runMocks: runMocks

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

runMocks

Description

Run the differential abundance detection methods on mock datasets.

Usage

runMocks(
  mocks,
  method_list,
  object,
  weights = NULL,
  verbose = TRUE,
  BPPARAM = BiocParallel::SerialParam()
)

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 or TreeSummarizedExperiment 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.
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

# 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", "CSS"))
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", "CSS"))

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

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