| run_test_multiple_groups | R Documentation |
Statistical test for multiple groups
Description
Differential expression analysis for multiple groups.
Usage
run_test_multiple_groups(
ps,
group,
taxa_rank = "all",
transform = c("identity", "log10", "log10p", "SquareRoot", "CubicRoot", "logit"),
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).
"SquareRoot", the transformation is Square Root.
"CubicRoot", the transformation is Cubic Root.
"logit", the transformation is Zero-inflated Logit Transformation
(Does not work well for microbiome data).
|
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 m-values. First, a sample is chosen as reference.
The scaling factor is then derived using a weighted trimmed mean over the
differences of the log-transformed gene-count fold-change between the
sample and the reference.
"RLE", relative log expression, RLE uses a pseudo-reference calculated
using the geometric mean of the gene-specific 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 data-derived threshold.
"CLR": centered log-ratio normalization.
"CPM": pre-sample 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"
)
