mt {phyloseq} | R Documentation

## Multiple testing of taxa abundance according to sample categories/classes

### Description

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`.

### Usage

    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", ...)

### Arguments

`physeq` | (Required). `otu_table-class` or `phyloseq-class`. In this multiple testing framework, different taxa correspond to variables (hypotheses), and samples to observations.
`classlabel` | (Required). A single character index of the sample-variable in the `sample_data` of `physeq` that will be used for multiple testing. Alternatively, `classlabel` can be a custom integer (or numeric coercable to an integer), character, or factor with length equal to `nsamples(physeq)`. NOTE: the default test applied to each taxa is a two-sample two-sided `t.test`, WHICH WILL FAIL with an error if you provide a data variable (or custom vector) that contains MORE THAN TWO classes. One alternative to consider is an F-test, by specifying `test="f"` as an additional argument. See the first example below, and/or further documentation of `mt.maxT` or `mt.minP` for other options and formal details.
`minPmaxT` | (Optional). Character string. `"mt.minP"` or `"mt.maxT"`. Default is to use `"mt.minP"`.
`method` | (Optional). Additional multiple-hypthesis correction methods. A character vector from the set `p.adjust.methods`. Default is `"fdr"`, for the Benjamini and Hochberg (1995) method to control False Discovery Rate (FDR). This argument is passed on to `p.adjust`, please see that documentation for more details.
`...` | (Optional). Additional arguments, forwarded to `mt.maxT` or `mt.minP`

### Value

A dataframe with components specified in the documentation for `mt.maxT` or `mt.minP`, respectively.

### See Also

`mt.maxT`

`mt.minP`

`p.adjust`

### Examples

    ## # 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=c("fdr", "bonferroni"), 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)

[Package phyloseq version 1.24.2 Index]