Transcriptomic Meta-Analysis as a Framework for Robust Cross-Study Biological Inference

The increasing availability of transcriptomic data has created new opportunities for integrating gene expression studies across biological systems and conditions. However, differences in experimental design, sequencing platforms, and sample composition introduce substantial heterogeneity, limiting direct comparability between studies. Transcriptomic meta-analysis provides a framework to address these challenges by identifying expression patterns that are reproducible across independent datasets. In this review, we outline the key methodological steps involved in transcriptomic meta-analysis, including dataset selection, preprocessing, normalization, batch-effect correction, and statistical integration. We discuss how these steps are influenced by the type of data being analyzed, from microarrays and bulk RNA sequencing to single-cell and spatial transcriptomics. Particular attention is given to the role of technical and biological heterogeneity, which must be explicitly considered to avoid misleading conclusions. Rather than treating heterogeneity solely as a source of noise, we argue that it defines the limits of reproducibility and interpretation in cross-study analyses. By focusing on consistent signals across diverse datasets, transcriptomic meta-analysis enables more robust biological inference and supports applications such as biomarker discovery and disease stratification.