set_limma: set_limma

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

set_limma

Description

Set the parameters for limma differential abundance detection method.

Usage

set_limma(
  assay_name = "counts",
  pseudo_count = FALSE,
  design = NULL,
  coef = 2,
  norm = c("TMM", "TMMwsp", "RLE", "upperquartile", "posupperquartile", "none"),
  weights_logical = FALSE,
  expand = TRUE
)

Arguments

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.
pseudo_count	add 1 to all counts if TRUE (default pseudo_count = FALSE).
design	character name of the metadata columns, formula, or design matrix with rows corresponding to samples and columns to coefficients to be estimated.
coef	integer or character index vector indicating which coefficients of the linear model are to be tested equal to zero.
norm	name of the normalization method to use in the differential abundance analysis. Choose between the native edgeR normalization methods, such as TMM, TMMwsp, RLE, upperquartile, posupperquartile, or none. Alternatively (only for advanced users), if norm is equal to "ratio", "poscounts", or "iterate" from norm_DESeq2, "CSS" from norm_CSS, or "TSS" from norm_TSS, the scaling factors are automatically transformed into normalization factors. If custom factors are supplied, make sure they are compatible with edgeR normalization factors.
weights_logical	logical vector, if TRUE a matrix of observational weights will be used for differential abundance analysis (default weights_logical = FALSE).
expand	logical, if TRUE create all combinations of input parameters (default expand = TRUE).

Value

A named list containing the set of parameters for DA_limma method.

See Also

DA_limma

Examples

# Set some basic combinations of parameters for limma
base_limma <- set_limma(design = ~ group, coef = 2)
# Set a specific set of normalization for limma (even of other packages!)
setNorm_limma <- set_limma(design = ~ group, coef = 2,
    norm = c("TMM", "upperquartile"))
# Set many possible combinations of parameters for limma
all_limma <- set_limma(pseudo_count = c(TRUE, FALSE), design = ~ group,
    coef = 2, weights_logical = c(TRUE, FALSE))

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