Fig. 3: Comparison of spot-based gene prediction and survival analysis performance among state-of-the-art methods and GHIST using the HER2ST dataset.
From: Spatial gene expression at single-cell resolution from histology using deep learning with GHIST
a,b, Violin and box plots of the average PCC (a) and SSIM (b) between ground-truth gene expression and predicted gene expression. Metrics measured from the test fold of a fourfold cross-validation, averaged over each gene (n = 785) across the dataset. c, Top five correlated genes. d,e, PCC (d) and SSIM (e) violin and box plots for each method for selected SVGs (n = 20 per image sample). f, C-indices of multivariate cox regression models predicting survival of HER2+ subtype from TCGA-BRCA patients (n = 92), using RNA-seq bulk, RNA-seq bulk using only genes present in HER2ST dataset, and the predicted pseudobulk from each method. C-indices were calculated from the test sets of a threefold cross-validation with 100 repeats. g, Cross-validated Kaplan–Meier curves for patients split into high-risk and low-risk groups by the median risk prediction of the multivariate cox regression models for each method and HER2+ breast cancer subtypes. The P value represents the result of the two-sided log-rank test for assessing the statistical significance of differences in survival between the groups. In a, b and d–f, each box plot ranges from the first to third quartile with the median as the horizontal line. The lower whisker extends to 1.5 times the interquartile range below the first quartile, while the upper whisker extends to 1.5 times the interquartile range above the third quartile.
