，触手可及
← Skill 合集
AGENT SKILL

bulk-combat-correction

bulk-combat-correction 是面向数据与分析、研究的 Agent Skill，来自 Starlitnightly/omicverse。查看技能说明、操作步骤与使用约定，并通过原项目了解详细用法。

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.

数据与分析研究
访问 GitHub 原项目 ↗

SKILL.md

下载 MD ↓
namebulk-rna-seq-batch-correction-with-combat
titleBulk RNA-seq batch correction with ComBat
descriptionUse 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

来源：Starlitnightly/omicverse · GPL 许可 · 许可原文 · MD 为来源页保存的历史版本