Randomized Spatial PCA (RASP): A computationally efficient method for dimensionality reduction of high-resolution spatial transcriptomics data
Fig 2
Overview of mouse ovary analysis using Vizgen MERFISH data.
A: Cell Type Annotation Comparison — Ground truth cell type labels (top left) alongside predictions from RASP, normal PCA, and other methods, illustrating accuracy of spatial mapping. B: Runtime Performance — Comparison of computational runtime across methods, highlighting RASP efficiency relative to standard randomized PCA. C: Clustering Accuracy (ARI) Across Methods — Adjusted Rand Index values for all methods, color-coded by clustering algorithm; shows full ARI range at default RASP parameters (kNN = 2–20, ) and the default parameter single-point performance (kNN=10,
). RASP-moran and RASP-CHAOS indicate ARI when using label-agnostic metrics for parameter selection. D: Effect of β on ARI — ARI values for RASP with inverse distance weighting raised to
(left), 0.5 (middle), and 2 (right), plotted against kNN values; colors indicate clustering algorithm used. E: Spatial Expression Patterns of Lhcgr — Normalized expression (left), reduced rank reconstructed expression (center), and spatially smoothed reconstructed expression (right). White lines depict luteinizing mural and luteal cell compartment boundaries.