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An rbiom object, such as from as_rbiom().
Any value accepted by as_rbiom() can also be given here.
A matrix-like object.
A phylo object representing the phylogenetic
relationships of the taxa in biom. Only required when
computing UniFrac distances. Default: biom$tree
When parsing the tree, should underscores be kept as
is? By default they will be converted to spaces (unless the entire ID
is quoted). Default FALSE
Dataset field(s) to include in the output data frame, or '.all'
to include all metadata fields. Default: '.all'
Alpha diversity metric(s) to use. Options are: "OTUs",
"Shannon", "Chao1", "Simpson", and/or
"InvSimpson". Set adiv=".all" to use all metrics.
Multiple/abbreviated values allowed.
Default: "Shannon"
Beta diversity distance algorithm(s) to use. Options are:
"Bray-Curtis", "Manhattan", "Euclidean",
"Jaccard", and "UniFrac". For "UniFrac", a
phylogenetic tree must be present in biom or explicitly
provided via tree=. Multiple/abbreviated values allowed.
Default: "Bray-Curtis"
Which taxa to display. An integer value will show the top n
most abundant taxa. A value 0 <= n < 1 will show any taxa with that
mean abundance or greater (e.g. 0.1 implies >= 10%). A
character vector of taxa names will show only those named taxa.
Default: 6.
Method for reducing dimensionality. Options are:
"PCoA" - Principal coordinate analysis; ape::pcoa().
"UMAP" - Uniform manifold approximation and projection; uwot::umap().
"NMDS" - Nonmetric multidimensional scaling; vegan::metaMDS().
"tSNE" - t-distributed stochastic neighbor embedding; tsne::tsne().
Multiple/abbreviated values allowed. Default: "PCoA"
Take relative abundances into account. When
weighted=FALSE, only presence/absence is considered.
Multiple values allowed. Default: TRUE
Only changes the "Weighted UniFrac" calculation.
Divides result by the total branch weights. Default: TRUE
For numeric metadata, report the absolute difference in values
for the two samples, for instance 2 instead of "10 vs 12".
Default: TRUE
What rank(s) of taxa to display. E.g. "Phylum",
"Genus", ".otu", etc. An integer vector can also be
given, where 1 is the highest rank, 2 is the second
highest, -1 is the lowest rank, -2 is the second
lowest, and 0 is the OTU "rank". Run biom$ranks to
see all options for a given rbiom object. Default: -1.
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
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.
Abbreviations are allowed. Default: "singly"
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
If TRUE, returns a
slam::simple_triplet_matrix(), otherwise returns a
normal R matrix object. Default: FALSE
Only display taxa with the most significant differences in
abundance. If p.top is >= 1, then the p.top most
significant taxa are displayed. If p.top is less than one, all
taxa with an adjusted p-value <= p.top are displayed.
Recommended to be used in combination with the taxa parameter
to set a lower bound on the mean abundance of considered taxa.
Default: Inf
The transformation to apply to the y-axis. Visualizing differences of both high- and low-abundance taxa is best done with a non-linear axis. Options are:
"sqrt" - square-root transformation
"log1p" - log(y + 1) transformation
"none" - no transformation
These methods allow visualization of both high- and low-abundance
taxa simultaneously, without complaint about 'zero' count
observations. Default: "sqrt"
Use xaxis.transform or yaxis.transform to pass custom values
directly to ggplot2's scale_* functions.
Transpose the axes, so that taxa are present as rows instead
of columns. Default: FALSE
Shade every other x position. Default: same as flip
How to calculate min/max of the crossbar,
errorbar, linerange, and pointrange layers.
Options are: "ci" (confidence interval), "range",
"sd" (standard deviation), "se" (standard error), and
"mad" (median absolute deviation).
The center mark of crossbar and pointrange represents
the mean, except for "mad" in which case it represents the median.
Default: "ci"
Minimum adjusted p-value to display on the plot with a bracket.
p.label = 0.05 - Show p-values that are <= 0.05.
p.label = 0 - Don't show any p-values on the plot.
p.label = 1 - Show all p-values on the plot.
