scale and transform score and output top markers for groups
top_markers(
data,
label,
n = 10,
use.glm = TRUE,
batch = NULL,
scale = TRUE,
use.mgm = TRUE,
softmax = TRUE,
slot = "score",
...
)
# S4 method for class 'AnyMatrix'
top_markers(
data,
label,
n = 10,
use.glm = TRUE,
batch = NULL,
scale = TRUE,
use.mgm = TRUE,
softmax = TRUE,
slot = "score",
...
)
# S4 method for class 'SummarizedExperiment'
top_markers(
data,
label,
n = 10,
use.glm = TRUE,
batch = NULL,
scale = TRUE,
use.mgm = TRUE,
softmax = TRUE,
slot = "score",
...
)an expression object, can be matrix or SummarizedExperiment
a vector of group label
integer, number of returned top genes for each group
logical, if to use stats::glm() to compute group mean score,
if TRUE, also compute mean score difference as output
a vector of batch labels, default NULL
logical, if to scale data by row
logical, if to scale data using scale_mgm()
logical, if to apply softmax transformation on output
a character, specify which slot to use when data is se object, optional, default 'score'
params for top_markers_abs() or top_markers_glm()
A tibble with top n feature names, group labels and ordered scores
data <- matrix(rgamma(100, 2), 10, dimnames = list(1:10))
top_markers(data, label = rep(c("A", "B"), 5))
#> # A tibble: 20 × 3
#> # Groups: .dot [2]
#> .dot Genes Scores
#> <chr> <chr> <dbl>
#> 1 A X4 0.200
#> 2 A X3 0.183
#> 3 A X7 0.163
#> 4 A X2 0.156
#> 5 A X1 0.0692
#> 6 A X10 0.0632
#> 7 A X8 0.0594
#> 8 A X9 0.0588
#> 9 A X6 0.0294
#> 10 A X5 0.0182
#> 11 B X5 0.312
#> 12 B X6 0.193
#> 13 B X9 0.0965
#> 14 B X8 0.0955
#> 15 B X10 0.0899
#> 16 B X1 0.0821
#> 17 B X2 0.0364
#> 18 B X7 0.0348
#> 19 B X3 0.0311
#> 20 B X4 0.0284