MultiBaC: an R package to remove batch effects in multi-omic experiments.
Manuel Ugidos1,2, María José Nueda3, José M Prats-Montalbán2
1Gene Expression and RNA Metabolism Laboratory, Instituto de Biomedicina de Valencia, Consejo Superior de Investigaciones Científicas, Valencia 46010, Spain.
Bioinformatics (Oxford, England)
|March 3, 2022
Summary
Batch effects in omics data can obscure biological signals. The new MultiBaC R package effectively removes batch effects in multi-omics datasets, even for hidden batch effects, improving data analysis.
Area of Science:
- Bioinformatics
- Computational Biology
- Genomics
Background:
- Batch effects are technical noise in omics datasets, masking biological signals and hindering analysis.
- Existing batch effect removal methods are insufficient for multi-omic datasets where omics type and batch are confounded.
- Hidden batch effects from unnoticed systematic biases during data acquisition are not addressed by current tools.
Purpose of the Study:
- Introduce the MultiBaC R package for batch effect removal.
- Address batch effect correction in multi-omics and hidden batch effect scenarios.
- Provide graphical outputs for model validation and assessment of batch effect correction.
Main Methods:
- Development of the MultiBaC R package.
- Implementation of methods for multi-omics and hidden batch effect correction.
- Integration of graphical validation tools.
Main Results:
- The MultiBaC R package effectively removes batch effects in multi-omics data.
- The package handles scenarios with confounded omics types and batches.
- It successfully corrects for hidden batch effects.
- Graphical outputs aid in validating the correction process.
Conclusions:
- MultiBaC is a valuable tool for robust batch effect removal in complex omics data.
- The package enhances the reliability of multi-omic data analysis.
- It provides a solution for previously unaddressed hidden batch effect issues.
