Gene expression analysis is crucial in understanding cellular processes, development, health, and disease. With RNA-seq outpacing microarray as the chosen platform for gene expression, is there space for array data in future profiling? This study involved 35 participants from the Adolescent Medicine Trials Network for HIV/AIDS Intervention protocol. RNA was isolated from whole blood samples and analyzed using both microarray and RNA-seq technologies. Data processing included quality control, normalization, and statistical analysis using non-parametric Mann–Whitney U tests. Differential expression analysis and pathway analysis were conducted to compare the outputs of the two platforms. The study found a high correlation in gene expression profiles between microarray and RNA-seq, with a median Pearson correlation coefficient of 0.76. RNA-seq identified 2395 differentially expressed genes (DEGs), while microarray identified 427 DEGs, with 223 DEGs shared between the two platforms. Pathway analysis revealed 205 perturbed pathways by RNA-seq and 47 by microarray, with 30 pathways shared. Both microarray and RNA-seq technologies provide highly concordant results when analyzed with consistent non-parametric statistical methods. The findings emphasize that both methods are reliable for gene expression analysis and can be used complementarily to enhance the robustness of biological insights.

Gene expression analysis is crucial in understanding cellular processes, development, health, and disease. With RNA-seq outpacing microarray as the chosen platform for gene expression, is there space for array data in future profiling? The study involved 35 participants from the Adolescent Medicine Trials Network for HIV/AIDS Intervention protocol. RNA was isolated from whole blood samples and analyzed using both microarray and RNA-seq technologies. Data processing included quality control, normalization, and statistical analysis using non-parametric Mann-Whitney U tests. Differential expression analysis and pathway analysis were conducted to compare the outputs of the two platforms. The study found a high correlation in gene expression profiles between microarray and RNA-seq, with a median Pearson correlation coefficient of 0.76. RNA-seq identified 2,395 differentially expressed genes (DEGs), while microarray identified 427 DEGs, with 223 DEGs shared between the two platforms. Pathway analysis revealed 205 perturbed pathways by RNA-seq and 47 by microarray, with 30 pathways shared. Both microarray and RNA-seq technologies provide highly concordant results when analyzed with consistent non-parametric statistical methods. The findings emphasize that both methods are reliable for gene expression analysis and can be used complementarily to enhance the robustness of biological insights.