tooluniverse-spatial-transcriptomics

Core Capabilities

Capability	Description
Data Import	10x Visium, MERFISH, seqFISH, Slide-seq, STARmap, Xenium formats
Quality Control	Spot/cell QC, spatial alignment verification, tissue coverage
Normalization	Spatial-aware normalization accounting for tissue heterogeneity
Spatial Clustering	Identify spatial domains with similar expression profiles
Spatial Variable Genes	Find genes with non-random spatial patterns
Neighborhood Analysis	Cell-cell proximity, spatial neighborhoods, niche identification
Spatial Patterns	Gradients, boundaries, hotspots, expression waves
Integration	Merge with scRNA-seq for cell type mapping
Ligand-Receptor Spatial	Map cell communication in tissue context
Visualization	Spatial plots, heatmaps on tissue, 3D reconstruction

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Phase 1: Data Import & Quality Control

Objective: Load spatial data and assess quality.

Supported platforms:

10x Visium (most common):

• Spots: 55μm diameter, ~50 cells per spot
• Resolution: ~5,000-10,000 spots per capture area
• Data: Expression matrix + spatial coordinates + H&E image

MERFISH/seqFISH (imaging-based):

• Single-cell resolution
• Targeted gene panels (100-10,000 genes)
• Absolute coordinates per cell

Slide-seq/Slide-seqV2:

• 10μm bead resolution
• Genome-wide profiling

Xenium (10x single-cell spatial):

• Single-cell resolution
• Large gene panels (300+ genes)
• Subcellular resolution

Data loading (Visium):

Quality Control:

1. Spot-level QC:

2. Spatial alignment verification:

Phase 2: Preprocessing & Normalization

Objective: Normalize data accounting for spatial heterogeneity.

Normalization:

Highly variable genes:

Spatial smoothing (optional):