Randomized Spatial PCA (RASP): A computationally efficient method for dimensionality reduction of high-resolution spatial transcriptomics data
Fig 6
Spatial domain analysis of the mouse olfactory bulb using STOmics Stereo-seq data.
A: Laminar Structure Identification — Ground truth cortical laminar structure (top left) alongside spatial domains identified by RASP, PCA, and other methods, demonstrating domain detection accuracy. B: Runtime Performance — Comparison of computational runtimes for all methods relative to normal PCA, highlighting efficiency differences. C: Spatial Autocorrelation Metrics — Moran’s I (top) and CHAOS (bottom) scores for all methods, with colors indicating clustering algorithms. Default and best scores are reported for RASP with default parameters (kNN = 30–100, ). D: Effect of β on Spatial Metrics — Moran’s I (top) and CHAOS (bottom) scores for RASP as inverse distance weighting is raised to
(left), 1 (middle), and 2 (right), plotted against varying kNN; colors indicate clustering algorithm. E: Spatial Expression of Doc2g — Normalized expression (left), reduced rank reconstructed expression (center), and spatially smoothed reduced rank reconstructed expression (right) of Doc2g plotted on tissue.