run_aldex  R Documentation 
Perform differential analysis using ALDEx2
run_aldex(
ps,
group,
taxa_rank = "all",
transform = c("identity", "log10", "log10p"),
norm = "none",
norm_para = list(),
method = c("t.test", "wilcox.test", "kruskal", "glm_anova"),
p_adjust = c("none", "fdr", "bonferroni", "holm", "hochberg", "hommel", "BH", "BY"),
pvalue_cutoff = 0.05,
mc_samples = 128,
denom = c("all", "iqlr", "zero", "lvha"),
paired = FALSE
)
ps 
a 
group 
character, the variable to set the group 
taxa_rank 
character to specify taxonomic rank to perform
differential analysis on. Should be one of

transform 
character, the methods used to transform the microbial
abundance. See

norm 
the methods used to normalize the microbial abundance data. See

norm_para 
arguments passed to specific normalization methods 
method 
test method, options include: "t.test" and "wilcox.test" for two groups comparison, "kruskal" and "glm_anova" for multiple groups comparison. 
p_adjust 
method for multiple test correction, default 
pvalue_cutoff 
cutoff of p value, default 0.05. 
mc_samples 
integer, the number of Monte Carlo samples to use for underlying distributions estimation, 128 is usually sufficient. 
denom 
character string, specifiy which features used to as the denominator for the geometric mean calculation. Options are:

paired 
logical, whether to perform paired tests, only worked for method "t.test" and "wilcox.test". 
a microbiomeMarker
object.
Fernandes, A.D., Reid, J.N., Macklaim, J.M. et al. Unifying the analysis of highthroughput sequencing datasets: characterizing RNAseq, 16S rRNA gene sequencing and selective growth experiments by compositional data analysis. Microbiome 2, 15 (2014).
ALDEx2::aldex()
data(enterotypes_arumugam)
ps < phyloseq::subset_samples(
enterotypes_arumugam,
Enterotype %in% c("Enterotype 3", "Enterotype 2")
)
run_aldex(ps, group = "Enterotype")
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