Randomized Spatial PCA (RASP): A computationally efficient method for dimensionality reduction of high-resolution spatial transcriptomics data
Fig 5
Human dorsolateral prefrontal cortex (DLPFC) spatial transcriptomics analysis, 10X Visium data.
A: Cortical Layer Identification — Ground truth cortical layer annotations (left) compared to spatial domains identified by RASP, PCA, and other competing methods, demonstrating spatial domain delineation accuracy. B: Runtime Comparison — Computational runtimes for all methods relative to randomized PCA, highlighting relative efficiency. C: Clustering Accuracy (ARI) Across Methods — Adjusted Rand Index (ARI) values for methods with colors indicating clustering algorithms. Shows the full range of ARI values at default RASP parameters (kNN = 3–10, ) alongside the single-point ARI performance at the default parameter (kNN = 5,
). RASP-moran and RASP-CHAOS values reflect ARI from label-agnostic metric-based parameter selection. D: Impact of β on Clustering Performance — ARI values for RASP with inverse distance weighting raised to
(left), 1 (middle), and 2 (right), plotted against varying kNN values; colors denote clustering algorithms. E: Spatial Expression of TMSB10 — Normalized TMSB10 expression (left), reduced rank reconstructed expression (center), and spatially smoothed reconstructed expression (right), all plotted on tissue sections. White lines indicate the border of cortical layer 5.