What is ComBat in the context of RNA-seq analysis?

ComBat is an empirical Bayes-based algorithm designed to adjust for technical batch effects in genomic data while preserving biological variation.

What file formats does this skill work with?

It primarily works with CSV and Pickle files for input and can export results as CSVs or AnnData .h5ad files.

How does the skill handle different gene identifiers?

The skill includes instructions for harmonizing gene symbols and identifiers during the concatenation process to prevent data loss.

How do I verify if the batch correction was successful?

The skill provides a benchmarking workflow using PCA plots to visualize sample mixing before and after the correction process.

Can I use this skill for single-cell data?

While omicverse supports single-cell analysis, this specific skill is optimized for bulk RNA-seq and microarray expression matrices.

Bulk RNA-seq Batch Correction

Name: Bulk RNA-seq Batch Correction
Author: Starlitnightly

byStarlitnightly

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813

데이터 과학 및 ML

Removes batch effects from merged bulk RNA-seq or microarray cohorts using the ComBat algorithm to ensure data consistency for downstream analysis.

소개

This skill provides a specialized workflow for harmonizing multi-cohort bulk expression data using the omicverse pyComBat wrapper. It guides users through loading expression matrices, transposing data for AnnData compatibility, and performing empirical Bayes batch correction. The skill also includes rigorous benchmarking steps, such as pre- and post-correction PCA visualizations and variance boxplots, to verify that technical variation is successfully mitigated while preserving biological signals.

주요 기능

Seamless conversion between Pandas DataFrames and AnnData objects
Automated ComBat integration via the pyComBat wrapper
Batch-specific variance checks with custom omicverse palettes
Pre- and post-correction PCA benchmarking and visualization
813 GitHub stars
Support for exporting both raw and corrected expression matrices

사용 사례

Removing technical variation between different sequencing runs or lab batches
Merging multiple GEO cohorts for unified meta-analysis of disease expression
Preparing harmonized datasets for accurate differential gene expression (DEG) studies

소개

주요 기능

Seamless conversion between Pandas DataFrames and AnnData objects
Automated ComBat integration via the pyComBat wrapper
Batch-specific variance checks with custom omicverse palettes
Pre- and post-correction PCA benchmarking and visualization
813 GitHub stars
Support for exporting both raw and corrected expression matrices

사용 사례

Removing technical variation between different sequencing runs or lab batches
Merging multiple GEO cohorts for unified meta-analysis of disease expression
Preparing harmonized datasets for accurate differential gene expression (DEG) studies