Correcting batch effects in large-scale multiomics studies using a reference-material-based ratio method.

ABSTRACT:

Background

Batch effects are notoriously common technical variations in multiomics data and may result in misleading outcomes if uncorrected or over-corrected. A plethora of batch-effect correction algorithms are proposed to facilitate data integration. However, their respective advantages and limitations are not adequately assessed in terms of omics types, the performance metrics, and the application scenarios.

Results

As part of the Quartet Project for quality control and data integration of multiomics profiling, we comprehensively assess the performance of seven batch effect correction algorithms based on different performance metrics of clinical relevance, i.e., the accuracy of identifying differentially expressed features, the robustness of predictive models, and the ab

SUBMITTER: Yu Y 

PROVIDER: S-EPMC10483871 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature