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A novel batch effect correction framework for robust integration of high-variance data via a global-information virtual reference batch
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DOI：10.3389/fmicb.2026.1877381.png)
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Abstract
En 中文
The integration of multi-batch high-variance datasets is increasingly important in studies of complex biological systems. In application domains such as microbial symbiosis; host–microbe interactions; and ecosystem robustness; this places a stringent demand on batch correction methods which must reduce technical batch effects while preserving the biologically meaningful cross-sample structure required for downstream interpretation. Here; we present GIR-Combat; a novel batch correction framework that constructs a global-information virtual reference batch from shared cross-batch structure; thereby enabling more consistent and objective correction across datasets. GIR-Combat identifies mutually nearest neighbors across batches; leverages their shared information to define a virtual reference; and incorporates this reference into a linear modeling framework for correction. By transforming reference-batch specification from a subjective choice into a modeling step; GIR-Combat provides a more objective and robust solution for correcting high-variance and compositionally imbalanced datasets in which conventional methods frequently underperform. We evaluated GIR-Combat on simulated datasets and multiple public benchmark datasets. The results show that GIR-Combat improves batch correction performance relative to existing methods; achieving better batch correction while preserving biologically meaningful structure. Quantitative and visual evaluation metrics further demonstrate its robustness and scalability in challenging integration scenarios. Overall; GIR-Combat provides a practical and methodologically grounded framework for high-variance multi-batch data integration; with potential value in applications where reliable integrated representations are required for interpreting complex biological interactions.
Keywords:
ComBat
linear model
batch effect correction
nearest neighbor pair
interaction-rich biological systems
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