View source: R/DAmetagenomeSeq.R
run_metagenomeseq  R Documentation 
Differential expression analysis based on the Zeroinflated LogNormal mixture model or Zeroinflated Gaussian mixture model using metagenomeSeq.
run_metagenomeseq(
ps,
group,
confounders = character(0),
contrast = NULL,
taxa_rank = "all",
transform = c("identity", "log10", "log10p"),
norm = "CSS",
norm_para = list(),
method = c("ZILN", "ZIG"),
p_adjust = c("none", "fdr", "bonferroni", "holm", "hochberg", "hommel", "BH", "BY"),
pvalue_cutoff = 0.05,
...
)
ps 
ps a 
group 
character, the variable to set the group, must be one of the var of the sample metadata. 
confounders 
character vector, the confounding variables to be adjusted.
default 
contrast 
this parameter only used for two groups comparison while there are multiple groups. For more please see the following details. 
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 
character, which model used for differential analysis, "ZILN" (Zeroinflated LogNormal mixture model)" or "ZIG" (Zeroinflated Gaussian mixture model). And the zeroinflated lognormal model is preferred due to the high sensitivity and low FDR. 
p_adjust 
method for multiple test correction, default 
pvalue_cutoff 
numeric, p value cutoff, default 0.05 
... 
extra arguments passed to the model. more details see

metagnomeSeq provides two differential analysis methods, zeroinflated
lognormal mixture model (implemented in
metagenomeSeq::fitFeatureModel()
) and zeroinflated Gaussian mixture
model (implemented in metagenomeSeq::fitZig()
). We recommend
fitFeatureModel over fitZig due to high sensitivity and low FDR. Both
metagenomeSeq::fitFeatureModel()
and metagenomeSeq::fitZig()
require
the abundance profiles before normalization.
For metagenomeSeq::fitZig()
, the output column is the coefficient of
interest, and logFC column in the output of
metagenomeSeq::fitFeatureModel()
is analogous to coefficient. Thus,
logFC is really just the estimate the coefficient of interest in
metagenomeSeq::fitFeatureModel()
. For more details see
these question Difference between fitFeatureModel and fitZIG in metagenomeSeq.
contrast
must be a two length character or NULL
(default). It is only
required to set manually for two groups comparison when there are multiple
groups. The order determines the direction of comparison, the first element
is used to specify the reference group (control). This means that, the first
element is the denominator for the fold change, and the second element is
used as baseline (numerator for fold change). Otherwise, users do required
to concern this paramerter (set as default NULL
), and if there are
two groups, the first level of groups will set as the reference group; if
there are multiple groups, it will perform an ANOVAlike testing to find
markers which difference in any of the groups.
Of note, metagenomeSeq::fitFeatureModel()
is not allows for multiple
groups comparison.
a microbiomeMarker
object.
Yang Cao
Paulson, Joseph N., et al. "Differential abundance analysis for microbial markergene surveys." Nature methods 10.12 (2013): 12001202.
data(enterotypes_arumugam)
ps < phyloseq::subset_samples(
enterotypes_arumugam,
Enterotype %in% c("Enterotype 3", "Enterotype 2")
)
run_metagenomeseq(ps, group = "Enterotype")
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