sc-multiome

name	sc-multiome
description	Multi-omics integration for single-cell data (CITE-seq, 10X Multiome, SHARE-seq). Weighted Nearest Neighbor (WNN) analysis, MOFA+, and muon/MuData workflows.
version	0.1.0
author	OmicsClaw
license	MIT
tags	["singlecell","multiome","CITE-seq","WNN","MOFA","multi-modal"]
metadata	{"omicsclaw":{"domain":"singlecell","requires":{"bins":"[Truncated]","env":"[Truncated]","config":"[Truncated]"},"emoji":"🧩","homepage":"https://github.com/OmicsClaw/OmicsClaw","os":["macos","linux"],"install":["[Truncated]"],"trigger_keywords":["multiome","CITE-seq","multi-modal","WNN","MOFA","RNA ATAC"]}}

🧩 Single-Cell Multi-Omics Integration

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

Common Modalities

Technology	Modalities	Package
CITE-seq	RNA + surface proteins (ADT)	Seurat
10X Multiome	RNA + ATAC	Seurat, Signac, ArchR
SHARE-seq	RNA + ATAC	Seurat, Signac
Spatial (Visium)	RNA + spatial	Seurat, Squidpy

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 =  resolution  


obj  RunUMAPobj nn.name   reduction.name  

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 = 
obj  FindTopFeaturesobj assay   min.cutoff  
obj  RunSVDobj assay  


DefaultAssayobj  
obj  NormalizeDataobj  FindVariableFeatures  ScaleData  RunPCA


obj  FindMultiModalNeighborsobj reduction.list   
                                dims.list   
obj  RunUMAPobj nn.name   reduction.name  
obj  FindClustersobj graph.name  

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

Parameter	Default	Description
--method	wnn	wnn, mofa, standard
--modalities	rna,adt	Comma-separated modalities
--n-factors	20	Number of factors (MOFA)

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