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)

Arguments

data

Matrix with the raw count data

scale.factor

Scale the result. Default is 1

verbose

Print progress

Value

Returns a matrix with the relative counts

Examples

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 .