--- name: simo-multiomics-integration-agent description: AI-powered spatial integration of multi-omics datasets using probabilistic alignment for comprehensive tissue atlas construction and cellular state mapping. license: MIT metadata: author: AI Group version: "1.0.0" created: "2026-01-20" compatibility: - system: Python 3.10+ allowed-tools: - run_shell_command - read_file - write_file --- # SIMO Multiomics Integration Agent 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 to Use This Skill * 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. * For spatial deconvolution combining multiple modalities. ## Core Capabilities 1. **Spatial-scRNA Integration**: Probabilistically align single-cell RNA-seq to spatial coordinates. 2. **Chromatin Accessibility Mapping**: Project scATAC-seq profiles onto spatial tissue locations. 3. **DNA Methylation Spatial Mapping**: Integrate single-cell methylation data with spatial context. 4. **Multi-Modal Fusion**: Combine transcriptomic, epigenomic, and proteomic layers. 5. **Probabilistic Cell-Type Assignment**: Assign cell types to spatial spots with uncertainty quantification. 6. **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 | ## Workflow 1. **Input**: Spatial transcriptomics (Visium/MERFISH/Xenium), reference single-cell multi-omics. 2. **Preprocessing**: Normalize, select features, QC both datasets. 3. **Embedding**: Learn joint latent representation across modalities. 4. **Probabilistic Alignment**: Compute cell-to-spot assignment probabilities. 5. **Spatial Imputation**: Transfer modalities to spatial coordinates. 6. **Niche Analysis**: Identify spatial domains by multi-omic signatures. 7. **Output**: Integrated spatial multi-omics object, niche assignments, visualizations. ## Example Usage **User**: "Integrate our scRNA-seq and scATAC-seq data with the spatial transcriptomics to understand chromatin states in different tissue regions." **Agent Action**: ```bash python3 Skills/Genomics/SIMO_Multiomics_Integration_Agent/simo_integration.py \ --spatial_data visium_data.h5ad \ --scrna_ref scrna_atlas.h5ad \ --scatac_ref scatac_atlas.h5ad \ --modalities rna,atac \ --n_spots_per_cell 5 \ --uncertainty_quantification true \ --output integrated_spatial_multiome.h5ad ``` ## Output Components | Output | Description | Format | |--------|-------------|--------| | Integrated Object | Multi-modal spatial data | AnnData/Muon | | Cell Type Map | Spatial cell type assignments | GeoTIFF, CSV | | Chromatin Accessibility Map | Spatial ATAC patterns | BigWig, CSV | | Niche Assignments | Spatial domain labels | CSV, Zarr | | Uncertainty Maps | Per-spot confidence | GeoTIFF | | Gene Activity Scores | ATAC-derived gene activity | AnnData layer | ## Spatial Platforms Supported | Platform | Resolution | Spots/Cells | Genes | |----------|------------|-------------|-------| | 10x Visium | 55 μm | ~5,000 | Whole transcriptome | | 10x Visium HD | 8 μm | ~300,000 | Whole transcriptome | | 10x Xenium | Subcellular | >100,000 | 300-5,000 panel | | MERFISH | Subcellular | >1M | 100-10,000 panel | | Slide-seq | 10 μm | ~60,000 | Whole transcriptome | | CosMx | Subcellular | >1M | 1,000-6,000 panel | ## AI/ML Components **Variational Integration**: - Multi-modal VAE for joint embeddings - Contrastive learning for modality alignment - Batch correction across datasets **Probabilistic Mapping**: - Optimal transport with entropic regularization - Gaussian process spatial smoothing - Bayesian uncertainty estimation **Niche Discovery**: - Multi-view clustering - Spatial autocorrelation (Moran's I) - Graph neural networks for niche boundaries ## Prerequisites * Python 3.10+ * Scanpy, Squidpy, Muon * scvi-tools, SnapATAC2 * POT (Python Optimal Transport) * PyTorch, GPyTorch ## 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 ## Special Considerations 1. **Batch Effects**: Pre-align datasets from different protocols 2. **Spot Deconvolution**: Lower resolution platforms need deconvolution 3. **Sparsity**: scATAC data requires aggregation strategies 4. **Compute**: Multi-modal integration is memory-intensive 5. **Validation**: Verify spatial patterns with known marker distributions ## Applications | Application | Use Case | |-------------|----------| | Tumor Microenvironment | Map chromatin states of immune infiltrates | | Development | Track lineage chromatin dynamics spatially | | Neurodegeneration | Spatial mapping of epigenetic changes | | Fibrosis | Understand spatial activation programs | ## Author AI Group - Biomedical AI Platform --- Source attribution: mdbabumiamssm Original project: https://github.com/mdbabumiamssm/LLMs-Universal-Life-Science-and-Clinical-Skills-/blob/ef3c40b235ba23083e67263a921759784f0dd251/Skills/Genomics/SIMO_Multiomics_Integration_Agent/SKILL.md License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice MIT License Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # Mega AI Hub resource metadata Creator / publisher: mdbabumiamssm Original project: https://github.com/mdbabumiamssm/LLMs-Universal-Life-Science-and-Clinical-Skills-/blob/ef3c40b235ba23083e67263a921759784f0dd251/Skills/Genomics/SIMO_Multiomics_Integration_Agent/SKILL.md Attribution basis: Original GitHub package verified against archived instruction text Fields of work: Data & Analytics, Education & Research, Healthcare & Life Sciences, Product Management Roles: Business Intelligence Analyst, Clinical Research, Data Scientist, Product Manager, Researcher Specialties: API, Backend Tools: Python Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: 04b9bfcc0120019ddbd0157765e332a0ac61eac49086b80c787fc10935f8774b Metadata reviewed: 2026-10-27 This metadata supersedes earlier Mega AI Hub catalog attribution. Original instruction and supporting files are preserved.