DA_corncob {benchdamic}

R Documentation

DA_corncob

Description

Fast run for corncob differential abundance detection method.

Usage

DA_corncob(
  object,
  pseudo_count = FALSE,
  formula,
  phi.formula,
  formula_null,
  phi.formula_null,
  test,
  boot = FALSE,
  coefficient = NULL,
  norm = c("TMM", "TMMwsp", "RLE", "upperquartile", "posupperquartile", "none",
    "ratio", "poscounts", "iterate", "TSS", "CSSmedian", "CSSdefault"),
  verbose = TRUE
)

Arguments

object	phyloseq object.
pseudo_count	add 1 to all counts if TRUE (default pseudo_count = FALSE).
formula	an object of class formula without the response: a symbolic description of the model to be fitted to the abundance.
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
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).
coefficient	The coefficient of interest as a single word formed by the variable name and the non reference level. (e.g.: 'ConditionDisease' if the reference level for the variable 'Condition' is 'control').
norm	name of the normalization method used to compute the normalization factors to use in the differential abundance analysis. If norm is equal to "TMM", "TMMwsp", "RLE", "upperquartile", "posupperquartile", "CSSmedian", "CSSdefault", "TSS" the scaling factors are automatically transformed into normalization factors.
verbose	an optional logical value. If TRUE, information about the steps of the algorithm is printed. Default verbose = TRUE.

Value

A list object containing the matrix of p-values 'pValMat', the matrix of summary statistics for each tag 'statInfo', and a suggested 'name' of the final object considering the parameters passed to the function.

See Also

bbdml and differentialTest for differential abundance and differential variance evaluation.

Examples

set.seed(1)
# Create a very simple phyloseq object
counts <- matrix(rnbinom(n = 60, size = 3, prob = 0.5), nrow = 10, ncol = 6)
metadata <- data.frame("Sample" = c("S1", "S2", "S3", "S4", "S5", "S6"),
                       "group" = as.factor(c("A", "A", "A", "B", "B", "B")))
ps <- phyloseq::phyloseq(phyloseq::otu_table(counts, taxa_are_rows = TRUE),
                         phyloseq::sample_data(metadata))
# No use of scaling factors
ps_NF <- norm_edgeR(object = ps, method = "none")
# The phyloseq object now contains the scaling factors:
scaleFacts <- phyloseq::sample_data(ps_NF)[, "NF.none"]
head(scaleFacts)
# Differential abundance
DA_corncob(object = ps_NF, formula = ~ group, phi.formula = ~ group,
    formula_null = ~ 1, phi.formula_null = ~ group, coefficient = "groupB",
    norm = "none", test = "Wald")

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