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name: bio-single-cell-doublet-detection description: Detect and remove doublets (multiple cells captured in one droplet) from single-cell RNA-seq data. Uses Scrublet (Python), DoubletFinder (R), and scDblFinder (R). Essential QC step before clustering to avoid artificial cell populations. Use when identifying and removing doublets from scRNA-seq data. tool_type: mixed primary_tool: Scrublet

Doublet Detection

Doublets are droplets containing two or more cells. They appear as artificial intermediate cell populations and must be removed before analysis.

Scrublet (Python)

Fast doublet detection based on simulated doublets from the data.

Basic Usage

Copyimport scrublet as scr
import scanpy as sc
import numpy as np

adata = sc.read_10x_mtx('filtered_feature_bc_matrix/')

scrub = scr.Scrublet(adata.X, expected_doublet_rate=0.06)
doublet_scores, predicted_doublets = scrub.scrub_doublets()

adata.obs['doublet_score'] = doublet_scores
adata.obs['predicted_doublet'] = predicted_doublets

print(f'Detected {predicted_doublets.sum()} doublets ({100*predicted_doublets.mean():.1f}%)')

Adjust Parameters

Copyscrub = scr.Scrublet(adata.X, expected_doublet_rate=0.06)
doublet_scores, predicted_doublets = scrub.scrub_doublets(
    min_counts=2,
    min_cells=3,
    min_gene_variability_pctl=85,
    n_prin_comps=30,
    synthetic_doublet_umi_subsampling=1.0
)

Visualize Doublet Scores

Copyimport matplotlib.pyplot as plt

scrub.plot_histogram()
plt.savefig('doublet_histogram.pdf')

# UMAP with doublet scores
sc.pp.normalize_total(adata, target_sum=1e4)
sc.pp.log1p(adata)
sc.pp.highly_variable_genes(adata)
sc.pp.pca(adata)
sc.pp.neighbors(adata)
sc.tl.umap(adata)

sc.pl.umap(adata, color=['doublet_score', 'predicted_doublet'], save='_doublets.pdf')

Filter Doublets

Copyadata_filtered = adata[~adata.obs['predicted_doublet']].copy()
print(f'Kept {adata_filtered.n_obs} cells after doublet removal')

Set Manual Threshold

Copyscrub = scr.Scrublet(adata.X)
doublet_scores, _ = scrub.scrub_doublets()

threshold = 0.25
predicted_doublets = doublet_scores > threshold
adata.obs['predicted_doublet'] = predicted_doublets

DoubletFinder (R)

Popular R package for doublet detection in Seurat workflows.

Basic Usage

Copylibrary(Seurat)
library(DoubletFinder)

seurat_obj <- Read10X(data.dir = 'filtered_feature_bc_matrix/')
seurat_obj <- CreateSeuratObject(counts = seurat_obj, min.cells = 3, min.features = 200)

seurat_obj <- NormalizeData(seurat_obj)
seurat_obj <- FindVariableFeatures(seurat_obj)
seurat_obj <- ScaleData(seurat_obj)
seurat_obj <- RunPCA(seurat_obj)
seurat_obj <- RunUMAP(seurat_obj, dims = 1:20)
seurat_obj <- FindNeighbors(seurat_obj, dims = 1:20)
seurat_obj <- FindClusters(seurat_obj, resolution = 0.5)

sweep.res <- paramSweep(seurat_obj, PCs = 1:20, sct = FALSE)
sweep.stats <- summarizeSweep(sweep.res, GT = FALSE)
bcmvn <- find.pK(sweep.stats)

optimal_pk <- as.numeric(as.character(bcmvn$pK[which.max(bcmvn$BCmetric)]))

nExp_poi <- round(0.06 * nrow(seurat_obj@meta.data))
seurat_obj <- doubletFinder(seurat_obj, PCs = 1:20, pN = 0.25, pK = optimal_pk,
                             nExp = nExp_poi, reuse.pANN = FALSE, sct = FALSE)

colnames(seurat_obj@meta.data)

With SCTransform

Copyseurat_obj <- SCTransform(seurat_obj)
seurat_obj <- RunPCA(seurat_obj)
seurat_obj <- RunUMAP(seurat_obj, dims = 1:30)
seurat_obj <- FindNeighbors(seurat_obj, dims = 1:30)
seurat_obj <- FindClusters(seurat_obj, resolution = 0.5)

sweep.res <- paramSweep(seurat_obj, PCs = 1:30, sct = TRUE)
sweep.stats <- summarizeSweep(sweep.res, GT = FALSE)
bcmvn <- find.pK(sweep.stats)

optimal_pk <- as.numeric(as.character(bcmvn$pK[which.max(bcmvn$BCmetric)]))
nExp_poi <- round(0.06 * nrow(seurat_obj@meta.data))

seurat_obj <- doubletFinder(seurat_obj, PCs = 1:30, pN = 0.25, pK = optimal_pk,
                             nExp = nExp_poi, reuse.pANN = FALSE, sct = TRUE)

Filter Doublets

Copydf_col <- grep('DF.classifications', colnames(seurat_obj@meta.data), value = TRUE)
seurat_obj$doublet <- seurat_obj@meta.data[[df_col]]

DimPlot(seurat_obj, group.by = 'doublet')

seurat_obj <- subset(seurat_obj, subset = doublet == 'Singlet')

Adjust Expected Doublet Rate

Copyn_cells <- ncol(seurat_obj)
doublet_rate <- n_cells / 1000 * 0.008
nExp_poi <- round(doublet_rate * n_cells)

scDblFinder (R/Bioconductor)

Fast Bioconductor package using gradient boosting for doublet detection.

