Higher-order correction of persistent batch effects in correlation networks.

Area of Science:

• Genomics
• Systems Biology
• Bioinformatics

Background:

• Systems biology frequently infers gene co-expression networks from gene expression data to identify functional modules and regulatory relationships.
• Batch effects are known to introduce systematic biases, confounding differential gene expression (DE) analysis, but their impact on gene co-expression remains underexplored.
• Standard batch correction methods improve DE analysis but do not fully address spurious differential co-expression (DC), potentially leading to artifactual associations.

Purpose of the Study:

• To investigate the impact of batch effects on gene co-expression analysis.
• To develop a method for correcting batch effects in gene co-expression matrices.
• To improve the accuracy of gene regulatory network inference and functional module identification.

Main Methods:

• Demonstrated the persistence of confounding in covariance after standard batch correction using synthetic and real-world data.
• Introduced Co-expression Batch Reduction Adjustment (COBRA), a method for computing batch-corrected gene co-expression matrices.
• COBRA estimates a conditional covariance matrix, controlling for continuous and categorical covariates.

Main Results:

• Standard batch correction methods do not eliminate confounders in gene co-expression covariance.
• COBRA effectively computes batch-corrected gene co-expression matrices.
• COBRA leverages genomic data's modular structure for efficient and accurate association estimation.

Conclusions:

• Batch effects significantly impact gene co-expression networks, leading to false biological associations.
• COBRA provides a robust solution for batch effect correction in gene co-expression analysis.
• This method enhances the reliability of gene regulatory network inference and functional genomics studies.