If a numeric vector with more than one value is
provided, they will be used as breaks for asterisk notation.
Default: 0.05
The confidence level for calculating a confidence interval.
Default: 0.95
Add methodology caption beneath the plot.
Default: TRUE
Show boxplot outliers? TRUE to always show.
FALSE to always hide. NULL to only hide them when
overlaying a dot or strip chart. Default: NULL
Angle of the labels at the bottom of the plot.
Options are "auto", '0', '30', and '90'.
Default: "auto".
Number of ordination dimensions to return. Either 2L or
3L. Default: 2L
Dataset field(s) that the data should be split by prior to
any calculations. Must be categorical. Default: NULL
A dist-class distance matrix, as returned from
bdiv_distmat() or stats::dist(). Required.
A named vector of grouping values. The names should
correspond to attr(dm, 'Labels'). Values can be either
categorical or numeric. Required.
The dataset (data.frame or tibble object). "Dataset fields"
mentioned below should match column names in df. Required.
Dataset field with the x-axis (independent; predictive)
values. Must be numeric. Default: NULL
Dataset field with the y-axis (dependent; response) values,
such as taxa abundance or alpha diversity.
Default: attr(df, 'response')
Dataset field with the statistical groups. Must be
categorical. Default: NULL
Dataset field with the group to color by. Must be
categorical. Default: stat.by
Dataset field with the group for shapes. Must be
categorical. Default: stat.by
Dataset field(s) to use for faceting. Must be categorical.
Default: NULL
How to color the groups. Options are:
TRUE - Automatically select colorblind-friendly colors.
FALSE or NULL - Don't use colors.
Auto-select colors from this set. E.g. "okabe"
Custom colors to use. E.g. c("red", "#00FF00")
Explicit mapping. E.g. c(Male = "blue", Female = "red")
See "Aesthetics" section below for additional information.
Default: TRUE
Shapes for each group.
Options are similar to colors's: TRUE, FALSE, NULL, shape
names (typically integers 0 - 17), or a named vector mapping
groups to specific shape names.
See "Aesthetics" section below for additional information.
Default: TRUE
Patterns for each group.
Options are similar to colors's: TRUE, FALSE, NULL, pattern
names ("brick", "chevron", "fish", "grid", etc), or a named
vector mapping groups to specific pattern names.
See "Aesthetics" section below for additional information.
Default: FALSE
Method for computing p-values: 'wilcox', 'kruskal',
'emmeans', or 'emtrends'. Default: 'emmeans'
How to fit the trendline. 'lm', 'log', or 'gam'.
Default: 'gam'
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.
Alternative hypothesis direction. Options are '!='
(two-sided; not equal to mu), '<' (less than mu), or '>'
(greater than mu). Default: '!='
Reference value to test against. Default: 0
Generate additional plots to aid in assessing data normality.
Default: FALSE
Dataset field(s) for intra- or inter- sample
comparisons. Alternatively, dataset field names given elsewhere can
be prefixed with '==' or '!=' to assign them to within or
between, respectively. Default: NULL
Random seed for permutations. Must be a non-negative integer.
Default: 0
The number of CPUs to use. Set to NULL to use all available,
or to 1 to disable parallel processing. Default: NULL
Number of random permutations to use.
Default: 999
Method to use for multiple comparisons adjustment of
p-values. Run p.adjust.methods for a list of available
options. Default: "fdr"
Rarefaction depths to show in the plot, or NULL to
auto-select. Default: NULL
Where to draw a horizontal line on the plot, intended to show
a particular rarefaction depth. Set to TRUE to show an
auto-selected rarefaction depth or FALSE to not show a line.
Default: NULL
Create a copy of biom before modifying. If FALSE, biom
is modified in place as a side-effect. See speed ups for
use cases. Default: TRUE
Show sample names under each bar. Default: FALSE
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"
When transform="rank", how to rank identical values.
Options are: c("average", "first", "last", "random", "max", "min").
See rank() for details. Default: "random"