set_ALDEx2: set_ALDEx2

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

set_ALDEx2

Description

Set the parameters for ALDEx2 differential abundance detection method.

Usage

set_ALDEx2(
  assay_name = "counts",
  pseudo_count = FALSE,
  design = NULL,
  mc.samples = 128,
  test = "t",
  paired.test = FALSE,
  denom = "all",
  contrast = NULL,
  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	a character with the name of a variable to group samples and compare them or a formula to compute a model.matrix (when test = "glm").
mc.samples	an integer. The number of Monte Carlo samples to use when estimating the underlying distributions. Since we are estimating central tendencies, 128 is usually sufficient.
test	a character string. Indicates which tests to perform. "t" runs Welch's t test while "wilcox" runs Wilcoxon test. "kw" runs Kruskal-Wallace test while "kw_glm" runs glm ANOVA-like test. "glm" runs a generalized linear model.
paired.test	A boolean. Toggles whether to do paired-sample tests. Applies to effect = TRUE and test = "t".
denom	An any variable (all, iqlr, zero, lvha, median, user) indicating features to use as the denominator for the Geometric Mean calculation The default "all" uses the geometric mean abundance of all features. Using "median" returns the median abundance of all features. Using "iqlr" uses the features that are between the first and third quartile of the variance of the clr values across all samples. Using "zero" uses the non-zero features in each grop as the denominator. This approach is an extreme case where there are many nonzero features in one condition but many zeros in another. Using "lvha" uses features that have low variance (bottom quartile) and high relative abundance (top quartile in every sample). It is also possible to supply a vector of row indices to use as the denominator. Here, the experimentalist is determining a-priori which rows are thought to be invariant. In the case of RNA-seq, this could include ribosomal protein genes and and other house-keeping genes. This should be used with caution because the offsets may be different in the original data and in the data used by the function because features that are 0 in all samples are removed by aldex.clr.
contrast	character vector with exactly three elements: the name of a variable used in "design", the name of the level of interest, and the name of the reference level. If "kw" or "kw_glm" as test, contrast vector is not used.
expand	logical, if TRUE create all combinations of input parameters (default expand = TRUE)

Value

A named list containing the set of parameters for DA_ALDEx2 method.

See Also

DA_ALDEx2

Examples

# Set some basic combinations of parameters for ALDEx2
base_ALDEx2 <- set_ALDEx2(design = "group", 
    contrast = c("group", "grp2", "grp1"))
# Set a specific set of normalization for ALDEx2 (even of other
# packages!)
setNorm_ALDEx2 <- set_ALDEx2(design = "group", 
    contrast = c("group", "grp2", "grp1"))
# Set many possible combinations of parameters for ALDEx2
all_ALDEx2 <- set_ALDEx2(design = "group", denom = c("iqlr", "zero"),
    test = c("t", "wilcox"), contrast = c("group", "grp2", "grp1"))

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