run_ancom  R Documentation 
Perform significant test by comparing the pairwise log ratios between all features.
run_ancom(
ps,
group,
confounders = character(0),
taxa_rank = "all",
transform = c("identity", "log10", "log10p"),
norm = "TSS",
norm_para = list(),
p_adjust = c("none", "fdr", "bonferroni", "holm", "hochberg", "hommel", "BH", "BY"),
pvalue_cutoff = 0.05,
W_cutoff = 0.75
)
ps 
a 
group 
character, the variable to set the group. 
confounders 
character vector, the confounding variables to be adjusted.
default 
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 
named 
p_adjust 
method for multiple test correction, default 
pvalue_cutoff 
significance level for each of the statistical tests, default 0.05. 
W_cutoff 
lower bound for the proportion for the Wstatistic, default 0.7. 
In an experiment with only two treatments, this tests the following
hypothesis for feature i
:
H_{0i}: E(log(\mu_i^1)) = E(log(\mu_i^2))
where \mu_i^1
and \mu_i^2
are the mean abundances for feature
i
in the two groups.
The developers of this method recommend the following significance tests
if there are 2 groups, use nonparametric Wilcoxon rank sum test
stats::wilcox.test()
. If there are more than 2 groups, use nonparametric
stats::kruskal.test()
or oneway ANOVA stats::aov()
.
a microbiomeMarker object, in which the slot
of
marker_table
contains four variables:
feature
, significantly different features.
enrich_group
, the class of the differential features enriched.
effect_size
, differential means for two groups, or F statistic for more
than two groups.
W
, the Wstatistic, number of features that a single feature is tested
to be significantly different against.
Huang Lin, Yang Cao
Mandal et al. "Analysis of composition of microbiomes: a novel method for studying microbial composition", Microbial Ecology in Health & Disease, (2015), 26.
data(enterotypes_arumugam)
ps < phyloseq::subset_samples(
enterotypes_arumugam,
Enterotype %in% c("Enterotype 3", "Enterotype 2")
)
run_ancom(ps, group = "Enterotype")
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