calculate group median, MAD or mean score and order genes based on scores

top_markers_abs(
  data,
  label,
  n = 10,
  pooled.sd = FALSE,
  method = c("median", "mad", "mean"),
  scale = TRUE,
  use.mgm = TRUE,
  softmax = TRUE,
  tau = 1
)

Arguments

data: matrix, features in row and samples in column
label: a vector of group label
n: integer, number of returned top genes for each group
pooled.sd: logical, if to use pooled SD for scaling
method: character, specify metric to compute, can be one of "median", "mad", "mean"
scale: logical, if to scale data by row
use.mgm: logical, if to scale data using scale_mgm()
softmax: logical, if to apply softmax transformation on output
tau: numeric, hyper parameter for softmax

Value

a tibble with feature names, group labels and ordered processed scores

Examples

data <- matrix(rgamma(100, 2), 10, dimnames = list(1:10))
top_markers_abs(data, label = rep(c("A", "B"), 5))
#> # A tibble: 20 × 3
#> # Groups:   .dot [2]
#>    .dot  Genes Scores
#>    <chr> <chr>  <dbl>
#>  1 A     8     0.282 
#>  2 A     4     0.101 
#>  3 A     3     0.0979
#>  4 A     7     0.0835
#>  5 A     5     0.0795
#>  6 A     10    0.0778
#>  7 A     2     0.0745
#>  8 A     1     0.0729
#>  9 A     9     0.0664
#> 10 A     6     0.0643
#> 11 B     1     0.151 
#> 12 B     9     0.137 
#> 13 B     4     0.123 
#> 14 B     3     0.119 
#> 15 B     6     0.0977
#> 16 B     5     0.0911
#> 17 B     10    0.0862
#> 18 B     7     0.0848
#> 19 B     2     0.0647
#> 20 B     8     0.0462