gabrielmoreira Spatial Omics Skills
Skills for spatial transcriptomics data analysis, mapping, and visualization.
Available Skills
Single-Cell to Spatial Mapping
Map scRNA-seq to spatial data using optimal transport (MOSCOT) for gene imputation and cell type transfer.
Skill file: single_cell_spatial_mapping.md
When to use:
- You have paired scRNA-seq and spatial transcriptomics data
- You want to impute genes not measured in the spatial modality
- You want to transfer cell type annotations to spatial coordinates
3D Spatial Data Visualization
Interactive 3D visualization and rotating GIF animations for spatial data with PyVista.
Skill file: visualize_3d_spatial.md
When to use:
- Your spatial data has 3D coordinates
- You want to visualize gene expression or cell types in 3D
- You want to create rotating GIF animations
Spatial 3D Slice Alignment (Spateo)
Align serial spatial transcriptomics sections into a 3D volume using Spateo morpho_align with pairwise rigid registration.
Skill file: spatial_3d_alignment.md
When to use:
- You have serial tissue sections that need 3D reconstruction
- You want morphology + expression-based slice registration
- You need rigid transformations between consecutive sections
Spatial Cell-Cell Interaction (Spateo LR)
Infer ligand-receptor interactions between spatially adjacent cell types using Spateo's two-group CCI analysis with permutation testing.
Skill file: spatial_cci.md
When to use:
- You want to find LR interactions constrained by spatial proximity
- You have imputed spatial data with mapped cell type labels
- You want to compare spatial vs non-spatial CCI results
Spatial Deconvolution (Cell2location / Tangram)
Estimate cell type composition at each spatial location using scRNA-seq reference data. Two-stage model training with Cell2location, or simpler Tangram alternative.
Skill file: spatial_deconvolution.md
When to use:
- You want to estimate cell type proportions in spatial data
- You have a scRNA-seq reference with cell type annotations
- You want to impute gene expression via deconvolution
Spatial Signal Boundary Analysis
Detect expression domain boundaries between spatially antagonistic signals (e.g., Cer1 restricting Nodal). Includes auto-boundary detection, distance-decay analysis, and comprehensive 6-panel visualization.
Skill file: spatial_boundary_analysis.md
When to use:
- You have two spatially opposing signals (inhibitor/target)
- You want to quantify spatial restriction of expression domains
- You need publication-quality boundary analysis figures
Serial H&E Image Registration (RoMa)
Align consecutive H&E histology images using deep dense feature matching (RoMa + DINOv2) with RANSAC rigid transform estimation and BFS global composition.
Skill file: he_image_registration.md
When to use:
- You have serial H&E sections that need global alignment
- You want to build a 3D coordinate frame from histology images
- You need to co-register spatial transcriptomics data with H&E