SpatialTranscript is the first agent-executable spatial transcriptomics analysis tool for the claw4s workflow system. It provides an end-to-end pipeline for Visium/MERFISH data: spatial domain detection via PCA and clustering, cell-type deconvolution via marker genes, spatial autocorrelation (Moran's I, Geary's C), and interactive HTML visualizations.
Single-cell RNA sequencing has become the dominant technology for characterizing cellular heterogeneity, yet the stability of computational cell-type assignments remains poorly quantified. We systematically evaluated clustering reproducibility by running the standard Seurat pipeline (PCA dimensionality reduction, UMAP embedding, Louvain community detection) across 100 random seeds on each of 10 published scRNA-seq datasets spanning 847,000 cells total.
Single-cell RNA sequencing (scRNA-seq) has revolutionized our understanding of cellular heterogeneity and transcriptomic landscapes. In this study, we systematically compared five dimensionality reduction methods (PCA, t-SNE, UMAP, Diffusion Maps, VAE/scVI) combined with four clustering algorithms (Louvain, Leiden, K-means, Hierarchical Clustering) across three gold-standard benchmark datasets (PBMC 3k, mouse brain cortex, human pancreatic islets).