dot-compute_aucell_scores: Compute AUCell-like rank-based scores
In SlimR: Adaptive Machine Learning-Powered, Context-Matching Tool for Single-Cell and Spatial Transcriptomics Annotation
.compute_aucell_scores R Documentation
Compute AUCell-like rank-based scores
Description
Uses a ranking approach similar to AUCell: for each cell, genes are ranked by
expression, and the score is based on where marker genes fall in that ranking.
This method is robust to batch effects and technical variation.
Usage
.compute_aucell_scores(expr_matrix, marker_sets, top_percent = 0.05)
Details
Key improvement: Uses recovery curve area under curve (AUC) calculation
rather than simple proportion, giving partial credit to markers ranked
just outside the top threshold.
SlimR documentation built on Feb. 5, 2026, 5:08 p.m.
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| .compute_aucell_scores | R Documentation |
Compute AUCell-like rank-based scores
Description
Uses a ranking approach similar to AUCell: for each cell, genes are ranked by expression, and the score is based on where marker genes fall in that ranking. This method is robust to batch effects and technical variation.
Usage
.compute_aucell_scores(expr_matrix, marker_sets, top_percent = 0.05)
Details
Key improvement: Uses recovery curve area under curve (AUC) calculation rather than simple proportion, giving partial credit to markers ranked just outside the top threshold.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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