runSplits {benchdamic} | R Documentation

## runSplits

### Description

Run the differential abundance detection methods on split datasets.

### Usage

    runSplits(split_list, method_list, normalization_list, object, verbose = TRUE)

### Arguments

`split_list` | A list of 2 `data.frame` objects: `Subset1` and `Subset2` produced by the `createSplits` function.
`method_list` | a list object containing the methods and their parameters.
`normalization_list` | a list object containing the normalization method names and their parameters produced by `setNormalizations`.
`object` | a phyloseq object.
`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

    data(ps_plaque_16S)

    # Balanced design for independent samples
    my_splits <- createSplits(
        object = ps_plaque_16S, varName =
        "HMP_BODY_SUBSITE", balanced = TRUE, N = 10 # N = 100 suggested
    )

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

    # Set the normalization methods according to the DA methods
    my_norm <- setNormalizations(fun = c("norm_edgeR", "norm_CSS"),
        method = c("TMM", "median"))

    # Run methods on split datasets
    results <- runSplits(split_list = my_splits, method_list = my_limma,
        normalization_list = my_norm, object = ps_plaque_16S)

[Package benchdamic version 1.0.0 Index]