bio-single-cell-batch-integration

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

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

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 =  dims  
merged  FindNeighborsmerged reduction   dims  
merged  FindClustersmerged resolution  

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)

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.

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)

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

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
DefaultAssayintegrated  
integrated  ScaleDataintegrated
integrated  RunPCAintegrated
integrated  RunUMAPintegrated dims  

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

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)

# LISI score (lower = more mixed)
library(lisi)
lisi_scores <- compute_lisi(Embeddings(merged, 'harmony'),
                            [e-mail], 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

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.

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), functioni
                    CreateSeuratObjectobj_listi project  obj_listi


merged  PercentageFeatureSetmerged pattern  
merged  subsetmerged nFeature_RNA    nFeature_RNA    percent.mt  


merged  NormalizeDatamerged
merged  FindVariableFeaturesmerged nfeatures  
merged  ScaleDatamerged vars.to.regress  
merged  RunPCAmerged npcs  


merged  RunHarmonymerged group.by.vars  


merged  RunUMAPmerged reduction   dims  
merged  FindNeighborsmerged reduction   dims  
merged  FindClustersmerged resolution  

DimPlotmerged group.by    ncol  

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)