domains / families / science / spatial-multiomics

spatial-multiomics

combined

Integrates spatial RNA with a second modality (protein, ATAC, or histone marks) on spatial CITE-seq, DBiT-seq, spatial-ATAC, or Visium CytAssist data. Use when deciding vertical (same-pixel co-profiling -> WNN/MOFA joint factors) versus diagonal (serial adjacent sections -> registration via PASTE/STalign) integration; recognizing that modalities from serial sections are DIFFERENT cells so joint same-cell methods do not apply; handling a bounded antibody/feature panel where absence is uninformative; or treating a pixel/spot as a multi-cell mixture rather than a single cell.

When to use

• subcellular resolution spatial data
• high-density spatial data
• spatial CITE-seq integration
• DBiT-seq integration
• spatial-ATAC integration
• Visium CytAssist data
• vertical integration (same-pixel co-profiling)
• diagonal integration (serial adjacent sections)

When not to use

• joint same-cell methods for modalities from serial sections

Skill page

Conflict

The two skills use the same name but define different tasks. The first copy is a platform-specific analysis guide for high-resolution spatial transcriptomics (Slide-seq, Stereo-seq, Visium HD, MERFISH) using Squidpy and SpatialData. The second copy is a conceptual and methodological guide for integrating spatial RNA with a second modality (protein, ATAC) using Muon, MOFA, and registration tools like PASTE. They disagree on the primary tool (squidpy vs muon) and the core objective (high-res spatial analysis vs multi-modal integration). Combining them is necessary to capture both the high-resolution processing steps and the multi-modal integration logic.