Highly Effective Batch Effect Correction Method for RNA-seq Count Data.

Area of Science:

• Transcriptomics
• Bioinformatics
• Statistical genetics

Background:

• RNA sequencing (RNA-seq) is crucial for gene expression analysis but is susceptible to batch effects.
• Batch effects are systematic variations that reduce data reliability and obscure biological signals.
• Accurate batch effect correction is vital for robust transcriptomic analysis.

Approach:

• Introduced ComBat-ref, a refined batch effect correction method for RNA-seq data.
• Utilized a negative binomial model with a pooled dispersion parameter for batch adjustment.
• Preserved count data from a reference batch to enhance statistical power.

Key Points:

• ComBat-ref demonstrated superior performance in simulated and real-world datasets (GFRN, NASA GeneLab).
• Significantly improved sensitivity and specificity in differential expression analysis compared to existing methods.
• Effectively mitigated batch effects while maintaining high detection power.

Conclusions:

• ComBat-ref offers a robust tool for enhancing RNA-seq data accuracy and interpretability.
• The method improves statistical power for reliable differential expression analysis.
• Facilitates deeper understanding of biological variations in transcriptomic studies.