Normalize Raw Data

LogNormalize(data, scale.factor = 10000, margin = 2L, verbose = TRUE, ...)

# S3 method for class 'data.frame'
LogNormalize(data, scale.factor = 10000, margin = 2L, verbose = TRUE, ...)

# S3 method for class 'V3Matrix'
LogNormalize(data, scale.factor = 10000, margin = 2L, verbose = TRUE, ...)

# Default S3 method
LogNormalize(data, scale.factor = 10000, margin = 2L, verbose = TRUE, ...)

Arguments

data: Matrix with the raw count data
scale.factor: Scale the data; default is 1e4
margin: Margin to normalize over
verbose: Print progress
...: Arguments passed to other methods

Value

A matrix with the normalized and log-transformed data

Examples

mat <- matrix(data = rbinom(n = 25, size = 5, prob = 0.2), nrow = 5)
mat
#>      [,1] [,2] [,3] [,4] [,5]
#> [1,]    1    0    0    1    1
#> [2,]    0    3    1    0    2
#> [3,]    1    2    3    2    2
#> [4,]    2    1    0    0    1
#> [5,]    0    1    1    2    1
mat_norm <- LogNormalize(data = mat)
mat_norm
#>          [,1]     [,2]     [,3]     [,4]     [,5]
#> [1,] 7.824446 0.000000 0.000000 7.601402 7.265130
#> [2,] 0.000000 8.363276 7.601402 0.000000 7.957927
#> [3,] 7.824446 7.957927 8.699681 8.294300 7.957927
#> [4,] 8.517393 7.265130 0.000000 0.000000 7.265130
#> [5,] 0.000000 7.265130 7.601402 8.294300 7.265130