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.

Data Requirements

• Expression Matrix: A single-cell gene expression matrix with genes as rows and cells as columns. Ideally stored in a sparse format (e.g., dgCMatrix).
• Gene Sets: A list of gene sets or signatures to test, provided as a GeneSetCollection or a standard R list of character vectors.

Key Parameters

• exprMatrix: The input single-cell gene expression matrix.
• geneSets: The gene sets or signatures to evaluate.
• aucMaxRank: The number or percentage of top-ranked genes to use for calculating the AUC (default is often 5% of expressed genes).
• plotStats: Logical in AUCell_buildRankings to plot the histogram of the number of detected genes per cell.
• plotHist: Logical in AUCell_exploreThresholds to automatically plot all AUC histograms.
• assign: Logical in AUCell_exploreThresholds to return the list of cells with an AUC value over the calculated threshold.
• aucThr: Specific numeric threshold to manually set when plotting individual histograms with AUCell_plotHist.

Best Practices

• Convert the expression matrix to a sparse format (dgCMatrix) before running AUCell to significantly save memory.
• Check the distribution of detected genes per cell using plotGeneCount or plotStats=TRUE to ensure most cells have enough detected genes to support the chosen aucMaxRank.
• Do not rely blindly on automatic threshold selection; always manually inspect the AUC histograms generated by AUCell_exploreThresholds to ensure the bimodal distribution aligns with biological expectations.
• Save the intermediate cells_rankings object if you plan to test additional gene sets later, as building rankings is the most computationally intensive step.

Common Pitfalls

• Memory Exhaustion: Running AUCell_buildRankings on large datasets with dense matrices can exceed RAM. Fix by converting the input matrix to dgCMatrix and utilizing parallel processing via BPPARAM.
• Inappropriate aucMaxRank: Setting aucMaxRank higher than the number of genes detected in most cells includes noise from tied zero-expression genes. Fix by checking the quantiles from AUCell_buildRankings and adjusting aucMaxRank accordingly.
• Small Gene Sets: Using very small gene sets (<10 genes) makes it difficult to establish a stable bimodal distribution, often resulting in cells with an AUC of 0. Fix by using larger, more robust gene sets (100-2k genes) when possible.

Alternatives

• UCell: Uses Mann-Whitney U statistics to score gene signatures, differing by being specifically optimized for lower memory consumption on extremely large single-cell datasets.
• GSVA: Calculates sample-wise enrichment, differing by using a kernel density estimate that is computationally heavier and less suited for the sparsity of single-cell scales.

Citations

• Aibar et al. (2017) SCENIC: single-cell regulatory network inference and clustering. Nature Methods. doi: 10.1038/nmeth.4463

References

• Homepage: https://bioconductor.org/packages/AUCell
• Vignette: https://bioconductor.org/packages/release/bioc/vignettes/AUCell/inst/doc/AUCell.html

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