Mining transcriptomic data for gestational diabetes mellitus: What public datasets reveal

Figure 3 Various transcriptomic technologies in gestational diabetes mellitus research. A: Overall proportions of transcriptomic technologies represented in gestational diabetes mellitus (GDM) datasets. RNA-sequencing (RNA-seq) remains the most prevalent (43%), followed by DNA microarrays (28%), non-coding RNA-seq (13%), multi-omics (6%) and single-cell RNA-seq (scRNA-seq) (10%). These distributions highlight both the persistence of classical platforms and the growing role of emerging modalities in GDM research; B: Distribution of dataset types across research themes in GDM. Placenta-related datasets dominate, followed by circulating biomarkers and β-cell/islet, while predictive modeling studies remain fewer. RNA-seq (blue) and microarray (light blue) are the most widely used platforms overall, whereas non-coding RNA-seq (light green) and scRNA-seq (dark green) contribute substantially to biomarker and islet-related studies. Multi-omics approaches (pink), though relatively limited, are increasingly adopted in biomarker discovery and predictive modeling. RNA-seq: RNA-sequencing; scRNA-seq: Single-cell RNA-sequencing; ncRNA-seq: Non-coding RNA-sequencing.