Test taxa abundances for associations with metadata.

A convenience wrapper for taxa_table() + stats_table().

Usage

taxa_stats(
  biom,
  regr = NULL,
  stat.by = NULL,
  rank = -1,
  taxa = 6,
  lineage = FALSE,
  unc = "singly",
  other = FALSE,
  split.by = NULL,
  transform = "none",
  test = "emmeans",
  fit = "gam",
  at = NULL,
  level = 0.95,
  alt = "!=",
  mu = 0,
  p.adj = "fdr"
)

Arguments

biom

An rbiom object, such as from as_rbiom(). Any value accepted by as_rbiom() can also be given here.

regr

Dataset field with the x-axis (independent; predictive) values. Must be numeric. Default: NULL

stat.by

Dataset field with the statistical groups. Must be categorical. Default: NULL

rank

What rank(s) of taxa to display. E.g. "Phylum", "Genus", ".otu", etc. An integer vector can also be given, where 1 is the highest rank, 2 is the second highest, -1 is the lowest rank, -2 is the second lowest, and 0 is the OTU "rank". Run biom$ranks to see all options for a given rbiom object. Default: -1.

taxa

Which taxa to display. An integer value will show the top n most abundant taxa. A value 0 <= n < 1 will show any taxa with that mean abundance or greater (e.g. 0.1 implies >= 10%). A character vector of taxa names will show only those named taxa. Default: 6.

lineage

Include all ranks in the name of the taxa. For instance, setting to TRUE will produce Bacteria; Actinobacteria; Coriobacteriia; Coriobacteriales. Otherwise the taxa name will simply be Coriobacteriales. You want to set this to TRUE when unc = "asis" and you have taxa names (such as Incertae_Sedis) that map to multiple higher level ranks. Default: FALSE

unc

How to handle unclassified, uncultured, and similarly ambiguous taxa names. Options are:

"singly" -: Replaces them with the OTU name.
"grouped" -: Replaces them with a higher rank's name.
"drop" -: Excludes them from the result.
"asis" -: To not check/modify any taxa names.

Default: "singly"

Abbreviations are allowed.

other

Sum all non-itemized taxa into an "Other" taxa. When FALSE, only returns taxa matched by the taxa argument. Specifying TRUE adds "Other" to the returned set. A string can also be given to imply TRUE, but with that value as the name to use instead of "Other". Default: FALSE

split.by

Dataset field(s) that the data should be split by prior to any calculations. Must be categorical. Default: NULL

transform

Transformation to apply. Options are: c("none", "rank", "log", "log1p", "sqrt", "percent"). "rank" is useful for correcting for non-normally distributions before applying regression statistics. Default: "none"

test

Method for computing p-values: 'wilcox', 'kruskal', 'emmeans', or 'emtrends'. Default: 'emmeans'

fit

How to fit the trendline. 'lm', 'log', or 'gam'. Default: 'gam'

at

Position(s) along the x-axis where the means or slopes should be evaluated. Default: NULL, which samples 100 evenly spaced positions and selects the position where the p-value is most significant.

level

The confidence level for calculating a confidence interval. Default: 0.95

alt

Alternative hypothesis direction. Options are '!=' (two-sided; not equal to mu), '<' (less than mu), or '>' (greater than mu). Default: '!='

mu

Reference value to test against. Default: 0

p.adj

Method to use for multiple comparisons adjustment of p-values. Run p.adjust.methods for a list of available options. Default: "fdr"

Value

A tibble data.frame with fields from the table below. This tibble object provides the $code operator to print the R code used to generate the statistics.

Field	Description
.mean	Estimated marginal mean. See `emmeans::emmeans()`.
.mean.diff	Difference in means.
.slope	Trendline slope. See `emmeans::emtrends()`.
.slope.diff	Difference in slopes.
.h1	Alternate hypothesis.
.p.val	Probability that null hypothesis is correct.
.adj.p	`.p.val` after adjusting for multiple comparisons.
.effect.size	Effect size. See `emmeans::eff_size()`.
.lower	Confidence interval lower bound.
.upper	Confidence interval upper bound.
.se	Standard error.
.n	Number of samples.
.df	Degrees of freedom.
.stat	Wilcoxon or Kruskal-Wallis rank sum statistic.
.t.ratio	`.mean` / `.se`
.r.sqr	Percent of variation explained by the model.
.adj.r	`.r.sqr`, taking degrees of freedom into account.
.aic	Akaike Information Criterion (predictive models).
.bic	Bayesian Information Criterion (descriptive models).
.loglik	Log-likelihood goodness-of-fit score.
.fit.p	P-value for observing this fit by chance.

Examples

    library(rbiom)
    
    biom <- rarefy(hmp50)
    
    taxa_stats(biom, stat.by = "Body Site", rank = "Family")[,1:6]
#> # Model:    gam(.abundance ~ `Body Site`, method = "REML")
#> # A tibble: 60 × 6
#>    .taxa            `Body Site`                .mean.diff .h1     .p.val  .adj.p
#>    <fct>            <chr>                           <dbl> <fct>    <dbl>   <dbl>
#>  1 Bacteroidaceae   Mid vagina - Stool              -608. != 0  5.43e-10 8.66e-9
#>  2 Bacteroidaceae   Saliva - Stool                  -607. != 0  5.61e-10 8.66e-9
#>  3 Bacteroidaceae   Anterior nares - Stool          -607. != 0  5.64e-10 8.66e-9
#>  4 Bacteroidaceae   Buccal mucosa - Stool           -606. != 0  5.77e-10 8.66e-9
#>  5 Streptococcaceae Buccal mucosa - Mid vagina       784. != 0  1.49e- 8 1.78e-7
#>  6 Streptococcaceae Anterior nares - Buccal m…      -763. != 0  2.36e- 8 2.01e-7
#>  7 Lactobacillaceae Buccal mucosa - Mid vagina     -1049. != 0  3.01e- 8 2.01e-7
#>  8 Lactobacillaceae Mid vagina - Saliva             1049. != 0  3.01e- 8 2.01e-7
#>  9 Lactobacillaceae Anterior nares - Mid vagi…     -1048. != 0  3.02e- 8 2.01e-7
#> 10 Streptococcaceae Buccal mucosa - Stool            784. != 0  5.50e- 8 3.30e-7
#> # ℹ 50 more rows

Test taxa abundances for associations with metadata.

Usage

Arguments

Value

See also

Examples