Calculates single-sample enrichment AUCell (Aibar et al., 2017) using plaid back-end. The computation is 10-100x faster than the original code.
Usage
replaid.aucell(
X,
matG,
aucMaxRank = NULL,
assay = "logcounts",
min.genes = 5,
max.genes = 500
)Arguments
- X
Gene or protein expression matrix. Generally log transformed. See details. Genes on rows, samples on columns. Also accepts SummarizedExperiment or SingleCellExperiment objects.
- matG
Gene sets sparse matrix. Genes on rows, gene sets on columns. Also accepts BiocSet objects or GMT lists.
- aucMaxRank
Rank threshold (see AUCell paper). Default aucMaxRank = 0.05*nrow(X).
- assay
Character: assay name for Bioconductor objects. Default "logcounts".
- min.genes
Integer: minimum genes per gene set. Default 5.
- max.genes
Integer: maximum genes per gene set. Default 500.
Details
Computing the AUCell score requires to compute the ranks of the expression matrix and approximating the AUC of a gene set. We have wrapped this in a single convenience function.
We have extensively compared the results of replaid.aucell and
from the original AUCell R package and we showed good concordance
of results in the score, logFC and p-values.
Examples
# Create example expression matrix
set.seed(123)
X <- matrix(rnorm(500), nrow = 50, ncol = 10)
rownames(X) <- paste0("GENE", 1:50)
colnames(X) <- paste0("Sample", 1:10)
# Create example gene sets
gmt <- list(
"Pathway1" = paste0("GENE", 1:15),
"Pathway2" = paste0("GENE", 10:25)
)
matG <- gmt2mat(gmt)
# Compute AUCell scores
scores <- replaid.aucell(X, matG)
print(scores[1:2, 1:5])
#> Sample1 Sample2 Sample3 Sample4 Sample5
#> Pathway2 -9.368685 -9.352829 -9.351168 -9.357331 -9.416749
#> Pathway1 -9.386580 -9.390724 -9.370116 -9.392067 -9.356749