runDA {benchdamic} | R Documentation

## runDA

### Description

Run the differential abundance detection methods.

### Usage

    runDA(method_list, object, weights = NULL, verbose = TRUE)

### Arguments

`method_list` | a list object containing the methods and their parameters.
`object` | a phyloseq object.
`weights` | an optional numeric matrix giving observational weights.
`verbose` | an optional logical value. If `TRUE`, information about the steps of the algorithm is printed. Default `verbose = TRUE`.

### Value

A named list containing the results for each method.

### 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))

    # Set some simple normalizations
    my_norm <- setNormalizations()

    # Add them to the phyloseq object
    ps <- runNormalizations(normalization_list = my_norm, object = ps)

    # Set some limma instances
    my_methods <- set_limma(design = ~ group, coef = 2,
        norm = c("TMM", "poscounts", "CSSmedian"))

    # Run the methods
    results <- runDA(method_list = my_methods, object = ps)

[Package benchdamic version 1.0.0 Index]