run_marker  R Documentation 
Find makers (differentially expressed metagenomic features)
Description
run_marker
is a wrapper of all differential analysis functions.
Usage
run_marker(
ps,
group,
da_method = c("lefse", "simple_t", "simple_welch", "simple_white", "simple_kruskal",
"simple_anova", "edger", "deseq2", "metagenomeseq", "ancom", "ancombc", "aldex",
"limma_voom", "sl_lr", "sl_rf", "sl_svm"),
taxa_rank = "all",
transform = c("identity", "log10", "log10p"),
norm = "none",
norm_para = list(),
p_adjust = c("none", "fdr", "bonferroni", "holm", "hochberg", "hommel", "BH", "BY"),
pvalue_cutoff = 0.05,
...
)
Arguments
ps 
a phyloseq::phyloseq object

group 
character, the variable to set the group

da_method 
character to specify the differential analysis method. The
options include:
"lefse", linear discriminant analysis (LDA) effect size (LEfSe) method,
for more details see run_lefse() .
"simple_t", "simple_welch", "simple_white", "simple_kruskal",
and "simple_anova", simple statistic methods; "simple_t", "simple_welch"
and "simple_white" for two groups comparison; "simple_kruskal", and
"simple_anova" for multiple groups comparison. For more details see
run_simple_stat() .
"edger", see run_edger() .
"deseq2", see run_deseq2() .
"metagenomeseq", differential expression analysis based on the
Zeroinflated LogNormal mixture model or Zeroinflated Gaussian mixture
model using metagenomeSeq, see run_metagenomeseq() .
"ancom", see run_ancom() .
"ancombc", differential analysis of compositions of microbiomes with
bias correction, see run_ancombc() .
"aldex", see run_aldex() .
"limma_voom", see run_limma_voom() .
"sl_lr", "sl_rf", and "sl_svm", there supervised leaning (SL) methods:
logistic regression (lr), random forest (rf), or support vector machine
(svm). For more details see run_sl() .

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

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.

... 
extra arguments passed to the corresponding differential analysis
functions, e.g. run_lefse() .

Details
This function is only a wrapper of all differential analysis
functions, We recommend to use the corresponding function, since it has a
better default arguments setting.
Value
a microbiomeMarker
object.
See Also
run_lefse()
,run_simple_stat()
,run_test_two_groups()
,
run_test_multiple_groups()
,run_edger()
,run_deseq2()
,
run_metagenomeseq
,run_ancom()
,run_ancombc()
,run_aldex()
,
run_limma_voom()
,run_sl()