runSplits: runSplits

In mcalgaro93/benchdamic: Benchmark of differential abundance methods on microbiome data

runSplits

Description

Run the differential abundance detection methods on split datasets.

Usage

runSplits(
  split_list,
  method_list,
  normalization_list,
  object,
  assay_name = "counts",
  min_counts = 0,
  min_samples = 0,
  verbose = TRUE,
  BPPARAM = BiocParallel::SerialParam()
)

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.
assay_name	the name of the assay to extract from the TreeSummarizedExperiment object (default assayName = "counts"). Not used if the input object is a phyloseq.
min_counts	Parameter to filter taxa. Set this number to keep features with more than min_counts counts in more than min_samples samples (default min_counts = 0).
min_samples	Parameter to filter taxa. Set this number to keep features with a min_counts counts in more than min_samples samples (default min_samples = 0).
verbose	an optional logical value. If TRUE, information about the steps of the algorithm is printed. Default verbose = TRUE.
BPPARAM	An optional BiocParallelParam instance defining the parallel back-end to be used during evaluation.

Value

A named list containing the results for each method.

Examples

data(ps_plaque_16S)

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

# Make sure the subject ID variable is a factor
phyloseq::sample_data(ps_plaque_16S)[, "RSID"] <- as.factor(
    phyloseq::sample_data(ps_plaque_16S)[["RSID"]])

# Initialize some limma based methods
my_limma <- set_limma(design = ~ RSID + HMP_BODY_SUBSITE, 
    coef = "HMP_BODY_SUBSITESupragingival Plaque",
    norm = c("TMM", "CSS"))

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

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

mcalgaro93/benchdamic documentation built on March 10, 2024, 10:40 p.m.