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")

[Package benchdamic version 1.0.0 Index]