Normalize count data to relative counts per cell by dividing by the total
per cell. Optionally use a scale factor, e.g. for counts per million (CPM)
use scale.factor = 1e6.
RelativeCounts(data, scale.factor = 1, verbose = TRUE)Matrix with the raw count data
Scale the result. Default is 1
Print progress
Returns a matrix with the relative counts
mat <- matrix(data = rbinom(n = 25, size = 5, prob = 0.2), nrow = 5)
mat
#> [,1] [,2] [,3] [,4] [,5]
#> [1,] 0 2 0 1 2
#> [2,] 1 3 0 1 0
#> [3,] 1 1 1 3 1
#> [4,] 2 1 1 1 0
#> [5,] 0 0 2 2 0
mat_norm <- RelativeCounts(data = mat)
mat_norm
#> 5 x 5 sparse Matrix of class "dgCMatrix"
#>
#> [1,] . 0.2857143 . 0.125 0.6666667
#> [2,] 0.25 0.4285714 . 0.125 .
#> [3,] 0.25 0.1428571 0.25 0.375 0.3333333
#> [4,] 0.50 0.1428571 0.25 0.125 .
#> [5,] . . 0.50 0.250 .