Randomized Spatial PCA (RASP): A computationally efficient method for dimensionality reduction of high-resolution spatial transcriptomics data
Fig 3
Covariate analysis (Mouse ovary dataset) for the cell type clustering case.
A: RASP Model Architectures — Illustrations of the baseline RASP model without covariates (top left), the single-stage RASP model incorporating one covariate (top right), and the two-stage RASP model architecture that integrates an additional covariate for improved clustering. B: Clustering Performance with Covariate Adjustment — Adjusted Rand Index (ARI) comparisons across models without covariates, single-stage RASP, and two-stage RASP, plotted against varying kNN values. Columns represent different clustering algorithms, while rows show the impact of different covariates: local cell density (top), cell library size (middle), and cell volume (bottom).