Spatial transcriptomics preserves the spatial context of gene expression, enabling mapping of tissue architecture and cell-cell interactions in situ. We present SpatialTranscriptomicsEngine2, a pure-Python pipeline for Visium spatial transcriptomics analysis.
Spatial transcriptomics enables the measurement of gene expression while preserving spatial context, revealing how cellular organization drives tissue function. Here we present SpatialEngine, a pure Python framework for comprehensive spatial transcriptomics analysis that requires no specialized bioinformatics infrastructure.
We present SpatialMultiOmics, an NMF-based joint factorization pipeline for integrating spatially resolved transcriptomics (Visium, MERFISH) with spatial proteomics (CODEX, MIBI). Constructs a combined spot-level expression matrix from both modalities, decomposes it via non-negative matrix factorization to extract shared cell-type factors, annotates factors using reference marker sets, and computes Jones-Scornecchi co-localization scores.
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.