set_metagenomeSeq: set_metagenomeSeq

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

set_metagenomeSeq

Description

Set the parameters for metagenomeSeq differential abundance detection method.

Usage

set_metagenomeSeq(
  assay_name = "counts",
  pseudo_count = FALSE,
  design = NULL,
  coef = 2,
  norm = "CSS",
  model = "fitFeatureModel",
  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	the model for the count distribution. Can be the variable name, or a character similar to "~ 1 + group", or a formula.
coef	coefficient of interest to grab log fold-changes.
norm	name of the normalization method to use in the differential abundance analysis. Choose the native metagenomeSeq normalization method CSS. Alternatively (only for advanced users), if norm is equal to "TMM", "TMMwsp", "RLE", "upperquartile", "posupperquartile", or "none" from norm_edgeR, "ratio", "poscounts", or "iterate" from norm_DESeq2, or "TSS" from norm_TSS, the factors are automatically transformed into scaling factors. If custom factors are supplied, make sure they are compatible with metagenomeSeq normalization factors.
model	character equal to "fitFeatureModel" for differential abundance analysis using a zero-inflated log-normal model, "fitZig" for a complex mathematical optimization routine to estimate probabilities that a zero for a particular feature in a sample is a technical zero or not. The latter model relies heavily on the limma package (default model = "fitFeatureModel").
expand	logical, if TRUE create all combinations of input parameters (default expand = TRUE)

Value

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

See Also

DA_metagenomeSeq

Examples

# Set a basic combination of parameters for metagenomeSeq
base_mgs <- set_metagenomeSeq(design = ~ group, coef = 2)
# Set a specific model for metagenomeSeq
setModel_mgs <- set_metagenomeSeq(design = ~ group, coef = 2, 
    model = "fitZig")
# Set many possible combinations of parameters for metagenomeSeq
all_mgs <- set_metagenomeSeq(pseudo_count = c(TRUE, FALSE), design = ~ group,
    coef = 2, model = c("fitFeatureModel", "fitZig"), norm = "CSS")

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