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

Fig 4

RASP performance across different scales in the Mouse Sagittal brain dataset.

A: Cell Type Annotations — Ground truth cell type labels (top) alongside predicted labels identified by RASP (bottom), demonstrating cell-level classification accuracy. B: Cell Type Clustering Accuracy — Adjusted Rand Index (ARI) values for all methods based on cell type annotations in A, with colors indicating clustering algorithms. Displays full ARI range at default RASP parameters (kNN = 2–20, ) and single-point ARI for default parameters (kNN = 10, ). RASP-moran and RASP-CHAOS indicate ARI when using label-agnostic metrics for parameter selection. C: Effect of β on Cell Type Clustering — ARI values for RASP with inverse distance weighting raised to (left), 1 (middle), and 2 (right), plotted against kNN values; colors denote clustering algorithm. D: Region Annotations — Ground truth spatial brain region labels and corresponding regions identified by RASP. E: Region-Level Clustering Accuracy — ARI quantification similar to B, but computed using region annotations from D. Shows full ARI range at default RASP parameters (kNN = 50–100, ) and single-point ARI performance (kNN = 50, ). RASP-moran and RASP-CHAOS indicate ARI when using label-agnostic metrics for parameter selection. F: Runtime Comparison — Computational runtime of all methods relative to randomized PCA, highlighting efficiency. H: Effect of β on Region-Level Clustering — ARI values computed on region annotations (D) with inverse distance weighting raised to (left), 1 (middle), and 2 (right), plotted against kNN values; colors indicate clustering algorithms.

doi: https://doi.org/10.1371/journal.pcbi.1013759.g004