Performs unbiased cell type recognition from single-cell RNA sequencing data, by leveraging reference transcriptomic datasets of pure cell types to infer the cell of origin of each single cell independently.
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Use bioconductor-singler to annotate [unlabelled single-cell dataset] against [pre-labelled normalized, log-transformed reference dataset] with SingleR, including QC-aware confidence assessment and identification of ambiguous assignments.
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SKILL.md
name: bioconductor-singlerdescription: Performs unbiased cell type recognition from single-cell RNA sequencing data, by leveraging reference transcriptomic datasets of pure cell types to infer the cell of origin of each single cell independently.when_to_use: "Use when: 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.. Not for: 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)."user-invocable: false
SingleR
Dependencies & Environment
Package-intrinsic requirements from the Bioconductor landing page — reproduce in any R environment.
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).
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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