BaySC introduces an integrative Bayesian spatial clustering model with MFM prior for automatic domain count, MRF for local coherence, and weighted log-likelihood fusion for multi-omics data, validated on twelve datasets with competitive metrics and better spARI.
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GTSA-PCA replaces global PCA covariance with curvature-weighted local operators and a geodesic alignment step to produce geometry-aware embeddings that improve on standard PCA and UMAP in small-sample high-curvature settings.
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BaySC: Uncovering Tissue Architecture in Spatial Multi-Omics via Probabilistic Spatial Clustering
BaySC introduces an integrative Bayesian spatial clustering model with MFM prior for automatic domain count, MRF for local coherence, and weighted log-likelihood fusion for multi-omics data, validated on twelve datasets with competitive metrics and better spARI.
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Curvature-Aware PCA with Geodesic Tangent Space Aggregation for Semi-Supervised Learning
GTSA-PCA replaces global PCA covariance with curvature-weighted local operators and a geodesic alignment step to produce geometry-aware embeddings that improve on standard PCA and UMAP in small-sample high-curvature settings.