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]