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Calculate the alpha diversity of each sample.

Source: R/adiv_table.r
adiv_table.Rd

Calculate the alpha diversity of each sample.

Usage

adiv_table(
  biom,
  adiv = "shannon",
  md = ".all",
  tree = NULL,
  transform = "none",
  ties = "random",
  seed = 0,
  cpus = NULL
)

adiv_matrix(
  biom,
  adiv = c("observed", "shannon", "simpson"),
  tree = NULL,
  transform = "none",
  ties = "random",
  seed = 0,
  cpus = NULL
)

adiv_vector(
  biom,
  adiv = "shannon",
  tree = NULL,
  transform = "none",
  ties = "random",
  seed = 0,
  cpus = NULL
)

Arguments

biom

An rbiom object, or any value accepted by as_rbiom().

adiv

Alpha diversity metric(s) to use. Options are: c("ace", "berger", "brillouin", "chao1", "faith", "fisher", "simpson", "inv_simpson", "margalef", "mcintosh", "menhinick", "observed", "shannon", "squares"). For "faith", a phylogenetic tree must be present in biom or explicitly provided via tree=. Set adiv=".all" to use all metrics. Multiple/abbreviated values allowed. Default: "shannon"

md

Dataset field(s) to include in the output data frame, or '.all' to include all metadata fields. Default: '.all'

tree

A phylo object representing the phylogenetic relationships of the taxa in biom. Only required when computing UniFrac distances. Default: biom$tree

transform

Transformation to apply. Options are: c("none", "rank", "log", "log1p", "sqrt", "percent"). "rank" is useful for correcting for non-normally distributions before applying regression statistics. Default: "none"

ties

When transform="rank", how to rank identical values. Options are: c("average", "first", "last", "random", "max", "min"). See rank() for details. Default: "random"

seed

Random seed for permutations. Must be a non-negative integer. Default: 0

cpus

The number of CPUs to use. Set to NULL to use all available, or to 1 to disable parallel processing. Default: NULL

Value

adiv_vector() -

A named numeric vector.

adiv_matrix() -

A matrix of samples x metric. The first column, 'depth', is never transformed.

adiv_table() -

A tibble data.frame of alpha diversity values. Each combination of sample/adiv has its own row. Column names are .sample, .depth, .adiv, and .diversity, followed by any metadata fields requested by md.

See also

sample_sums() for sample depths.

Other alpha_diversity: adiv_boxplot(), adiv_corrplot(), adiv_stats()

Examples

    library(rbiom)
    
    biom <- hmp50[1:5]
    
    adiv_table(biom)
#> # A tibble: 5 × 8
#>   .sample .depth .adiv   .diversity   Age   BMI `Body Site`   Sex   
#>   <chr>    <dbl> <fct>        <dbl> <dbl> <dbl> <fct>         <fct> 
#> 1 HMP01     1660 shannon       1.74    22    20 Buccal mucosa Female
#> 2 HMP02     1371 shannon       2.59    24    23 Buccal mucosa Male  
#> 3 HMP03     1353 shannon       2.95    28    26 Saliva        Male  
#> 4 HMP04     1895 shannon       3.26    25    23 Saliva        Male  
#> 5 HMP05     3939 shannon       1.46    27    24 Buccal mucosa Female
    
    biom <- rarefy(biom)
    adiv_table(biom, md = NULL)
#> # A tibble: 5 × 4
#>   .sample .depth .adiv   .diversity
#>   <fct>    <dbl> <fct>        <dbl>
#> 1 HMP01     1353 shannon       1.75
#> 2 HMP02     1353 shannon       2.59
#> 3 HMP03     1353 shannon       2.95
#> 4 HMP04     1353 shannon       3.24
#> 5 HMP05     1353 shannon       1.43
    
    adiv_vector(biom, 'faith')
    
    adiv_matrix(biom)
#>       depth observed  shannon   simpson
#> HMP01  1353       49 1.745200 0.5724740
#> HMP02  1353       75 2.587377 0.8125427
#> HMP03  1353       75 2.950982 0.8936622
#> HMP04  1353       73 3.235844 0.9315730
#> HMP05  1353       44 1.430414 0.5248395
#> attr(,"cmd")
#> [1] "adiv_matrix(biom, c(\"observed\", \"shannon\", \"simpson\"))"