run_test_multiple_groups  R Documentation 
Statistical test for multiple groups
Description
Statistical test for multiple groups
Usage
run_test_multiple_groups(
ps,
group,
taxa_rank = "all",
transform = c("identity", "log10", "log10p"),
norm = "TSS",
norm_para = list(),
method = c("anova", "kruskal"),
p_adjust = c("none", "fdr", "bonferroni", "holm", "hochberg", "hommel", "BH", "BY"),
pvalue_cutoff = 0.05,
effect_size_cutoff = NULL
)
Arguments
ps 
a phyloseq::phyloseq object

group 
character, the variable to set the group

taxa_rank 
character to specify taxonomic rank to perform
differential analysis on. Should be one of
phyloseq::rank_names(phyloseq) , or "all" means to summarize the taxa by
the top taxa ranks (summarize_taxa(ps, level = rank_names(ps)[1]) ), or
"none" means perform differential analysis on the original taxa
(taxa_names(phyloseq) , e.g., OTU or ASV).

transform 
character, the methods used to transform the microbial
abundance. See transform_abundances() for more details. The
options include:
"identity", return the original data without any transformation
(default).
"log10", the transformation is log10(object) , and if the data contains
zeros the transformation is log10(1 + object) .
"log10p", the transformation is log10(1 + object) .

norm 
the methods used to normalize the microbial abundance data. See
normalize() for more details.
Options include:
"none": do not normalize.
"rarefy": random subsampling counts to the smallest library size in the
data set.
"TSS": total sum scaling, also referred to as "relative abundance", the
abundances were normalized by dividing the corresponding sample library
size.
"TMM": trimmed mean of mvalues. First, a sample is chosen as reference.
The scaling factor is then derived using a weighted trimmed mean over the
differences of the logtransformed genecount foldchange between the
sample and the reference.
"RLE", relative log expression, RLE uses a pseudoreference calculated
using the geometric mean of the genespecific abundances over all
samples. The scaling factors are then calculated as the median of the
gene counts ratios between the samples and the reference.
"CSS": cumulative sum scaling, calculates scaling factors as the
cumulative sum of gene abundances up to a dataderived threshold.
"CLR": centered logratio normalization.
"CPM": presample normalization of the sum of the values to 1e+06.

norm_para 
arguments passed to specific normalization methods

method 
test method, must be one of "anova" or "kruskal"

p_adjust 
method for multiple test correction, default none ,
for more details see stats::p.adjust.

pvalue_cutoff 
numeric, p value cutoff, default 0.05.

effect_size_cutoff 
numeric, cutoff of effect size default NULL
which means no effect size filter. The eta squared is used to measure the
effect size for anova/kruskal test.

Value
a microbiomeMarker
object.
See Also
run_posthoc_test()
,run_test_two_groups()
,run_simple_stat()
Examples
data(enterotypes_arumugam)
ps < phyloseq::subset_samples(
enterotypes_arumugam,
Enterotype %in% c("Enterotype 3", "Enterotype 2", "Enterotype 1")
)
mm_anova < run_test_multiple_groups(
ps,
group = "Enterotype",
method = "anova"
)