rarefy: Rarefy OTU counts.

In cmmr/rbiom: Read/Write, Analyze, and Visualize 'BIOM' Data

Rarefy OTU counts.

Description

Sub-sample OTU observations such that all samples have an equal number. 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.

Usage

rarefy(biom, depth = 0.1, n = NULL, seed = 0, clone = TRUE)

Arguments

biom	An rbiom-class object, or data coercible with as_rbiom().
depth	How many observations to keep per sample. When ⁠0 < depth < 1⁠, it is taken as the minimum percentage of the dataset's observations to keep. Ignored when n is specified. Default: 0.1
n	The number of samples to keep. When ⁠0 < n < 1⁠, it is taken as the percentage of samples to keep. If negative, that number or percentage of samples is dropped. If 0, all samples are kept. If NULL, depth is used instead. Default: NULL
seed	An integer seed for randomizing which observations to keep or drop. If you need to create different random rarefactions of the same data, set the seed to a different number each time.
clone	Create a copy of biom before modifying. If FALSE, biom is modified in place as a side-effect. See speed ups for use cases. Default: TRUE

Value

An rbiom object.

See Also

Other rarefaction: rare_corrplot(), rare_multiplot(), rare_stacked(), rarefy_cols(), sample_sums()

Other transformations: mutate(), rarefy_cols(), slice(), subset()

Examples

    library(rbiom)
    
    sample_sums(hmp50) %>% head()
    
    biom <- rarefy(hmp50)
    sample_sums(biom) %>% head()

cmmr/rbiom documentation built on Feb. 16, 2024, 3:34 a.m.