Version Compatibility

Reference examples tested with: anndata 0.10+, scanpy 1.10+, scikit-learn 1.4+, scvi-tools 1.1+

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.

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)

Goal: Remove batch effects from merged scRNA-seq datasets using Harmony's iterative correction of PCA embeddings.

Approach: Run PCA on merged data, iteratively adjust embeddings to mix batches while preserving biological variation, and use corrected embeddings for downstream analysis.

"Integrate my batches" → Merge samples, preprocess jointly, correct technical variation in the embedding space, and cluster on corrected coordinates.

R with Seurat

r
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

r
# 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

python
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)

Goal: Integrate batches using a deep generative model that learns a shared latent space.

Approach: Train a variational autoencoder (scVI) conditioned on batch to learn batch-invariant latent representations, then use the latent space for clustering and visualization.

python
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

python
# 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)

python
# 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)

Goal: Integrate batches using Seurat's anchor-based framework (CCA or RPCA).

Approach: Find shared biological anchors between datasets via canonical correlation analysis, then use anchors to correct expression values into a unified space.

CCA-based Integration

r
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)

r
# 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

r
# 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)

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

Goal: Assess whether integration successfully removed batch effects while preserving biological variation.

Approach: Compute mixing metrics (LISI, silhouette scores) and visualize batch versus cell-type separation before and after integration.

Mixing Metrics (R)

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

r
# 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)

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

Goal: Run end-to-end multi-sample integration from raw 10X files to clustered, integrated UMAP.

Approach: Load and merge samples, preprocess jointly, integrate with Harmony, and perform downstream clustering on corrected embeddings.

r
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)