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The Role of Microarray in Modern Sequencing: Statistical Approach Matters in a Comparison Between Microarray and
Isaac D Raplee1, Samiksha A Borkar1, Li Yin1
1Molecular HIV and Host Interactions Section, National Institute of Allergy and Infectious Diseases, National Institutes of Health, 50 South Drive, Bethesda, MD 20894, USA.
Biotech (Basel (Switzerland))
|July 23, 2025
Summary
Gene expression analysis using microarray and RNA-sequencing (RNA-seq) shows high concordance. Both methods are reliable for profiling gene expression and can complement each other for robust biological insights.
Area of Science:
- Genomics
- Molecular Biology
- Bioinformatics
Background:
- Gene expression analysis is fundamental to understanding biological processes, health, and disease.
- RNA-sequencing (RNA-seq) is increasingly preferred over microarray for gene expression profiling.
- The utility of microarray data in the era of RNA-seq warrants investigation.
Purpose of the Study:
- To compare the gene expression profiling capabilities of microarray and RNA-seq technologies.
- To assess the concordance and identify shared findings between the two platforms.
- To evaluate the complementary potential of microarray and RNA-seq in biological research.
Main Methods:
- Whole blood RNA from 35 participants was analyzed using both microarray and RNA-seq.
- Data underwent quality control, normalization, and non-parametric Mann-Whitney U statistical tests.
- Differential expression and pathway analyses were performed to compare platform outputs.
Main Results:
- A high correlation (median Pearson correlation coefficient of 0.76) was observed between microarray and RNA-seq.
- RNA-seq identified more differentially expressed genes (2395) than microarray (427), with 223 shared.
- Pathway analysis revealed more perturbed pathways with RNA-seq (205) compared to microarray (47), with 30 shared.
Conclusions:
- Microarray and RNA-seq provide highly concordant gene expression results when analyzed with consistent statistical methods.
- Both technologies are reliable for gene expression analysis.
- Microarray and RNA-seq can be used complementarily to strengthen biological insights.
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