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Wilcox analysis

Source: R/wilcox.R
step_wilcox.Rd

Performs a wilcox test to determine features (be it Operational Taxanomic Unit (OTU), species, etc.) that are differentially abundant between two or more groups of multiple samples.

Usage

step_wilcox(
  rec,
  norm_method = "compositional",
  max_significance = 0.05,
  p_adj_method = "BH",
  rarefy = FALSE,
  id = rand_id("wilcox")
)

# S4 method for class 'Recipe'
step_wilcox(
  rec,
  norm_method = "compositional",
  max_significance = 0.05,
  p_adj_method = "BH",
  rarefy = FALSE,
  id = rand_id("wilcox")
)

# S4 method for class 'PrepRecipe'
step_wilcox(
  rec,
  norm_method = "compositional",
  max_significance = 0.05,
  p_adj_method = "BH",
  rarefy = FALSE,
  id = rand_id("wilcox")
)

Arguments

rec

A Recipe object. The step will be added to the sequence of operations for this Recipe.

norm_method

Transformation to apply. The options include: 'compositional' (ie relative abundance), 'Z', 'log10', 'log10p', 'hellinger', 'identity', 'clr', 'alr', or any method from the vegan::decostand function.

max_significance

The q-value threshold for significance.

p_adj_method

Character. Specifying the method to adjust p-values for multiple comparisons. Default is “BH” (Benjamini-Hochberg procedure).

rarefy

Boolean indicating if OTU counts must be rarefyed. This rarefaction uses the standard R sample function to resample from the abundance values in the otu_table component of the first argument, physeq. Often one of the major goals of this procedure is to achieve parity in total number of counts between samples, as an alternative to other formal normalization procedures, which is why a single value for the sample.size is expected. If 'no_seed', rarefaction is performed without a set seed.

id

A character string that is unique to this step to identify it.

Value

An object of class Recipe

See also

Other Diff taxa steps: step_aldex(), step_ancom(), step_corncob(), step_deseq(), step_lefse(), step_maaslin(), step_metagenomeseq()

Examples

data(metaHIV_phy)

## Init Recipe
rec <-
  recipe(metaHIV_phy, "RiskGroup2", "Phylum") |>
  step_subset_taxa(tax_level = "Kingdom", taxa = c("Bacteria", "Archaea")) |>
  step_filter_taxa(.f = "function(x) sum(x > 0) >= (0.4 * length(x))")

rec
#> ── DAR Recipe ──────────────────────────────────────────────────────────────────
#> Inputs:
#> 
#>      ℹ phyloseq object with 451 taxa and 156 samples 
#>      ℹ variable of interes RiskGroup2 (class: character, levels: hts, msm, pwid) 
#>      ℹ taxonomic level Phylum 
#> 
#> Preporcessing steps:
#> 
#>      ◉ step_subset_taxa() id = subset_taxa__Linzer_torte 
#>      ◉ step_filter_taxa() id = filter_taxa__Malsouka 
#> 
#> DA steps:
#> 

## Define step with default parameters and prep
rec <-
  step_wilcox(rec) |>
  prep(parallel = FALSE)

rec
#> ── DAR Results ─────────────────────────────────────────────────────────────────
#> Inputs:
#> 
#>      ℹ phyloseq object with 76 taxa and 156 samples 
#>      ℹ variable of interes RiskGroup2 (class: character, levels: hts, msm, pwid) 
#>      ℹ taxonomic level Phylum 
#> 
#> Results:
#> 
#>      ✔ wilcox__Dutch_Baby_Pancake diff_taxa = 5 
#> 
#>      ℹ 5 taxa are present in all tested methods 
#> 

## Wearing rarefaction only for this step
rec <-
  recipe(metaHIV_phy, "RiskGroup2", "Species") |>
  step_wilcox(rarefy = TRUE)
#> ! Run wilcox without rarefaction is not recommended (id = wilcox__Masan)

rec
#> ── DAR Recipe ──────────────────────────────────────────────────────────────────
#> Inputs:
#> 
#>      ℹ phyloseq object with 451 taxa and 156 samples 
#>      ℹ variable of interes RiskGroup2 (class: character, levels: hts, msm, pwid) 
#>      ℹ taxonomic level Species 
#> 
#> Preporcessing steps:
#> 
#> 
#> DA steps:
#> 
#>      ◉ step_wilcox() id = wilcox__Masan