contrastsTest: Identify differentially-abundant and differentially-variable...

In corncob: Count Regression for Correlated Observations with the Beta-Binomial

Identify differentially-abundant and differentially-variable taxa using contrasts

Description

Identify differentially-abundant and differentially-variable taxa using contrasts

Usage

contrastsTest(
  formula,
  phi.formula,
  contrasts_DA = NULL,
  contrasts_DV = NULL,
  data,
  link = "logit",
  phi.link = "logit",
  sample_data = NULL,
  taxa_are_rows = TRUE,
  filter_discriminant = TRUE,
  fdr_cutoff = 0.05,
  fdr = "fdr",
  inits = NULL,
  try_only = NULL,
  ...
)

Arguments

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
contrasts_DA	List. Optional. Constructs a contrast matrix. List elements should be characters specifying contrasts in the parameters within formula. Note that this is only available with "Wald" value for test. Must include at least one of contrasts_DA or contrasts_DV.
contrasts_DV	List. Optional. Constructs a contrast matrix. List elements should be characters specifying contrasts in the parameters within phi.formula. Note that this is only available with "Wald" value for test. Must include at least one of contrasts_DA or contrasts_DV.
data	a data frame containing the OTU table, or phyloseq object containing the variables in the models
link	link function for abundance covariates, defaults to "logit"
phi.link	link function for dispersion covariates, defaults to "logit"
sample_data	Data frame or matrix. Defaults to NULL. If data is a data frame or matrix, this must be included as covariates/sample data.
taxa_are_rows	Boolean. Optional. If data is a data frame or matrix, this indicates whether taxa are rows. Defaults to TRUE.
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.
inits	Optional initializations for model fit using formula and phi.formula as rows of a matrix. Defaults to NULL.
try_only	Optional numeric. Will try only the try_only taxa, specified either via numeric input or character taxa names. Useful for speed when troubleshooting. Defaults to NULL, testing all taxa.
...	Optional additional arguments for bbdml

Details

This function uses contrast matrices to test for differential abundance and differential variability using a Wald-type chi-squared test. To use a formula implementation, see differentialTest.

Value

An object of class contrastsTest. List with elements p containing the p-values for each contrast, p_fdr containing the p-values after false discovery rate control, significant_taxa containing the taxa names of the statistically significant taxa, contrasts_DA containing the contrast matrix for parameters associated with the abundance, contrasts_DV containing the contrast matrix for parameters associated with the dispersion, discriminant_taxa_DA containing the taxa for which at least one covariate associated with the abundance was perfectly discriminant, discriminant_taxa_DV containing the taxa for which at least one covariate associated with the dispersion was perfectly discriminant, and data containing the data used to fit the models.

Examples

# phyloseq example
data(soil_phylum_small)
da_analysis <- contrastsTest(formula = ~ DayAmdmt,
                             phi.formula = ~ DayAmdmt,
                             contrasts_DA = list("DayAmdmt21 - DayAmdmt11",
                                                 "DayAmdmt22 - DayAmdmt21"),
                             data = soil_phylum_small,
                             fdr_cutoff = 0.05)

corncob documentation built on Aug. 31, 2023, 9:06 a.m.