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Randomized Spatial PCA (RASP): A computationally efficient method for dimensionality reduction of high-resolution spatial transcriptomics data

Fig 7

Spatial domain analysis of human breast cancer tissue using 10x Xenium data.

A: Ground Truth and Predicted Spatial Domains — Ground truth spatial domain annotations (top left) compared to spatial domain predictions from RASP and normal PCA, illustrating method performance in domain delineation. B: Clustering Accuracy Across Methods — Adjusted Rand Index (ARI) values for all methods, with colors representing different clustering algorithms. Displays full range of ARI scores at default RASP parameters (kNN = 30–100, ), along with the single-point performance at default parameter values (kNN = 50, ). RASP-moran and RASP-CHAOS indicate ARI computed using label-agnostic metrics for parameter selection. C: Spatial Expression of FAM3B and CD52 — Normalized expression (left column), reduced rank reconstructed expression (center column), and spatially smoothed reduced rank reconstructed expression (right column) of FAM3B and CD52 plotted on tissue sections. White boundaries indicate DCIS (top row) and Immune cell compartments (bottom row). D: Influence of Smoothing Parameters on Clustering — ARI values for RASP plotted against smoothing distance (top) and kNN (right), with colors indicating different clustering algorithms.

doi: https://doi.org/10.1371/journal.pcbi.1013759.g007