🧩 Single-Cell Multi-Omics Integration

Jointly analyze multiple modalities (RNA + protein, RNA + ATAC) measured in the same cells.

Common Modalities

Workflow

1. Calculate: Reassemble isolated modalities to identical identifiers.
2. Normalize: Compute scale and metric dimensions natively for both arms.
3. Execute: Fuse structures via multi-modal matrices (WNN/MOFA).
4. Visualise: Render combined UMAP architecture layouts.
5. Report: Tabulate alignment consistency metrics.

CLI Reference

python skills/singlecell/multiome/sc_multiome.py \
  --input <data.h5ad> --output <dir>
python omicsclaw.py run sc-multiome --demo

Algorithm / Methodology

CITE-seq Analysis (Seurat R)

Load Data

library(Seurat)

data <- Read10X('filtered_feature_bc_matrix/')
rna_counts <- data$`Gene Expression`
adt_counts <- data$`Antibody Capture`

obj <- CreateSeuratObject(counts = rna_counts, assay = 'RNA')
obj[['ADT']] <- CreateAssayObject(counts = adt_counts)

QC and Normalization

obj <- PercentageFeatureSet(obj, pattern = '^MT-', col.name = 'percent.mt')
obj <- subset(obj, nFeature_RNA > 200 & percent.mt < 20)

# Normalize RNA
obj <- NormalizeData(obj, assay = 'RNA')
obj <- FindVariableFeatures(obj, assay = 'RNA')
obj <- ScaleData(obj, assay = 'RNA')

# Normalize ADT (CLR normalization)
obj <- NormalizeData(obj, assay = 'ADT', normalization.method = 'CLR', margin = 2)
obj <- ScaleData(obj, assay = 'ADT')

Weighted Nearest Neighbor (WNN) Clustering

Goal: Jointly cluster cells using both modalities, weighting each per cell.

# PCA for each modality
obj <- RunPCA(obj, assay = 'RNA', reduction.name = 'pca')
obj <- RunPCA(obj, assay = 'ADT', reduction.name = 'apca',
              features = rownames(obj[['ADT']]))

# WNN graph combining both modalities
obj <- FindMultiModalNeighbors(obj,
    reduction.list = list('pca', 'apca'),
    dims.list = list(1:30, 1:18))

# Cluster on WNN graph
obj <- FindClusters(obj, graph.name = 'wsnn', resolution = 0.5)

# UMAP on WNN
obj <- RunUMAP(obj, nn.name = 'weighted.nn', reduction.name = 'wnn.umap')

Visualize

DimPlot(obj, reduction = 'wnn.umap', label = TRUE)

FeaturePlot(obj, features = c('adt_CD3', 'adt_CD19', 'adt_CD14'),
            reduction = 'wnn.umap')

# Compare modality weights
VlnPlot(obj, features = 'RNA.weight', group.by = 'seurat_clusters')

10X Multiome (RNA + ATAC, Seurat + Signac)

library(Seurat)
library(Signac)

# Load data
rna_counts <- Read10X_h5('filtered_feature_bc_matrix.h5')$`Gene Expression`
atac_counts <- Read10X_h5('filtered_feature_bc_matrix.h5')$Peaks
fragments <- CreateFragmentObject('atac_fragments.tsv.gz')

obj <- CreateSeuratObject(counts = rna_counts, assay = 'RNA')
obj[['ATAC']] <- CreateChromatinAssay(counts = atac_counts, fragments = fragments,
                                       genome = 'hg38', min.cells = 5)

# ATAC processing
obj <- NucleosomeSignal(obj)
obj <- TSSEnrichment(obj)
obj <- RunTFIDF(obj, assay = 'ATAC')
obj <- FindTopFeatures(obj, assay = 'ATAC', min.cutoff = 'q0')
obj <- RunSVD(obj, assay = 'ATAC')

# RNA processing
DefaultAssay(obj) <- 'RNA'
obj <- NormalizeData(obj) %>% FindVariableFeatures() %>% ScaleData() %>% RunPCA()

# WNN integration
obj <- FindMultiModalNeighbors(obj, reduction.list = list('pca', 'lsi'),
                                dims.list = list(1:30, 2:30))
obj <- RunUMAP(obj, nn.name = 'weighted.nn', reduction.name = 'wnn.umap')
obj <- FindClusters(obj, graph.name = 'wsnn')

MuData / muon (Python)

import scanpy as sc
import muon as mu
from muon import prot as pt

mdata = mu.read_10x_h5('filtered_feature_bc_matrix.h5')

rna = mdata.mod['rna']
prot = mdata.mod['prot']

# Process RNA
sc.pp.filter_cells(rna, min_genes=200)
sc.pp.normalize_total(rna, target_sum=1e4)
sc.pp.log1p(rna)
sc.pp.highly_variable_genes(rna)
sc.tl.pca(rna)

# Process protein (CLR normalization)
pt.pp.clr(prot)

# Multi-omics factor analysis
mu.tl.mofa(mdata, n_factors=20)

# Joint UMAP
mu.tl.umap(mdata)
mu.pl.umap(mdata, color=['rna:leiden', 'prot:CD3'])

Multi-Modal Marker Discovery

DefaultAssay(obj) <- 'RNA'
rna_markers <- FindAllMarkers(obj, only.pos = TRUE)

DefaultAssay(obj) <- 'ADT'
adt_markers <- FindAllMarkers(obj, only.pos = TRUE)

all_markers <- rbind(
    transform(rna_markers, modality = 'RNA'),
    transform(adt_markers, modality = 'ADT')
)

Modality Weight Inspection

weights <- obj@reductions$wnn@misc$weights
aggregate(weights, by = list(obj$seurat_clusters), mean)

Parameters

--method

wnn

--modalities

rna,adt

--n-factors

20

Example Queries

• "Run multimodal WNN integration across my protein and transcript data"
• "Use MOFA to derive multi-omic factors"

Output Structure

output_dir/
├── report.md
├── result.json
├── processed.h5ad
├── figures/
│   └── summary_plot.png
├── tables/
│   └── metrics.csv
└── reproducibility/
    ├── commands.sh
    ├── environment.yml
    └── checksums.sha256

Version Compatibility

Reference examples tested with: scanpy 1.10+, muon 0.1+, numpy 1.26+

Dependencies

Required: scanpy, numpy Optional: muon, Seurat (R), Signac (R), ArchR (R)

Citations

• WNN — Hao et al., Cell 2021
• MOFA+ — Argelaguet et al., Molecular Systems Biology 2020
• muon — Bredikhin et al., Genome Biology 2022
• CITE-seq — Stoeckius et al., Nature Methods 2017

Safety

• Local-first: Strict offline processing without external upload.
• Disclaimer: Requires OmicsClaw reporting structures and disclaimers.
• Audit trail: Hyperparameters and operational flow states are logged fully.

Integration with Orchestrator

Trigger conditions:

• Automatically invoked dynamically based on tool metadata and user intent matching.

Chaining partners:

• sc-preprocess

  — Data loading and QC

• sc-integrate

  — Batch integration (single modality)

• sc-annotate

  — Cell type annotation post WNN