Basic Usage

Copylibrary(scDblFinder)
library(SingleCellExperiment)

sce <- SingleCellExperiment(assays = list(counts = counts_matrix))
sce <- scDblFinder(sce)

table(sce$scDblFinder.class)

From Seurat Object

Copylibrary(scDblFinder)
library(Seurat)

sce <- as.SingleCellExperiment(seurat_obj)

sce <- scDblFinder(sce)

seurat_obj$scDblFinder_class <- sce$scDblFinder.class
seurat_obj$scDblFinder_score <- sce$scDblFinder.score

DimPlot(seurat_obj, group.by = 'scDblFinder_class')

seurat_obj <- subset(seurat_obj, subset = scDblFinder_class == 'singlet')

Multi-Sample Processing

Copysce <- scDblFinder(sce, samples = 'sample_id')

Adjust Parameters

Copysce <- scDblFinder(sce,
    dbr = 0.06,
    dbr.sd = 0.015,
    nfeatures = 1500,
    dims = 20,
    k = 30
)

Expected Doublet Rates

Formula: rate ≈ cells_loaded / 1000 * 0.008

Compare Methods

Copylibrary(scDblFinder)

seurat_obj$scrublet <- scrublet_results
sce <- as.SingleCellExperiment(seurat_obj)
sce <- scDblFinder(sce)
seurat_obj$scDblFinder <- sce$scDblFinder.class

DimPlot(seurat_obj, group.by = c('doublet', 'scDblFinder', 'scrublet'), ncol = 3)

table(seurat_obj$doublet, seurat_obj$scDblFinder)

Handling Heterotypic vs Homotypic Doublets

Heterotypic Doublets

• Two different cell types
• Easier to detect (intermediate expression)
• All methods handle well

Homotypic Doublets

• Same cell type
• Harder to detect (no intermediate signature)
• May have higher total counts

Copyadata.obs['log_counts'] = np.log1p(adata.obs['total_counts'])
sc.pl.violin(adata, 'log_counts', groupby='predicted_doublet')

Scanpy Integration Pipeline

Copyimport scanpy as sc
import scrublet as scr

adata = sc.read_10x_mtx('filtered_feature_bc_matrix/')

adata.var['mt'] = adata.var_names.str.startswith('MT-')
sc.pp.calculate_qc_metrics(adata, qc_vars=['mt'], inplace=True)

scrub = scr.Scrublet(adata.X, expected_doublet_rate=0.06)
doublet_scores, predicted_doublets = scrub.scrub_doublets()
adata.obs['doublet_score'] = doublet_scores
adata.obs['is_doublet'] = predicted_doublets

print(f'Before filtering: {adata.n_obs} cells')
adata = adata[~adata.obs['is_doublet']].copy()
adata = adata[adata.obs['pct_counts_mt'] < 20].copy()
print(f'After filtering: {adata.n_obs} cells')

sc.pp.normalize_total(adata, target_sum=1e4)
sc.pp.log1p(adata)
sc.pp.highly_variable_genes(adata)
sc.pp.pca(adata)
sc.pp.neighbors(adata)
sc.tl.umap(adata)
sc.tl.leiden(adata)

Seurat Integration Pipeline

Copylibrary(Seurat)
library(DoubletFinder)

seurat_obj <- Read10X('filtered_feature_bc_matrix/')
seurat_obj <- CreateSeuratObject(counts = seurat_obj, min.cells = 3, min.features = 200)

seurat_obj[['percent.mt']] <- PercentageFeatureSet(seurat_obj, pattern = '^MT-')

seurat_obj <- NormalizeData(seurat_obj)
seurat_obj <- FindVariableFeatures(seurat_obj)
seurat_obj <- ScaleData(seurat_obj)
seurat_obj <- RunPCA(seurat_obj)
seurat_obj <- RunUMAP(seurat_obj, dims = 1:20)
seurat_obj <- FindNeighbors(seurat_obj, dims = 1:20)
seurat_obj <- FindClusters(seurat_obj, resolution = 0.5)

sweep.res <- paramSweep(seurat_obj, PCs = 1:20)
sweep.stats <- summarizeSweep(sweep.res)
bcmvn <- find.pK(sweep.stats)
pk <- as.numeric(as.character(bcmvn$pK[which.max(bcmvn$BCmetric)]))
nExp <- round(0.06 * ncol(seurat_obj))

seurat_obj <- doubletFinder(seurat_obj, PCs = 1:20, pN = 0.25, pK = pk, nExp = nExp)

df_col <- grep('DF.classifications', colnames(seurat_obj@meta.data), value = TRUE)
seurat_obj <- subset(seurat_obj, cells = colnames(seurat_obj)[seurat_obj@meta.data[[df_col]] == 'Singlet'])
seurat_obj <- subset(seurat_obj, subset = percent.mt < 20)

seurat_obj <- NormalizeData(seurat_obj)
seurat_obj <- FindVariableFeatures(seurat_obj)
seurat_obj <- ScaleData(seurat_obj)
seurat_obj <- RunPCA(seurat_obj)
seurat_obj <- RunUMAP(seurat_obj, dims = 1:20)
seurat_obj <- FindNeighbors(seurat_obj, dims = 1:20)
seurat_obj <- FindClusters(seurat_obj)

Method Comparison

Related Skills

• preprocessing - QC before doublet detection
• clustering - Run after filtering doublets
• data-io - Load data before processing