Agent skill

bio-single-cell-doublet-detection

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.

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SKILL.md

Version Compatibility

Reference examples tested with: matplotlib 3.8+, 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.

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)

Goal: Detect and score doublets in scRNA-seq data using simulated doublet profiles.

Approach: Simulate artificial doublets by combining random cell pairs, embed real and simulated cells together, and score each cell's similarity to simulated doublets.

"Remove doublets from my data" → Identify droplets containing multiple cells by comparing each cell's profile to computationally simulated doublets, then filter flagged cells.

Basic Usage

python
import 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

python
scrub = 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

python
import 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

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

Set Manual Threshold

python
scrub = 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)

Goal: Detect doublets in Seurat objects using DoubletFinder's pANN-based classification.

Approach: Optimize the pK neighborhood parameter via parameter sweep, compute artificial nearest neighbor proportions, and classify cells as singlets or doublets.

Basic Usage

r
library(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

r
seurat_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

r
df_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

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

scDblFinder (R/Bioconductor)

Goal: Detect doublets using scDblFinder's gradient-boosted classifier for fast, accurate identification.

Approach: Simulate doublets, train a gradient boosting classifier on real vs simulated profiles, and score each cell.

Basic Usage

r
library(scDblFinder)
library(SingleCellExperiment)

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

table(sce$scDblFinder.class)

From Seurat Object

r
library(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

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

Adjust Parameters

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

Expected Doublet Rates

Cells Loaded Expected Rate
1,000 ~0.8%
2,000 ~1.6%
5,000 ~4.0%
10,000 ~8.0%
15,000 ~12%

Formula: rate ≈ cells_loaded / 1000 * 0.008

Compare Methods

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

Scanpy Integration Pipeline

Goal: Run doublet detection as part of a complete Scanpy preprocessing workflow.

Approach: Detect and remove doublets with Scrublet before QC filtering, then proceed through normalization, HVG selection, and clustering.

python
import 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

Goal: Run DoubletFinder as part of a complete Seurat preprocessing workflow.

Approach: Preprocess and cluster, run DoubletFinder parameter sweep and classification, filter doublets, then re-preprocess clean singlets.

r
library(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

Method Speed Accuracy Language
Scrublet Fast Good Python
DoubletFinder Slow Good R
scDblFinder Fast Excellent R

Related Skills

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

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