set_edgeR: set_edgeR

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

set_edgeR

Description

Set the parameters for edgeR differential abundance detection method.

Usage

set_edgeR(
  assay_name = "counts",
  pseudo_count = FALSE,
  group_name = NULL,
  design = NULL,
  robust = FALSE,
  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).
group_name	character giving the name of the column containing information about experimental group/condition for each sample/library.
design	character or formula to specify the model matrix.
robust	logical, should the estimation of prior.df be robustified against outliers?
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 observation weights must be supplied (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_edgeR method.

See Also

DA_edgeR

Examples

# Set some basic combinations of parameters for edgeR
base_edgeR <- set_edgeR(group_name = "group", design = ~ group, coef = 2)

# Set a specific set of normalization for edgeR
setNorm_edgeR <- set_edgeR(group_name = "group", design = ~ group, coef = 2,
    norm = c("TMM", "RLE"))

# Set many possible combinations of parameters for edgeR
all_edgeR <- set_edgeR(pseudo_count = c(TRUE, FALSE), group_name = "group",
    design = ~ group, robust = c(TRUE, FALSE), coef = 2,
    weights_logical = c(TRUE, FALSE))

mcalgaro93/benchdamic documentation built on Nov. 23, 2024, 5:06 p.m.