Deprecated matrix transformations — matrix

A collection of transformations that operate directly on matrices.

Usage

mtx_rarefy(
  mtx,
  margin = 2L,
  depth = 0.1,
  n = NULL,
  seed = 0L,
  upsample = NULL,
  cpus = NULL
)

mtx_percent(mtx, margin = 2L)

mtx_rescale(mtx, margin = 2L, range = c(0, 1))

rarefy_cols(mtx, depth = 0.1, n = NULL, seed = 0L, cpus = NULL)

rescale_rows(mtx)

rescale_cols(mtx)

Arguments

mtx: A numeric matrix or sparse matrix of counts.
margin: Apply the transformation to the matrix's rows (margin=1L) or columns (margin=2L). Instead of 1L and 2L, you may also use 'rows' and 'cols'. Default: 2L (column-wise, aka sample-wise for otu tables)
depth: How many observations to keep per sample.
n: Deprecated. The number of samples to keep. This argument is ignored in the current version.
seed: An integer seed for randomizing which observations to keep or drop.
upsample: If the count data is in percentages, provide an integer value here to scale each sample's observations to integers that sum to this value. Maps to inflate in the new syntax.
cpus: The number of CPUs to use. Set to NULL to use all available, or to 1 to disable parallel processing. Default: NULL
range: When rescaling, what should the minimum and maximum values be? Default: c(0, 1)

Value

A A numeric matrix or sparse matrix, depending on the input type, with the same dimensions as mtx.

Examples

    mtx <- matrix(1:12, ncol = 3, dimnames = list(paste0("OTU", 1:4), paste0("Sample", 1:3)))
    
    mtx
#>      Sample1 Sample2 Sample3
#> OTU1       1       5       9
#> OTU2       2       6      10
#> OTU3       3       7      11
#> OTU4       4       8      12
    
    suppressWarnings({
    
      mtx_rarefy(mtx)
      
      rarefy_cols(mtx)
      
      mtx_percent(mtx)
      
      mtx_rescale(mtx)
      
      rescale_rows(mtx)
      
      rescale_cols(mtx)
      
    })
#>      Sample1   Sample2   Sample3
#> OTU1     0.1 0.1923077 0.2142857
#> OTU2     0.2 0.2307692 0.2380952
#> OTU3     0.3 0.2692308 0.2619048
#> OTU4     0.4 0.3076923 0.2857143