SingleR

Dependencies & Environment

Package-intrinsic requirements from the Bioconductor landing page — reproduce in any R environment.

• Version: 2.14.0 · Bioconductor: 3.23 · R: ≥ 4.6
• Depends: SummarizedExperiment
• Imports: Matrix, BiocGenerics, S4Vectors, DelayedArray, Rcpp, beachmat
• System requirements: C++17
• Install: BiocManager::install("SingleR")

When to Use

• Annotating unlabelled single-cell datasets using pre-labelled reference datasets (e.g., HumanPrimaryCellAtlasData()).
• Propagating biological knowledge to new datasets in an automated manner without manually interpreting clusters.
• Resolving closely related cell type labels using fine-tuning.

When NOT to Use

• For unsupervised clustering without a reference (use scran instead because SingleR requires known labels).
• For manual marker gene definition and cluster interpretation (use Seurat instead because SingleR automates this using a reference).

Data Requirements

• Test dataset: Unlogged counts are acceptable, as only ranks are used by SingleR().
• Reference dataset: Must be normalized and log-transformed (e.g., using normalizeRnaCounts.se()).

Key Parameters

• test: The unlabelled single-cell dataset to be annotated.
• ref: The pre-labelled reference dataset.
• labels: The cell type labels corresponding to the reference dataset.
• assay.type.test: The assay index or name to use from the test dataset.
• de.method: The marker detection mode (e.g., "wilcox" for single-cell references).

Best Practices

• Perform cell-based quality control before running SingleR().
• Normalize and log-transform reference datasets using normalizeRnaCounts.se().
• Inspect assignment confidence using plotScoreHeatmap() and plotDeltaDistribution().
• Remove poor-quality or ambiguous assignments using pruneScores().
• Examine the expression of marker genes for each label in the test dataset using plotMarkerHeatmap().

Common Pitfalls

• Using raw counts for the reference (fix: log-transform and normalize the reference first using normalizeRnaCounts.se()).
• Default marker detection failing on low-coverage data (fix: use de.method="wilcox" to consider variance of expression across cells).
• Uncertain assignments for cells whose true label does not exist in the reference (fix: inspect per-cell deltas and use pruneScores() to replace low-quality assignments with NA).

Alternatives

• Seurat (for manual marker gene definition and cluster interpretation).
• scmap (for projection-based single-cell annotation).

Citations

• Aran, D., et al. 2019. "Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage." Nat. Immunol. 20 (2): 163–72.
• Grun, D., et al. 2016. "De Novo Prediction of Stem Cell Identity using Single-Cell Transcriptome Data." Cell Stem Cell 19 (2): 266–77.

References

• Homepage: bioconductor.org/packages/SingleR
• Vignette: vignette_0_9645ffcc.txt

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