Calculates regulatory activities using AUCell.
Arguments
- mat
Matrix to evaluate (e.g. expression matrix). Target nodes in rows and conditions in columns.
rownames(mat)
must have at least one intersection with the elements innetwork
.target
column.- network
Tibble or dataframe with edges and it's associated metadata.
- .source
Column with source nodes.
- .target
Column with target nodes.
- aucMaxRank
Threshold to calculate the AUC.
- nproc
Number of cores to use for computation.
- seed
A single value, interpreted as an integer, or NULL for random number generation.
- minsize
Integer indicating the minimum number of targets per source.
Details
AUCell (Aibar et al., 2017) uses the Area Under the Curve (AUC) to calculate
whether a set of targets is enriched within the molecular readouts of each
sample. To do so, AUCell first ranks the molecular features of each sample
from highest to lowest value, resolving ties randomly. Then, an AUC can be
calculated using by default the top 5% molecular features in the ranking.
Therefore, this metric, aucell
, represents the proportion of
abundant molecular features in the target set, and their relative abundance
value compared to the other features within the sample.
Aibar S. et al. (2017) Scenic: single-cell regulatory network inference and clustering. Nat. Methods, 14, 1083–1086.
Examples
inputs_dir <- system.file("testdata", "inputs", package = "decoupleR")
mat <- readRDS(file.path(inputs_dir, "mat.rds"))
net <- readRDS(file.path(inputs_dir, "net.rds"))
run_aucell(mat, net, minsize=0, nproc=1, aucMaxRank=3)
#> # A tibble: 72 × 4
#> statistic source condition score
#> <chr> <chr> <chr> <dbl>
#> 1 aucell T1 S01 0.667
#> 2 aucell T1 S02 0.667
#> 3 aucell T1 S03 0.667
#> 4 aucell T1 S04 1
#> 5 aucell T1 S05 0.333
#> 6 aucell T1 S06 0.333
#> 7 aucell T1 S07 1
#> 8 aucell T1 S08 1
#> 9 aucell T1 S09 1
#> 10 aucell T1 S10 1
#> # ℹ 62 more rows