rarefy: Subset counts so that all samples have the same number of...
In rbiom: Read/Write, Transform, and Summarize 'BIOM' Data

Description Usage Arguments Value Examples

Subset counts so that all samples have the same number of observations.

1	rarefy(biom, depth = NULL, seed = 0)

`biom`	A `matrix`, `simple_triplet_matrix`, or `BIOM` object, as returned from read.biom. For matrices, the rows and columns are assumed to be the taxa and samples, respectively.
`depth`	The number of observations to keep, per sample. If set to `NULL`, a depth will be automatically selected. Samples that have fewer than this number of observations will be dropped. If called on data with non-integer abundances, values will be re-scaled to integers between 1 and `depth` such that they sum to `depth`.
`seed`	An integer to use for seeding the random number generator. If you need to create different random rarefactions of the same `BIOM` object, set this seed value to a different number each time.

A matrix, simple_triplet_matrix, or BIOM object, depending on the input object type. The type of object provided is the same type that is returned. The retained observations are randomly selected, based on a seed value derived from the BIOM object. Therefore, rarefying the same biom to the same depth will always produce the same resultant rarification.

    library(rbiom)

    infile <- system.file("extdata", "hmp50.bz2", package = "rbiom")
    biom <- read.biom(infile)
    range(slam::col_sums(biom$counts))

    biom <- rarefy(biom, depth=1000)
    range(slam::col_sums(biom$counts))