mt-methods: Multiple testing of taxa abundance according to sample...

In phyloseq: Handling and analysis of high-throughput microbiome census data

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="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)

Example output

B=300
b=3	b=6	b=9	b=12	b=15	b=18	b=21	b=24	b=27	b=30	
b=33	b=36	b=39	b=42	b=45	b=48	b=51	b=54	b=57	b=60	
b=63	b=66	b=69	b=72	b=75	b=78	b=81	b=84	b=87	b=90	
b=93	b=96	b=99	b=102	b=105	b=108	b=111	b=114	b=117	b=120	
b=123	b=126	b=129	b=132	b=135	b=138	b=141	b=144	b=147	b=150	
b=153	b=156	b=159	b=162	b=165	b=168	b=171	b=174	b=177	b=180	
b=183	b=186	b=189	b=192	b=195	b=198	b=201	b=204	b=207	b=210	
b=213	b=216	b=219	b=222	b=225	b=228	b=231	b=234	b=237	b=240	
b=243	b=246	b=249	b=252	b=255	b=258	b=261	b=264	b=267	b=270	
b=273	b=276	b=279	b=282	b=285	b=288	b=291	b=294	b=297	b=300	
r=5	r=10	r=15	r=20	r=25	r=30	r=35	r=40	r=45	r=50	
r=55	r=60	r=65	r=70	r=75	r=80	r=85	r=90	r=95	r=100	
r=105	r=110	r=115	r=120	r=125	r=130	r=135	r=140	r=145	r=150	
r=155	r=160	r=165	r=170	r=175	r=180	r=185	r=190	r=195	r=200	
r=205	r=210	r=215	r=220	r=225	r=230	r=235	r=240	r=245	r=250	
r=255	r=260	r=265	r=270	r=275	r=280	r=285	r=290	r=295	r=300	
r=305	r=310	r=315	r=320	r=325	r=330	r=335	r=340	r=345	r=350	
r=355	r=360	r=365	r=370	r=375	r=380	r=385	r=390	r=395	r=400	
r=405	r=410	r=415	r=420	r=425	r=430	r=435	r=440	r=445	r=450	
r=455	r=460	r=465	r=470	r=475	r=480	r=485	r=490	r=495	r=500	
r=505	r=510	r=515	r=520	r=525	r=530	r=535	r=540	r=545	r=550	
                      index   teststat        rawp      adjp      plower
Prevotella              207 344.733232 0.003333333 0.3333333 0.003333333
Bacteroides             203  85.017554 0.003333333 0.3333333 0.003333333
Blautia                 187  19.526091 0.003333333 0.3333333 0.003333333
Bryantella              503  16.383216 0.003333333 0.3333333 0.003333333
Parabacteroides         205  12.894442 0.003333333 0.3333333 0.003333333
Bifidobacterium         240   9.293854 0.003333333 0.3333333 0.003333333
Alistipes               208   8.707611 0.003333333 0.3333333 0.003333333
Collinsella             245   8.125017 0.003333333 0.3333333 0.003333333
Dorea                   182   7.444561 0.003333333 0.3333333 0.003333333
Phascolarctobacterium   513   7.006232 0.003333333 0.3333333 0.003333333
                                      Genus        fdr
Prevotella                       Prevotella 0.04666667
Bacteroides                     Bacteroides 0.04666667
Blautia                             Blautia 0.04666667
Bryantella                       Bryantella 0.04666667
Parabacteroides             Parabacteroides 0.04666667
Bifidobacterium             Bifidobacterium 0.04666667
Alistipes                         Alistipes 0.04666667
Collinsella                     Collinsella 0.04666667
Dorea                                 Dorea 0.04666667
Phascolarctobacterium Phascolarctobacterium 0.04666667

phyloseq documentation built on Nov. 8, 2020, 6:41 p.m.