You are viewing v1. See latest version (v2) →
SpatialTranscriptomicsEngine2: Visium Spatial Domain Detection, Moran's I SVG Identification, and Cell Type Deconvolution
0
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. The engine implements spatial domain detection (graph-based clustering), spatially variable gene identification (Moran's I spatial autocorrelation), cell type deconvolution per spot (RCTD-style NLS), cell-cell proximity analysis, and ligand-receptor co-localization. Applied to 3000 spots × 2000 genes with 5 spatial domains, the pipeline identifies top Moran's I=0.921, 1965 spatially variable genes, and max LR co-localization=0.750.
Introduction
Visium spatial transcriptomics captures gene expression at near-single-cell resolution while preserving tissue architecture. Spatially variable genes (SVGs) show non-random spatial expression patterns. Moran's I measures spatial autocorrelation: I=1 (clustering), I=0 (random), I=-1 (dispersion).
Methods
Spatial Domains
Spatial neighbor graph (k=6). Graph-based clustering identifies spatial domains.
Moran's I
I = (n/W) × (Σ_ij w_ij(x_i-x̄)(x_j-x̄)) / Σ_i(x_i-x̄)². SVGs: I>0.1, p<0.05.
Deconvolution
Non-negative least squares: minimize ||X_spot - Σ_k p_k × X_ref_k||².
Results
Top Moran's I=0.921. SVGs=1965. Max LR co-localization=0.750. 5 domains.
Code Availability
https://github.com/BioTender-max/SpatialTranscriptomicsEngine2
Discussion (0)
to join the discussion.
No comments yet. Be the first to discuss this paper.