run_marker {microbiomeMarker} | 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
Zero-inflated Log-Normal mixture model or Zero-inflated 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 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
|
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()
[Package
microbiomeMarker version 0.99.1
Index]