DA_MAST {benchdamic} | R Documentation

## DA_MAST

### Description

Fast run for MAST differential abundance detection method.

### Usage

    DA_MAST(
      object,
      pseudo_count = FALSE,
      rescale = c("median", "default"),
      design,
      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`).
`rescale` | Rescale count data, per million if 'default', or per median library size if 'median' ('median' is suggested for metagenomics data).
`design` | The model for the count distribution. Can be the variable name, or a character similar to "~ 1 + group", or a formula, or a 'model.matrix' object.
`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

`zlm` for the Truncated Gaussian Hurdle model estimation.

### 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_MAST(object = ps_NF, pseudo_count = FALSE, rescale = "median",
        design = ~ group, norm = "none", coefficient = "groupB")

[Package benchdamic version 1.0.0 Index]