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name: bio-single-cell-batch-integration description: Integrate multiple scRNA-seq samples/batches using Harmony, scVI, Seurat anchors, and fastMNN. Remove technical variation while preserving biological differences. Use when integrating multiple scRNA-seq batches or datasets. tool_type: mixed primary_tool: Harmony measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools:

• read_file
• run_shell_command

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Batch Integration

Integrate multiple scRNA-seq datasets to remove batch effects while preserving biological variation.

Tool Comparison

Tool	Speed	Scalability	Best For
Harmony	Fast	Good	Quick integration, most use cases
scVI	Moderate	Excellent	Large datasets, deep learning
Seurat CCA/RPCA	Moderate	Good	Conserved biology across batches
fastMNN	Fast	Good	MNN-based correction

Harmony (R/Python)

R with Seurat

library(Seurat)
library(harmony)

# Merge datasets first
merged <- merge(sample1, y = list(sample2, sample3), add.cell.ids = c('S1', 'S2', 'S3'))

# Standard preprocessing
merged <- NormalizeData(merged)
merged <- FindVariableFeatures(merged)
merged <- ScaleData(merged)
merged <- RunPCA(merged)

# Run Harmony on PCA embeddings
merged <- RunHarmony(merged, group.by.vars = 'orig.ident', dims.use = 1:30)

# Use harmony embeddings for downstream
merged <- RunUMAP(merged, reduction = 'harmony', dims = 1:30)
merged <- FindNeighbors(merged, reduction = 'harmony', dims = 1:30)
merged <- FindClusters(merged, resolution = 0.5)

Multiple Batch Variables

# Correct for both sample and technology
merged <- RunHarmony(merged, group.by.vars = c('sample', 'technology'),
                     dims.use = 1:30, max.iter.harmony = 20)

Python with Scanpy

import scanpy as sc
import scanpy.external as sce

adata = sc.read_h5ad('merged.h5ad')

# Standard preprocessing
sc.pp.normalize_total(adata, target_sum=1e4)
sc.pp.log1p(adata)
sc.pp.highly_variable_genes(adata, batch_key='batch')
adata = adata[:, adata.var.highly_variable]
sc.pp.scale(adata)
sc.tl.pca(adata)

# Run Harmony
sce.pp.harmony_integrate(adata, key='batch')

# Use corrected embedding
sc.pp.neighbors(adata, use_rep='X_pca_harmony')
sc.tl.umap(adata)
sc.tl.leiden(adata)

scVI (Python)

Deep learning-based integration for large datasets.

import scvi
import scanpy as sc

adata = sc.read_h5ad('merged.h5ad')

# Setup for scVI
scvi.model.SCVI.setup_anndata(adata, batch_key='batch')

# Train model
model = scvi.model.SCVI(adata, n_latent=30, n_layers=2)
model.train(max_epochs=100, early_stopping=True)

# Get latent representation
adata.obsm['X_scVI'] = model.get_latent_representation()

# Use for downstream
sc.pp.neighbors(adata, use_rep='X_scVI')
sc.tl.umap(adata)
sc.tl.leiden(adata)

scVI with Covariates

# Include continuous covariates
scvi.model.SCVI.setup_anndata(adata, batch_key='batch',
                               continuous_covariate_keys=['percent_mito'])

model = scvi.model.SCVI(adata, n_latent=30)
model.train()

scANVI (with cell type labels)

# If you have reference labels for some cells
scvi.model.SCANVI.setup_anndata(adata, batch_key='batch', labels_key='cell_type',
                                 unlabeled_category='Unknown')

model = scvi.model.SCANVI(adata, n_latent=30)
model.train(max_epochs=100)

# Predict labels for unlabeled cells
adata.obs['predicted_type'] = model.predict()

Seurat Integration (R)

CCA-based Integration

library(Seurat)

# Split by batch
obj_list <- SplitObject(merged, split.by = 'batch')

# Normalize each
obj_list <- lapply(obj_list, function(x) {
    x <- NormalizeData(x)
    x <- FindVariableFeatures(x, selection.method = 'vst', nfeatures = 2000)
    return(x)
})

