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. 2026 Sep 6;6(11):101403. doi: 10.1016/j.xops.2026.101403

Figure 3.

Figure 3

Cross-platform validation of GeoMx spatial transcriptomic data using scRNA-seq. (A-B) Workflow illustrates normalization and integration workflow between GeoMx and scRNA-seq data sets for RPE and photoreceptor layers. (C-D) Raw scale Pearson correlation heatmaps comparing GeoMx RPE ROIs from control eyes (central and peripheral regions) with scRNA-seq pseudobulk RPE data sets (C), GeoMx photoreceptor ROIs with cone pseudobulk profiles from four scRNA-seq donors (D). Regions of interest are labeled by donor identity, demonstrating modest expression concordance for cone-specific genes. (E and F) Comparison of RPE and cone-specific gene expression following Z normalization across data sets, remains consistent, thus confirming layer-specific expression and cross-platform reproducibility. BH = Benjamini–Hochberg; CPM = counts per million; DSP = Digital Spatial Profiler; FDR = false discovery rate; ROI = region of interest; RPE = retinal pigment epithelium; scRNA-seq = single-cell RNA sequencing.