Bioconductor Aucell
AUCell allows to identify cells with active gene sets (e.g.
Install
npx skills add bioMate-AI/biomate-bioconductor-kb --skill bioconductor-aucell -g -a claude-code -y- Repository
- bioMate-AI/biomate-bioconductor-kb793
- Category
- Engineering
- Updated
- 5mo ago
- License
- See repository
- Context cost
- Lightabout 1,310 tokens
- Runs scripts
- No, instructions only
What it does
AUCell allows to identify cells with active gene sets (e.g. signatures, gene modules...) in single-cell RNA-seq data. AUCell uses the "Area Under the Curve" (AUC) to calculate whether a critical subset of the input gene set is enriched with
AUCell
Dependencies & Environment
Package-intrinsic requirements from the Bioconductor landing page, reproduce in any R environment.
- Version: 1.34.0 · Bioconductor: 3.23 · R: ≥ 4.6
- Depends: DelayedMatrixStats, data.table, GSEABase, Matrix, mixtools, R.utils, SummarizedExperiment, BiocGenerics
- Imports: DelayedArray, DelayedMatrixStats, data.table, GSEABase, Matrix, mixtools, R.utils, SummarizedExperiment, BiocGenerics
- Install:
BiocManager::install("AUCell")
When to Use
- Single-Cell Signature Scoring: Identifying cells with active gene sets, signatures, or modules in single-cell RNA-seq data using
AUCell_run. - Normalization-Independent Scoring: Scoring gene sets across cells independently of the gene expression units and normalization procedures by building rank-based profiles with
AUCell_buildRankings. - Cell State Classification: Classifying cell types or states by exploring the bimodal distribution of signature activity scores and setting assignment thresholds with
AUCell_exploreThresholds. - Dimensionality Reduction Overlay: Visualizing the activity of specific gene signatures across cell populations by coloring t-SNE or UMAP plots using
AUCell_plotTSNE.
When NOT to Use
- Bulk RNA-seq GSEA: For bulk RNA-seq gene set enrichment analysis, use fgsea instead because AUCell is specifically optimized for evaluating signatures on individual cells in highly sparse single-cell data.
- Differential Expression of Single Genes: For differential expression of individual genes between clusters, use edgeR or scran instead because AUCell summarizes multi-gene signatures into a single AUC score per cell.
