Summary This source did not publish a separate summary. Review SKILL.md before using the skill.
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
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
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 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 adata_filtered = adata[~adata.obs['predicted_doublet']].copy()
print(f'Kept {adata_filtered.n_obs} cells after doublet removal')
Set Manual Threshold 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 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([email protected] ))
seurat_obj <- doubletFinder(seurat_obj, PCs = 1:20, pN = 0.25, pK = optimal_pk,
nExp = nExp_poi, reuse.pANN = FALSE, sct = FALSE)
colnames([email protected] )
With SCTransform 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([email protected] ))
seurat_obj <- doubletFinder(seurat_obj, PCs = 1:30, pN = 0.25, pK = optimal_pk,
nExp = nExp_poi, reuse.pANN = FALSE, sct = TRUE)
Filter Doublets df_col <- grep('DF.classifications', colnames([email protected] ), value = TRUE)
seurat_obj$doublet <- [email protected] [[df_col]]
DimPlot(seurat_obj, group.by = 'doublet')
seurat_obj <- subset(seurat_obj, subset = doublet == 'Singlet')
Adjust Expected Doublet Rate n_cells <- ncol(seurat_obj)
doublet_rate <- n_cells / 1000 * 0.008
nExp_poi <- round(doublet_rate * n_cells)
scDblFinder (R/Bioconductor) Goal: Detect doublets using scDblFinder's gradient-boosted classifier for fast, accurate identification.
Approach: Simulate doublets, train a gradient boosting classifier on real vs simulated profiles, and score each cell.
Basic Usage library(scDblFinder)
library(SingleCellExperiment)
sce <- SingleCellExperiment(assays = list(counts = counts_matrix))
sce <- scDblFinder(sce)
table(sce$scDblFinder.class)
From Seurat Object 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 sce <- scDblFinder(sce, samples = 'sample_id')
Adjust Parameters 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 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
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.
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.
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([email protected] ), value = TRUE)
seurat_obj <- subset(seurat_obj, cells = colnames(seurat_obj)[[email protected] [[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
