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

ABSTRACT:

Motivation

Systems biology analyses often use correlations in gene expression profiles to infer co-expression networks that are then used as input for gene regulatory network inference or to identify functional modules of co-expressed or putatively co-regulated genes. While systematic biases, including batch effects, are known to induce spurious associations and confound differential gene expression analyses (DE), the impact of batch effects on gene co-expression has not been fully explored. Methods have been developed to adjust expression values, ensuring conditional independence of mean and variance from batch or other covariates for each gene, resulting in improved fidelity of DE analysis. However, such adjustments do not address the potential for spurious differential co-expres

SUBMITTER: Micheletti S 

PROVIDER: S-EPMC11441315 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature