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In rbiom: Read/Write, Analyze, and Visualize 'BIOM' Data

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Description

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Arguments

biom	An rbiom object, such as from as_rbiom(). Any value accepted by as_rbiom() can also be given here.
mtx	A matrix-like object.
tree	A phylo object representing the phylogenetic relationships of the taxa in biom. Only required when computing UniFrac distances. Default: biom$tree
underscores	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
md	Dataset field(s) to include in the output data frame, or '.all' to include all metadata fields. Default: '.all'
adiv	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"
bdiv	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"
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.
ord	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"
weighted	Take relative abundances into account. When weighted=FALSE, only presence/absence is considered. Multiple values allowed. Default: TRUE
normalized	Only changes the "Weighted UniFrac" calculation. Divides result by the total branch weights. Default: TRUE
delta	For numeric metadata, report the absolute difference in values for the two samples, for instance 2 instead of "10 vs 12". Default: TRUE
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.
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. Abbreviations are allowed. Default: "singly"
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
sparse	If TRUE, returns a slam::simple_triplet_matrix(), otherwise returns a normal R matrix object. Default: FALSE
p.top	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
y.transform	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.
flip	Transpose the axes, so that taxa are present as rows instead of columns. Default: FALSE
stripe	Shade every other x position. Default: same as flip
ci	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"
p.label	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
level	The confidence level for calculating a confidence interval. Default: 0.95
caption	Add methodology caption beneath the plot. Default: TRUE
outliers	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
xlab.angle	Angle of the labels at the bottom of the plot. Options are "auto", '0', '30', and '90'. Default: "auto".
k	Number of ordination dimensions to return. Either 2L or 3L. Default: 2L
split.by	Dataset field(s) that the data should be split by prior to any calculations. Must be categorical. Default: NULL
dm	A dist-class distance matrix, as returned from bdiv_distmat() or stats::dist(). Required.
groups	A named vector of grouping values. The names should correspond to attr(dm, 'Labels'). Values can be either categorical or numeric. Required.
df	The dataset (data.frame or tibble object). "Dataset fields" mentioned below should match column names in df. Required.
regr	Dataset field with the x-axis (independent; predictive) values. Must be numeric. Default: NULL
resp	Dataset field with the y-axis (dependent; response) values, such as taxa abundance or alpha diversity. Default: attr(df, 'response')
stat.by	Dataset field with the statistical groups. Must be categorical. Default: NULL
color.by	Dataset field with the group to color by. Must be categorical. Default: stat.by
shape.by	Dataset field with the group for shapes. Must be categorical. Default: stat.by
facet.by	Dataset field(s) to use for faceting. Must be categorical. Default: NULL
colors	How to color the groups. Options are: TRUE - Automatically select colorblind-friendly colors. FALSE or NULL - Don't use colors. a palette name - Auto-select colors from this set. E.g. "okabe" character vector - Custom colors to use. E.g. c("red", "#00FF00") named character vector - Explicit mapping. E.g. c(Male = "blue", Female = "red") See "Aesthetics" section below for additional information. Default: TRUE
shapes	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	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
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.
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
check	Generate additional plots to aid in assessing data normality. Default: FALSE
within, between	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
seed	Random seed for permutations. Must be a non-negative integer. Default: 0
cpus	The number of CPUs to use. Set to NULL to use all available, or to 1 to disable parallel processing. Default: NULL
permutations	Number of random permutations to use. Default: 999
p.adj	Method to use for multiple comparisons adjustment of p-values. Run p.adjust.methods for a list of available options. Default: "fdr"
depths	Rarefaction depths to show in the plot, or NULL to auto-select. Default: NULL
rline	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
clone	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
labels	Show sample names under each bar. Default: FALSE
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"
ties	When transform="rank", how to rank identical values. Options are: c("average", "first", "last", "random", "max", "min"). See rank() for details. Default: "random"

rbiom documentation built on April 3, 2025, 6:39 p.m.