# Find integration anchors
anchors <- FindIntegrationAnchors(object.list = obj_list, dims = 1:30)

# Integrate
integrated <- IntegrateData(anchorset = anchors, dims = 1:30)

# Switch to integrated assay for downstream
DefaultAssay(integrated) <- 'integrated'
integrated <- ScaleData(integrated)
integrated <- RunPCA(integrated)
integrated <- RunUMAP(integrated, dims = 1:30)

RPCA (Faster for Large Datasets)

# Use reciprocal PCA for faster integration
anchors <- FindIntegrationAnchors(object.list = obj_list, dims = 1:30,
                                   reduction = 'rpca')
integrated <- IntegrateData(anchorset = anchors, dims = 1:30)

Seurat v5 Integration

# Seurat v5 uses layers
merged[['RNA']] <- split(merged[['RNA']], f = merged$batch)
merged <- IntegrateLayers(merged, method = CCAIntegration, orig.reduction = 'pca',
                          new.reduction = 'integrated.cca')
merged <- JoinLayers(merged)

fastMNN (R)

library(batchelor)
library(SingleCellExperiment)

# Convert Seurat to SCE
sce <- as.SingleCellExperiment(merged)

# Run fastMNN
corrected <- fastMNN(sce, batch = sce$batch, d = 30, k = 20)

# Extract corrected values
reducedDim(sce, 'MNN') <- reducedDim(corrected, 'corrected')

Evaluate Integration

Mixing Metrics (R)

# LISI score (lower = more mixed)
library(lisi)
lisi_scores <- compute_lisi(Embeddings(merged, 'harmony'),
                            merged@meta.data, c('batch', 'cell_type'))

# Batch mixing should be high, cell type separation preserved
mean(lisi_scores$batch)      # Want high
mean(lisi_scores$cell_type)  # Want low (preserved)

Visual Assessment

# Before integration
DimPlot(merged, reduction = 'pca', group.by = 'batch')
DimPlot(merged, reduction = 'pca', group.by = 'cell_type')

# After integration
DimPlot(merged, reduction = 'harmony', group.by = 'batch')
DimPlot(merged, reduction = 'harmony', group.by = 'cell_type')

Silhouette Score (Python)

from sklearn.metrics import silhouette_score

# Batch silhouette (want low - batches mixed)
batch_sil = silhouette_score(adata.obsm['X_scVI'], adata.obs['batch'])

# Cell type silhouette (want high - types separated)
celltype_sil = silhouette_score(adata.obsm['X_scVI'], adata.obs['cell_type'])

Complete Workflow

library(Seurat)
library(harmony)

# Load and merge samples
samples <- list.files('data/', pattern = '*.h5', full.names = TRUE)
obj_list <- lapply(samples, Read10X_h5)
names(obj_list) <- gsub('.h5', '', basename(samples))

merged <- merge(CreateSeuratObject(obj_list[[1]], project = names(obj_list)[1]),
                y = lapply(2:length(obj_list), function(i)
                    CreateSeuratObject(obj_list[[i]], project = names(obj_list)[i])))

# QC
merged[['percent.mt']] <- PercentageFeatureSet(merged, pattern = '^MT-')
merged <- subset(merged, nFeature_RNA > 200 & nFeature_RNA < 5000 & percent.mt < 20)

# Preprocess
merged <- NormalizeData(merged)
merged <- FindVariableFeatures(merged, nfeatures = 2000)
merged <- ScaleData(merged, vars.to.regress = 'percent.mt')
merged <- RunPCA(merged, npcs = 50)

# Integrate with Harmony
merged <- RunHarmony(merged, group.by.vars = 'orig.ident')

# Downstream analysis on integrated data
merged <- RunUMAP(merged, reduction = 'harmony', dims = 1:30)
merged <- FindNeighbors(merged, reduction = 'harmony', dims = 1:30)
merged <- FindClusters(merged, resolution = 0.5)

DimPlot(merged, group.by = c('orig.ident', 'seurat_clusters'), ncol = 2)

When to Use Each Method

Scenario	Recommended
Quick integration, most cases	Harmony
Large datasets (>500k cells)	scVI or Harmony
Strong batch effects	scVI
Reference mapping	Seurat anchors or scANVI
Preserving rare populations	fastMNN

Related Skills

• single-cell/preprocessing - QC before integration
• single-cell/clustering - Clustering after integration
• single-cell/cell-annotation - Annotation after integration
• single-cell/multimodal-integration - Multi-omic integration (different from batch)

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