BatchVaria: a variance-aware framework for evaluating batch correction in high-dimensional omics data (opens in new tab)

Batch effects and other unwanted technical sources of variation remain a persistent challenge in the integrative analysis of high-dimensional -omics data. Although established methods such as ComBat effectively mitigate batch-associated signal, their impact on biologically meaningful variation is frequently evaluated in an ad hoc and non-quantitative manner. This is particularly problematic in heterogeneous disease contexts, such as breast cancer transcriptomics, where technical and biologica...