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MultiBaC: an R package to remove batch effects in multi-omic experiments.

Manuel Ugidos^1,2, 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

This summary is machine-generated.

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.

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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.