For each tissue we computed cancer and normal across-individual averages for each probe.
To compute similarity between two triangles, we computed the ratio between the area of intersection of the triangles and the area of their union.
To avoid underestimating the computed correlation as a consequence of intrinsic differences between cell subpopulations, we computed a weighted Spearman correlation using the R package wCorr44.
To identify similarities in tissue gene expression between pig and human we computed Pearson's correlation coefficients for pairs of common tissues across these two species.
Within each tissue, for each gene, we computed a test statistic to assess the significance of the between-mouse variance component relative to the within-mouse variance component.
To characterize PAS clusters that are highly used in two tissue types simultaneously (overlapping), we computed the test statistics to be the difference between the mean of the highest two residual values and the lowest value.
To assess relations between these ordinal variables, we computed Spearman's rank-correlation coefficient ρ.
To obtain tissue coverage area, we compute the difference in areas bounded by the stent and lumen contours.
We computed overlap for both tissue TF pairs and their biological functions between tissues using hypergeometric distribution.
To test the hypothesis of brain tissue property covariance within functional basal ganglia circuits, we computed voxel-wise correlations between parameter values extracted from motor, associative, or limbic STNs and the rest of the brain.
