bio-single-cell-multimodal-integration

FreedomIntelligence/OpenClaw-Medical-SkillsBiology Medicine and BioinformaticsAudit pendingSnapshot 2e70391e85d2

Summary

This source did not publish a separate summary. Review SKILL.md before using the skill.

SKILL.md

Version Compatibility

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

Before using code patterns, verify installed versions match. If versions differ:

Python: pip show <package> then help(module.function) to check signatures
R: packageVersion('<pkg>') then ?function_name to verify parameters

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

Multimodal Integration

"Integrate RNA and protein data from my CITE-seq experiment" → Jointly analyze multiple modalities (RNA + protein, RNA + ATAC) measured in the same cells using weighted nearest neighbor or factor analysis.

R: Seurat::FindMultiModalNeighbors() for WNN integration
Python: muon for MuData handling, scanpy + anndata for multimodal objects

Analyze multi-modal single-cell data where multiple measurements are made per cell.

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 coordinates	Seurat, Squidpy

CITE-seq Analysis (Seurat)

Load Data

library(Seurat)

# Read 10X data with antibody capture
data <- Read10X('filtered_feature_bc_matrix/')

# Separate RNA and ADT
rna_counts <- data$`Gene Expression`
adt_counts <- data$`Antibody Capture`

# Create Seurat object with both assays
obj <- CreateSeuratObject(counts = rna_counts, assay = 'RNA')
obj[['ADT']] <- CreateAssayObject(counts = adt_counts)

# RNA QC (standard)
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')

# Dimensionality reduction 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')

# UMAP colored by cluster
DimPlot(obj, reduction = 'wnn.umap', label = TRUE)

# ADT expression on UMAP
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')

library(Seurat)
library(Signac)

# Read RNA counts
rna_counts <- Read10X_h5('filtered_feature_bc_matrix.h5')$`Gene Expression`

# Read ATAC fragments
atac_counts <- Read10X_h5('filtered_feature_bc_matrix.h5')$Peaks
fragments <- CreateFragmentObject('atac_fragments.tsv.gz')

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

# ATAC QC
obj <- NucleosomeSignal(obj)
obj <- TSSEnrichment(obj)

# ATAC normalization
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')

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

# Load multimodal data
mdata = mu.read_10x_h5('filtered_feature_bc_matrix.h5')

# Access modalities
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'])

# Check how much each modality contributes per cell
weights <- obj@reductions$wnn@misc$weights

# Average weight by cluster
aggregate(weights, by = list(obj$seurat_clusters), mean)

import numpy as np

# Correlate RNA and protein for same genes/proteins
common = set(rna.var_names) & set(prot.var_names)
for gene in common:
    rna_expr = rna[:, gene].X.toarray().flatten()
    prot_expr = prot[:, gene].X.toarray().flatten()
    corr = np.corrcoef(rna_expr, prot_expr)[0, 1]
    print(f'{gene}: r={corr:.3f}')

# Find markers using both modalities
DefaultAssay(obj) <- 'RNA'
rna_markers <- FindAllMarkers(obj, only.pos = TRUE)

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

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

bio-single-cell-multimodal-integration

Summary

SKILL.md

Version Compatibility

Multimodal Integration

Common Modalities

CITE-seq Analysis (Seurat)

Load Data

QC and Normalization

Weighted Nearest Neighbor (WNN) Clustering

Visualize

10X Multiome (RNA + ATAC)

Load Data

Process ATAC

Joint Analysis

Scanpy/MuData (Python)

CITE-seq with MuData

Integration Metrics

Modality Weights

Correlation Between Modalities

Marker Discovery

Multi-Modal Markers

Related Skills

Related skills