# Bioconductor Aucell

AUCell allows to identify cells with active gene sets (e.g.

- Repository: https://github.com/bioMate-AI/biomate-bioconductor-kb (793 stars)
- Category: Engineering
- License: see repository
- Last updated: 2026-06-20
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add bioMate-AI/biomate-bioconductor-kb --skill bioconductor-aucell -g -a claude-code -y`
Codex: `npx skills add bioMate-AI/biomate-bioconductor-kb --skill bioconductor-aucell -g -a codex -y`
Cursor: `npx skills add bioMate-AI/biomate-bioconductor-kb --skill bioconductor-aucell -g -a cursor -y`
Gemini CLI: `npx skills add bioMate-AI/biomate-bioconductor-kb --skill bioconductor-aucell -g -a gemini-cli -y`

## Description

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

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# 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.

The rest: https://github.com/bioMate-AI/biomate-bioconductor-kb/blob/HEAD/skills/transcriptomics/aucell/SKILL.md