Normalize count data per cell and transform to log scale
LogNormalize(data, scale.factor = 10000, verbose = TRUE)
Matrix with the raw count data
Scale the data. Default is 1e4
Print progress
Returns a matrix with the normalize and log transformed data
mat <- matrix(data = rbinom(n = 25, size = 5, prob = 0.2), nrow = 5)
mat
#> [,1] [,2] [,3] [,4] [,5]
#> [1,] 0 0 1 1 1
#> [2,] 2 2 0 0 2
#> [3,] 0 1 2 2 1
#> [4,] 1 0 2 0 2
#> [5,] 1 2 0 1 1
mat_norm <- LogNormalize(data = mat)
mat_norm
#> 5 x 5 sparse Matrix of class "dgCMatrix"
#>
#> [1,] . . 7.601402 7.824446 7.265130
#> [2,] 8.517393 8.294300 . . 7.957927
#> [3,] . 7.601402 8.294300 8.517393 7.265130
#> [4,] 7.824446 . 8.294300 . 7.957927
#> [5,] 7.824446 8.294300 . 7.824446 7.265130