Package index
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as_rbiom() - Convert a variety of data types to an rbiom object.
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read_biom() - Parse counts, metadata, taxonomy, and phylogeny from a BIOM file.
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read_fasta() - Parse a fasta file into a named character vector.
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read_tree() - Read a newick formatted phylogenetic tree.
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write_mothur()write_qiime2() - Export data to QIIME 2 or mothur.
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write_biom()write_metadata()write_counts()write_taxonomy()write_fasta()write_tree()write_xlsx() - Save an rbiom object to a file.
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convert_to_SE()convert_to_TSE()convert_to_phyloseq()convert_to_animalcules() - Convert biom data to an external package class.
The rbiom Object
The rbiom object itself includes many methods, including $counts, $metadata, $taxonomy, $samples, $n_samples and more.
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rbiom_objects - Working with rbiom Objects.
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as.list(<rbiom>) - Convert an rbiom object to a base R list.
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as.matrix(<rbiom>) - Convert an rbiom object to a simple count matrix.
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pull(<rbiom>) - Map sample names to metadata field values.
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with(<rbiom>)within(<rbiom>) - Evaluate expressions on metadata.
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mutate(<rbiom>)rename(<rbiom>) - Create, modify, and delete metadata fields.
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glimpse(<rbiom>) - Get a glimpse of your metadata.
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subset(<rbiom>)`[`(<rbiom>)na.omit(<rbiom>)subset_taxa() - Subset an rbiom object by sample names, OTU names, metadata, or taxonomy.
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slice(<rbiom>)slice_head(<rbiom>)slice_tail(<rbiom>)slice_min(<rbiom>)slice_max(<rbiom>)slice_sample(<rbiom>) - Subset to a specific number of samples.
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taxa_boxplot() - Visualize BIOM data with boxplots.
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taxa_clusters() - Cluster samples by taxa abundances k-means.
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taxa_corrplot() - Visualize taxa abundance with scatterplots and trendlines.
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taxa_heatmap() - Display taxa abundances as a heatmap.
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taxa_map() - Map OTUs names to taxa names at a given rank.
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taxa_table()taxa_matrix() - Taxa abundances per sample.
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taxa_stacked() - Display taxa abundances as a stacked bar graph.
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taxa_stats() - Test taxa abundances for associations with metadata.
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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.
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adiv_boxplot() - Visualize alpha diversity with boxplots.
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adiv_corrplot() - Visualize alpha diversity with scatterplots and trendlines.
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adiv_matrix() - Create a matrix of samples x alpha diversity metrics.
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adiv_stats() - Test alpha diversity for associations with metadata.
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adiv_table() - Calculate the alpha diversity of each sample.
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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.
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bdiv_boxplot() - Visualize BIOM data with boxplots.
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bdiv_clusters() - Cluster samples by beta diversity k-means.
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bdiv_corrplot() - Visualize beta diversity with scatterplots and trendlines.
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bdiv_heatmap() - Display beta diversities in an all vs all grid.
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bdiv_ord_plot() - Ordinate samples and taxa on a 2D plane based on beta diversity distances.
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bdiv_ord_table() - Calculate PCoA and other ordinations, including taxa biplots and statistics.
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bdiv_stats() - Test beta diversity for associations with metadata.
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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.
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rare_corrplot() - Visualize rarefaction curves with scatterplots and trendlines.
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rare_multiplot() - Combines rare_corrplot and rare_stacked into a single figure.
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rare_stacked() - Visualize the number of observations per sample.
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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.
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distmat_ord_table() - Run ordinations on a distance matrix.
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distmat_stats() - Run statistics on a distance matrix vs a categorical or numeric variable.
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rarefy_cols()rescale_cols()rescale_rows() - Transform a counts matrix.
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stats_boxplot() - Visualize categorical metadata effects on numeric values.
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stats_corrplot() - Visualize regression with scatterplots and trendlines.
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stats_table() - Run non-parametric statistics on a data.frame.
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tree_subset() - Create a subtree by specifying tips to keep.
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plot_heatmap() - Create a heatmap with tracks and dendrograms from any matrix.
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biom_merge() - Combine several rbiom objects into one.