mt | R Documentation |
Please note that it is up to you to perform any necessary
normalizing / standardizing transformations prior to these tests.
See for instance transform_sample_counts
.
mt(physeq, classlabel, minPmaxT = "minP", method = "fdr", ...) ## S4 method for signature 'phyloseq,ANY' mt(physeq, classlabel, minPmaxT = "minP", method = "fdr", ...) ## S4 method for signature 'otu_table,integer' mt(physeq, classlabel, minPmaxT = "minP", method = "fdr", ...) ## S4 method for signature 'otu_table,numeric' mt(physeq, classlabel, minPmaxT = "minP", method = "fdr", ...) ## S4 method for signature 'otu_table,logical' mt(physeq, classlabel, minPmaxT = "minP", method = "fdr", ...) ## S4 method for signature 'otu_table,character' mt(physeq, classlabel, minPmaxT = "minP", method = "fdr", ...) ## S4 method for signature 'otu_table,factor' mt(physeq, classlabel, minPmaxT = "minP", method = "fdr", ...)
physeq |
(Required). |
classlabel |
(Required). A single character index of the sample-variable
in the NOTE: the default test applied to each taxa is a two-sample two-sided
|
minPmaxT |
(Optional). Character string. |
method |
(Optional). Additional multiple-hypthesis correction methods.
A character vector from the set |
... |
(Optional). Additional arguments, forwarded to
|
A dataframe with components specified in the documentation for
mt.maxT
or mt.minP
, respectively.
mt.maxT
mt.minP
p.adjust
## # Simple example, testing genera that sig correlate with Enterotypes data(enterotype) # Filter samples that don't have Enterotype x <- subset_samples(enterotype, !is.na(Enterotype)) # (the taxa are at the genera level in this dataset) res = mt(x, "Enterotype", method="fdr", test="f", B=300) head(res, 10) ## # Not surprisingly, Prevotella and Bacteroides top the list. ## # Different test, multiple-adjusted t-test, whether samples are ent-2 or not. ## mt(x, get_variable(x, "Enterotype")==2)
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