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Evaluating Cross-Platform Batch Correction Methods for Integrated Microarray and RNA-seq Data Analysis.

Xuejun Sun¹, Yu Zhang¹, Chuwen Liu¹

¹Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, U.S.A.

Biorxiv : the Preprint Server for Biology

|June 5, 2026

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This summary is machine-generated.

Integrating microarray and RNA-seq data is challenging. Gene-wise methods like limma offer the best performance for cross-platform analysis, controlling errors and improving discovery.

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Integrating gene expression data from different platforms (microarray, RNA-seq) is crucial for complex trait analysis.
Platform-specific technical differences pose significant challenges for cross-platform data integration.

Purpose of the Study:

To evaluate ten batch-effect correction methods for combining microarray and RNA-seq data.
To compare unsupervised (sample-wise, gene-wise) and supervised methods for cross-platform integration.

Main Methods:

Classified methods into unsupervised sample-wise, unsupervised gene-wise, and supervised approaches.
Assessed performance using distribution alignment, clustering, outcome prediction, and differential expression (DE) analysis.
Utilized paired real datasets and simulation studies for evaluation.

Keywords:

RNA-seq batch effect correction cross-platform integration meta-analysis microarray

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Main Results:

Supervised methods showed good alignment but introduced information leakage and inflated Type I error.
Gene-wise methods generally maintained Type I error control and offered higher power, with limma excelling in DE analysis.
Meta-analysis also controlled Type I error well; limma and QN performed best for outcome prediction.

Conclusions:

limma is recommended as the top general-purpose method for cross-platform integration due to its strong performance and error control.
Gene-wise methods are superior to supervised methods for unbiased discovery in integrated gene expression studies.
QN offers practical advantages for normalizing new samples without refitting.