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:

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