DA_ALDEx2  R Documentation 
Fast run for the ALDEx2's differential abundance detection method. Support for Welch's t, Wilcoxon, KruskalWallace, KruskalWallace glm ANOVAlike, and glm tests.
DA_ALDEx2( object, assay_name = "counts", pseudo_count = FALSE, design = NULL, mc.samples = 128, test = c("t", "wilcox", "kw", "kw_glm", "glm"), paired.test = FALSE, denom = "all", contrast = NULL, verbose = TRUE )
object 
a phyloseq or TreeSummarizedExperiment object. 
assay_name 
the name of the assay to extract from the
TreeSummarizedExperiment object (default 
pseudo_count 
add 1 to all counts if TRUE (default

design 
a character with the name of a variable to group samples and
compare them or a formula to compute a model.matrix (when

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 KruskalWallace test while "kw_glm" runs glm ANOVAlike test. "glm" runs a generalized linear model. 
paired.test 
A boolean. Toggles whether to do pairedsample tests.
Applies to 
denom 
An 
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. 
verbose 
an optional logical value. If 
A list object containing the matrix of pvalues 'pValMat', the matrix of summary statistics for each tag 'statInfo', and a suggested 'name' of the final object considering the parameters passed to the function.
aldex
for the DirichletMultinomial model
estimation. Several and more complex tests are present in the ALDEx2
framework.
set.seed(1) # Create a very simple phyloseq object counts < matrix(rnbinom(n = 300, size = 3, prob = 0.5), nrow = 50, ncol = 6) metadata < data.frame("Sample" = c("S1", "S2", "S3", "S4", "S5", "S6"), "group" = as.factor(c("A", "A", "A", "B", "B", "B"))) ps < phyloseq::phyloseq(phyloseq::otu_table(counts, taxa_are_rows = TRUE), phyloseq::sample_data(metadata)) # Differential abundance with t test and denom defined by the user DA_t < DA_ALDEx2(ps, design = "group", test = "t", denom = c(1,2,3), paired.test = FALSE, contrast = c("group", "B", "A")) # Differential abundance with wilcox test and denom = "iqlr" DA_w < DA_ALDEx2(ps, design = "group", test = "wilcox", denom = "iqlr", paired.test = FALSE, contrast = c("group", "B", "A")) # Differential abundance with kw test and denom = "zero" # mc.samples = 2 to speed up (128 suggested) DA_kw < DA_ALDEx2(ps, design = "group", test = "kw", denom = "zero", mc.samples = 2) # Differential abundance with kw_glm test and denom = "median" DA_kw_glm < DA_ALDEx2(ps, design = "group", test = "kw", denom = "median", mc.samples = 2) # Differential abundance with glm test and denom = "all" DA_glm < DA_ALDEx2(ps, design = ~ group, test = "glm", denom = "all", mc.samples = 2, contrast = c("group", "B", "A"))
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