runMocks {benchdamic} | R Documentation

## runMocks

### Description

Run the differential abundance detection methods on mock datasets.

### Usage

    runMocks(mocks, method_list, object, weights = NULL, verbose = TRUE)

### Arguments

`mocks` | a `data.frame` containing `N` rows and `nsamples` columns (if even). Each cell of the data frame contains the "grp1" or "grp2" characters which represent the mock groups pattern. Produced by the `createMocks` function.
`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

    # Load some data
    data(ps_stool_16S)

    # Generate the pattern for 10 mock comparisons
    # (N = 1000 is suggested)
    mocks <- createMocks(nsamples = phyloseq::nsamples(ps_stool_16S), N = 10)
    head(mocks)

    # Add some normalization/scaling factors to the phyloseq object
    my_norm <- setNormalizations(fun = c("norm_edgeR", "norm_CSS"),
        method = c("TMM", "median"))
    ps_stool_16S <- runNormalizations(normalization_list = my_norm,
        object = ps_stool_16S)

    # Initialize some limma based methods
    my_limma <- set_limma(design = ~ group, coef = 2,
        norm = c("TMM", "CSSmedian"))

    # Run methods on mock datasets
    results <- runMocks(mocks = mocks, method_list = my_limma,
        object = ps_stool_16S)

[Package benchdamic version 1.0.0 Index]