.png){kind=icon}
Return
Correcting batch effects in large-scale multiomics studies using a reference-material-based ratio method
.png){kind=icon}
OA
AI
DOI：10.1186/s13059-023-03047-z.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
BackgroundBatch 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.ResultsAs 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 ability of accurately clustering cross-batch samples into their own donors. The ratio-based method, i.e., by scaling absolute feature values of study samples relative to those of concurrently profiled reference material(s), is found to be much more effective and broadly applicable than others, especially when batch effects are completely confounded with biological factors of study interests. We further provide practical guidelines for implementing the ratio based approach in increasingly large-scale multiomics studies.ConclusionsMultiomics measurements are prone to batch effects, which can be effectively corrected using ratio-based scaling of the multiomics data. Our study lays the foundation for eliminating batch effects at a ratio scale.
Keywords:
Batch effect
Ratio
Reference materials
Multiomics
Phenomics
Differentially expressed
Prediction
Data integration
Quartet family
Metrology
Journal
G
IF:
9.4
Papers:
6.4K
Citations:
7.3W
Organization
Cited Papers
Normalization of RNA-seq data using factor analysis of control genes or samples.png)
NATURE BIOTECHNOLOGYOAAI
NATURE BIOTECHNOLOGYIF41.7
OAAIShareSaveFirmiana: towards a one-stop proteomic cloud platform for data processing and analysisNATURE BIOTECHNOLOGY
NATURE BIOTECHNOLOGYIF41.7
PREAI
ShareSaveThe MicroArray Quality Control (MAQC)-IIII study of common practices for the development and validation of microarray-based predictive modelsNATURE BIOTECHNOLOGYOAAI
NATURE BIOTECHNOLOGYIF41.7
OAAIShareSaveThe MicroArray Quality Control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurementsNATURE BIOTECHNOLOGYOAAI
NATURE BIOTECHNOLOGYIF41.7
OAAIShareSaveShareSave
View more