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corncob analysis

Source: R/corncob.R
step_corncob.Rd

Corncob an individual taxon regression model that uses abundance tables and sample data. corncob is able to model differential abundance and differential variability, and addresses each of the challenges presented below:

Usage

step_corncob(
  rec,
  phi.formula = stats::formula(~1),
  formula_null = stats::formula(~1),
  phi.formula_null = stats::formula(~1),
  link = "logit",
  phi.link = "logit",
  test = "Wald",
  boot = FALSE,
  B = 1000,
  filter_discriminant = TRUE,
  fdr_cutoff = 0.05,
  fdr = "fdr",
  log2FC = 0,
  rarefy = FALSE,
  id = rand_id("corncob")
)

# S4 method for class 'Recipe'
step_corncob(
  rec,
  phi.formula = stats::formula(~1),
  formula_null = stats::formula(~1),
  phi.formula_null = stats::formula(~1),
  link = "logit",
  phi.link = "logit",
  test = "Wald",
  boot = FALSE,
  B = 1000,
  filter_discriminant = TRUE,
  fdr_cutoff = 0.05,
  fdr = "fdr",
  log2FC = 0,
  rarefy = FALSE,
  id = rand_id("corncob")
)

# S4 method for class 'PrepRecipe'
step_corncob(
  rec,
  phi.formula = stats::formula(~1),
  formula_null = stats::formula(~1),
  phi.formula_null = stats::formula(~1),
  link = "logit",
  phi.link = "logit",
  test = "Wald",
  boot = FALSE,
  B = 1000,
  filter_discriminant = TRUE,
  fdr_cutoff = 0.05,
  fdr = "fdr",
  log2FC = 0,
  rarefy = FALSE,
  id = rand_id("corncob")
)

Arguments

rec

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

phi.formula

An object of class formula without the response: a symbolic description of the model to be fitted to the dispersion.

formula_null

Formula for mean under null, without response.

phi.formula_null

Formula for overdispersion under null, without response.

Link function for abundance covariates, defaults to "logit".

Link function for dispersion covariates, defaults to "logit".

test

Character. Hypothesis testing procedure to use. One of "Wald" or "LRT" (likelihood ratio test).

boot

Boolean. Defaults to FALSE. Indicator of whether or not to use parametric bootstrap algorithm. (See pbWald and pbLRT).

B

Optional integer. Number of bootstrap iterations. Ignored if boot is FALSE. Otherwise, defaults to 1000.

filter_discriminant

Boolean. Defaults to TRUE. If FALSE, discriminant taxa will not be filtered out.

fdr_cutoff

Integer. Defaults to 0.05. Desired type 1 error rate.

fdr

Character. Defaults to "fdr". False discovery rate control method, see p.adjust for more options.

log2FC

log2FC cutoff.

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

Details

  • different sequencing depth

  • excessive zeros from unobserved taxa

  • high variability of empirical relative abundances (overdispersion)

  • within-taxon correlation

  • hypothesis testing with categorical and continuous covariates

See also

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

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) >= (0.3 * 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__Coca 
#>      ◉ step_filter_taxa() id = filter_taxa__Profiterole 
#> 
#> DA steps:
#> 

## Define step with default parameters and prep
rec <- 
  step_corncob(rec) |>
  prep(parallel = FALSE)
  
rec
#> ── DAR Results ─────────────────────────────────────────────────────────────────
#> Inputs:
#> 
#>      ℹ phyloseq object with 101 taxa and 156 samples 
#>      ℹ variable of interes RiskGroup2 (class: character, levels: hts, msm, pwid) 
#>      ℹ taxonomic level Phylum 
#> 
#> Results:
#> 
#>      ✔ corncob__Briouat diff_taxa = 4 
#> 
#>      ℹ 4 taxa are present in all tested methods 
#>