Differential expression analysis based on the Zero-inflated Log-Normal mixture model or Zero-inflated Gaussian mixture model using metagenomeSeq.
run_metagenomeseq(
ps,
group,
confounders = character(0),
contrast = NULL,
taxa_rank = "all",
transform = c("identity", "log10", "log10p", "SquareRoot", "CubicRoot", "logit"),
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 a phyloseq::phyloseq object.
character, the variable to set the group, must be one of the var of the sample metadata.
character vector, the confounding variables to be adjusted.
default character(0), indicating no confounding variable.
this parameter only used for two groups comparison while there are multiple groups. For more please see the following details.
character to specify taxonomic rank to perform
differential analysis on. Should be one of phyloseq::rank_names(ps),
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(ps), e.g.,
OTU or ASV).
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).
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.
arguments passed to specific normalization methods.
character, which model used for differential analysis, "ZILN" (Zero-inflated Log-Normal mixture model)" or "ZIG" (Zero-inflated Gaussian mixture model). And the zero-inflated log-normal model is preferred due to the high sensitivity and low FDR.
method for multiple test correction, default none,
for more details see stats::p.adjust.
numeric, p value cutoff, default 0.05
extra arguments passed to the model. more details see
metagenomeSeq::fitFeatureModel() and metagenomeSeq::fitZig(),
e.g. control (can be setted using metagenomeSeq::zigControl()) for
metagenomeSeq::fitZig().
a microbiomeMarker object.
metagnomeSeq provides two differential analysis methods, zero-inflated
log-normal mixture model (implemented in
metagenomeSeq::fitFeatureModel()) and zero-inflated 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 ANOVA-like testing to find
markers which difference in any of the groups.
Of note, metagenomeSeq::fitFeatureModel() is not allows for multiple
groups comparison.
Paulson, Joseph N., et al. "Differential abundance analysis for microbial marker-gene surveys." Nature methods 10.12 (2013): 1200-1202.
data(enterotypes_arumugam)
ps <- phyloseq::subset_samples(
enterotypes_arumugam,
Enterotype %in% c("Enterotype 3", "Enterotype 2")
)
run_metagenomeseq(ps, group = "Enterotype")
#> Default value being used.
#> Warning: Partial NA coefficients for 91 probe(s)
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> microbiomeMarker-class inherited from phyloseq-class
#> normalization method: [ CSS ]
#> microbiome marker identity method: [ metagenomeSeq: ZILN ]
#> marker_table() Marker Table: [ 16 microbiome markers with 5 variables ]
#> otu_table() OTU Table: [ 235 taxa and 24 samples ]
#> sample_data() Sample Data: [ 24 samples by 10 sample variables ]
#> tax_table() Taxonomy Table: [ 235 taxa by 1 taxonomic ranks ]