| name | bioconductor-scdblfinder |
| description | The scDblFinder package gathers various methods for the detection and handling of doublets/multiplets in single-cell sequencing data (i.e. multiple cells captured within the same droplet or reaction volume). It includes methods formerly fou |
| when_to_use | Use when: Identifying heterotypic doublets in single-cell RNA sequencing data using the scDblFinder iterative classifier.; Detecting doublets in multiplexed samples by processing each capture separately using the samples argument.; Testing for specific doublet type enrichments (e.g., combinations of clusters) using doubletPairwiseEnrichment or clusterStickiness.; Recovering intra-sample doublets that are ne. Not for: For identifying homotypic doublets or inter-sample doublets in multiplexed experiments (use cell hashing or genotype-based SNP calls instead, as scDblFinder focuses on transcriptionally distinct heterotypic doublets). |
| user-invocable | false |
scDblFinder
Dependencies & Environment
Package-intrinsic requirements from the Bioconductor landing page — reproduce in any R environment.
- Version: 1.26.0 · Bioconductor: 3.23 · R: ≥ 4.6
- Depends: SingleCellExperiment
- Imports: igraph, Matrix, BiocGenerics, BiocParallel, BiocNeighbors, BiocSingular, S4Vectors, SummarizedExperiment, scran, scater, scuttle, bluster, DelayedArray, xgboost, MASS, IRanges, GenomicRanges, GenomeInfoDb, Rsamtools, rtracklayer
- Install:
BiocManager::install("scDblFinder")
When to Use
- Identifying heterotypic doublets in single-cell RNA sequencing data using the
scDblFinder iterative classifier.
- Detecting doublets in multiplexed samples by processing each capture separately using the
samples argument.
- Testing for specific doublet type enrichments (e.g., combinations of clusters) using
doubletPairwiseEnrichment or clusterStickiness.
- Recovering intra-sample doublets that are neighbors to known inter-sample doublets (e.g., from cell hashing) using
recoverDoublets.
When NOT to Use
- For identifying homotypic doublets or inter-sample doublets in multiplexed experiments (use cell hashing or genotype-based SNP calls instead, as
scDblFinder focuses on transcriptionally distinct heterotypic doublets).
Data Requirements
- A
SingleCellExperiment object or a simple count matrix containing raw counts (assay 'counts').
- Empty droplets should already be removed prior to running the tool.
Key Parameters
- samples (NULL): Column name in
colData or a vector indicating the sample/capture of origin to process batches separately.
- clusters (FALSE): Cluster labels or
TRUE to use fastcluster; enables cluster-based artificial doublet generation.
- dbr (NULL): Expected proportion of doublets; if omitted, automatically estimated based on the number of captured cells.
- dbr.per1k (0.008): Expected doublet rate per thousand cells, defaulting to standard 10X rates.
- artificialDoublets (NULL): Number of artificial doublets to generate; defaults to roughly the number of cells.
- nfeatures (1000): Number of top expressed genes to retain for dimensionality reduction to speed up analysis.
Best Practices
- Always split multi-sample datasets by providing the
samples argument so doublets are sought independently for each capture.
- Use the cluster-based approach (
clusters=TRUE) if your dataset has a very clear cluster structure to avoid generating unidentifiable homotypic artificial doublets.
- Check the distribution of doublet scores (
hist(sce$scDblFinder.score)); a bimodal distribution indicates successful doublet identification.
- If running multiple samples with the cluster-based approach, pre-cluster all samples together using
fastcluster and pass the labels to the clusters argument to ensure concordant labels across samples.
Common Pitfalls
- Way too many doublets called: Often caused by pooling multiple samples without specifying the
samples argument, making the tool assume a massive single capture. Fix: Provide the samples argument to split cells by capture/batch.
- 'Size factors should be positive' error: Caused by cells with zero reads or very low read counts that drop to zero after feature selection. Fix: Filter out empty droplets and extremely low-count cells before running
scDblFinder.
- Nonsensical cluster labels across samples: When running multiple samples without providing cluster labels,
scDblFinder clusters them sample-wise, leading to mismatched labels. Fix: Pre-cluster all samples together using fastcluster and pass the labels to the clusters argument.
Alternatives
- computeDoubletDensity: A simpler density-based method (formerly
scran::doubletCells) that identifies cells with a high local density of artificial doublets.
- findDoubletClusters: Identifies entire clusters that are likely composed of doublets by checking if their expression profile lies between two other clusters.
- directDblClassification: Trains a classifier directly on gene expression without the kNN step, though generally yields worse predictions than
scDblFinder.
Citations
- Germain, P. L., Lun, A., Macnair, W., & Robinson, M. D. (2021). Doublet identification in single-cell sequencing data using scDblFinder. F1000Research, 10, 979.
- Howitt et al., 2024. (Referenced regarding doublets in 10x Flex datasets).
References
- Homepage: bioconductor.org/packages/scDblFinder
- Vignette: vignette_0_d20f7c0c.txt
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