Package index
-
as_rbiom()
- Convert a variety of data types to an rbiom object.
-
read_fasta()
- Parse a fasta file into a named character vector.
-
read_tree()
- Read a newick formatted phylogenetic tree.
-
write_biom()
write_metadata()
write_counts()
write_taxonomy()
write_fasta()
write_tree()
write_xlsx()
- Save an rbiom object to a file.
-
convert_to_SE()
convert_to_TSE()
- Convert an rbiom object to a SummarizedExperiment object.
The rbiom Object
The rbiom object itself includes many methods, including $counts
, $metadata
, $taxonomy
, $samples, $n_samples and more.
-
rbiom_objects
- Working with rbiom Objects.
-
as.list(<rbiom>)
- Convert an rbiom object to a base R list.
-
as.matrix(<rbiom>)
- Convert an rbiom object to a simple count matrix.
-
pull(<rbiom>)
- Map sample names to metadata field values.
-
with(<rbiom>)
within(<rbiom>)
- Evaluate expressions on metadata.
-
mutate(<rbiom>)
rename(<rbiom>)
- Create, modify, and delete metadata fields.
-
glimpse(<rbiom>)
- Get a glimpse of your metadata.
-
subset(<rbiom>)
`[`(<rbiom>)
na.omit(<rbiom>)
subset_taxa()
- Subset an rbiom object by sample names, OTU names, metadata, or taxonomy.
-
slice(<rbiom>)
slice_head(<rbiom>)
slice_tail(<rbiom>)
slice_min(<rbiom>)
slice_max(<rbiom>)
slice_sample(<rbiom>)
- Subset to a specific number of samples.
-
rarefy()
- Rarefy OTU counts.
-
taxa_boxplot()
- Visualize BIOM data with boxplots.
-
taxa_clusters()
- Define sample kmeans clusters from taxa abundances.
-
taxa_corrplot()
- Visualize taxa abundance with scatterplots and trendlines.
-
taxa_heatmap()
- Display taxa abundances as a heatmap.
-
taxa_map()
- Map OTUs names to taxa names at a given rank.
-
taxa_table()
taxa_matrix()
- Taxa abundances per sample.
-
taxa_stacked()
- Display taxa abundances as a stacked bar graph.
-
taxa_stats()
- Test taxa abundances for associations with metadata.
-
taxa_sums()
taxa_means()
taxa_apply()
- Get summary taxa abundances.
Alpha Diversity
Examine the diversity of OTUs present in each individual sample, and how that diversity correlates with metadata.
-
adiv_boxplot()
- Visualize alpha diversity with boxplots.
-
adiv_corrplot()
- Visualize alpha diversity with scatterplots and trendlines.
-
adiv_matrix()
- Create a matrix of samples x alpha diversity metrics.
-
adiv_stats()
- Test alpha diversity for associations with metadata.
-
adiv_table()
- Calculate the alpha diversity of each sample.
-
sample_sums()
sample_apply()
- Summarize the taxa observations in each sample.
Beta Diversity
See how similiar samples are to each other, and what metadata/taxa influence clustering.
-
bdiv_boxplot()
- Visualize BIOM data with boxplots.
-
bdiv_clusters()
- Define sample PAM clusters from beta diversity.
-
bdiv_corrplot()
- Visualize beta diversity with scatterplots and trendlines.
-
bdiv_heatmap()
- Display beta diversities in an all vs all grid.
-
bdiv_ord_plot()
- Ordinate samples and taxa on a 2D plane based on beta diversity distances.
-
bdiv_ord_table()
- Calculate PCoA and other ordinations, including taxa biplots and statistics.
-
bdiv_stats()
- Test beta diversity for associations with metadata.
-
bdiv_table()
bdiv_matrix()
bdiv_distmat()
- Distance / dissimilarity between samples.
Rarefaction
Improve signal-to-noise in analyses by ensuring all samples have an equal number of observations.
-
rare_corrplot()
- Visualize rarefaction curves with scatterplots and trendlines.
-
rare_multiplot()
- Combines rare_corrplot and rare_stacked into a single figure.
-
rare_stacked()
- Visualize the number of observations per sample.
-
rarefy()
- Rarefy OTU counts.
Low Level Functions
Most functions operate on rbiom objects. These let you use arbitrary data.frames, distance matrices, matrices, and phylo objects.
-
distmat_ord_table()
- Run ordinations on a distance matrix.
-
distmat_stats()
- Run statistics on a distance matrix vs a categorical or numeric variable.
-
rarefy_cols()
rescale_cols()
rescale_rows()
- Transform a counts matrix.
-
stats_boxplot()
- Visualize categorical metadata effects on numeric values.
-
stats_corrplot()
- Visualize regression with scatterplots and trendlines.
-
stats_table()
- Run non-parametric statistics on a data.frame.
-
tree_subset()
- Create a subtree by specifying tips to keep.
-
plot_heatmap()
- Create a heatmap with tracks and dendrograms from any matrix.
-
biom_merge()
- Combine several rbiom objects into one.