AN
Data & Repro
AnnData
AnnData is a Python package for handling annotated data matrices, storing experimental measurements (X) alongside observation metadata (obs)…
K-Dense-AI/claude-scientific-skillsView
Data & Repro
SI
simo-multiomics-integration-agent
Maintainer FreedomIntelligence · Last updated April 1, 2026
Single-cell multi-omics integration with SIMO/MOFA+ for joint embedding.
Original source
FreedomIntelligence/OpenClaw-Medical-Skills
https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/simo-multiomics-integration-agent
- Maintainer
- FreedomIntelligence
- License
- MIT
- Last updated
- April 1, 2026
Skill Snapshot
Key Details From SKILL.md
Key Notes
- The SIMO Multiomics Integration Agent performs spatial integration of multi-omics datasets through probabilistic alignment. Unlike previous tools limited to transcriptomics, SIMO integrates spatial transcriptomics with single-cell RNA-seq and expands to chromatin accessibility, DNA methylation, and proteomics data.
- When integrating spatial transcriptomics with single-cell multi-omics data.
- For constructing comprehensive tissue atlases with spatial context.
- To map epigenomic states (ATAC-seq, methylation) onto spatial coordinates.
- When analyzing multi-modal cellular phenotypes in tissue architecture.
Source Doc
Excerpt From SKILL.md
Core Capabilities
-
Spatial-scRNA Integration: Probabilistically align single-cell RNA-seq to spatial coordinates.
-
Chromatin Accessibility Mapping: Project scATAC-seq profiles onto spatial tissue locations.
-
DNA Methylation Spatial Mapping: Integrate single-cell methylation data with spatial context.
-
Multi-Modal Fusion: Combine transcriptomic, epigenomic, and proteomic layers.
-
Probabilistic Cell-Type Assignment: Assign cell types to spatial spots with uncertainty quantification.
-
Spatial Niche Identification: Discover cellular niches defined by multi-omic signatures.
Supported Modalities
| Modality | Input Format | Spatial Reference |
|---|---|---|
| scRNA-seq | AnnData, Seurat | Visium, MERFISH, Xenium |
| scATAC-seq | SnapATAC2, ArchR | Visium, Slide-seq |
| scMethyl | Bismark, allcools | Any spatial modality |
| CITE-seq (protein) | AnnData | Spatial proteomics |
| Multi-ome (RNA+ATAC) | Muon, SnapATAC2 | All platforms |
Integration Algorithm
| Step | Method | Purpose |
|---|---|---|
| Feature Selection | HVG + marker genes | Reduce dimensionality |
| Embedding | Variational autoencoder | Shared latent space |
| Alignment | Optimal transport | Probabilistic matching |
| Spatial Mapping | Gaussian processes | Smooth spatial predictions |
| Uncertainty | Posterior sampling | Confidence intervals |
Use cases
- When integrating spatial transcriptomics with single-cell multi-omics data.
- For constructing comprehensive tissue atlases with spatial context.
- To map epigenomic states (ATAC-seq, methylation) onto spatial coordinates.
Not for
- Do not rely on this catalog entry alone for installation or maintenance details.
Upstream Related Skills
- scGPT_Agent - For foundation model embeddings
- Spatial_Epigenomics_Agent - For spatial epigenomics analysis
- Cell_Cell_Communication - For ligand-receptor analysis
- Nicheformer_Spatial_Agent - For spatial niche modeling
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