Package index

Tidytacos

Package overview

tidytacos-package: tidytacos: Manipulate Taxonomic Composition Data of Microbial Communities

tidytacos: tidytacos: Functions to manipulate and visualize amplicon count data.

File handling

Functions for loading in or creating tidytaco objects or converting to another datatype.

read_tidytacos(): Read community data written by tidytacos

write_tidytacos(): Write community data in tidytacos format

merge_tidytacos(): Merge two tidytacos objects

create_tidytacos(): Initiate tidytacos object

add_metadata(): Add metadata to the tidytacos object

from_dada(): DADA2 to a tidytacos object

from_phyloseq(): Convert phyloseq object to tidytacos object

as_phyloseq(): Convert tidytacos object to phyloseq object

to_biom(): Write the counts of the tidytacos object to a biom file

to_fasta(): Write the sequences of the taxa table to a fasta file

Table manipulation

Functions for manipulation of the three distinct tables using tidy-related functions.

samples(): Extract the sample table

taxa(): Extract the taxon table

counts(): Extract the count table

select_counts(): Retain or remove a set of count variables

select_samples(): Retain or remove a set of sample variables

select_taxa(): Retain or remove a set of taxon variables

mutate_counts(): Create extra variables in the count table

mutate_samples(): Create extra variables in the sample table

mutate_taxa(): Create extra variables in the taxa table

filter_counts(): Filter the counts

filter_samples(): Filter the samples

filter_taxa(): Filter the taxa

aggregate_samples(): Aggregate samples with identical values for all metadata

aggregate_taxa(): Aggregate taxa on a given taxonomic rank

everything(): Get all data in one single table

Relative abundance, prevalence and other count computations

Functions for transforming the count table to different matrix representations or pairwise comparison.

counts_matrix(): Return a counts matrix

rel_abundance_matrix(): Return a relative abundance matrix

add_rel_abundance(): Add relative abundances to count table

add_mean_rel_abundance(): Add average relative abundances to taxa table

mean_rel_abundances(): Get mean relative abundances of taxa in general or per condition

add_absolute_abundance(): Add absolute abundances to count table

add_spike_ratio(): Add spike ratio

add_density(): Add density to count table

add_logratio(): Add logratios

add_clr_abundance(): Perform a centered log ratio transformation on the readcounts.

add_jervis_bardy(): Apply the taxon QC method of Jervis-Bardy

prevalences(): Get prevalences of taxa in general or per condition

add_prevalence(): Add taxon prevalences to the taxon table

counts_tidy(): Convert matrix with counts to tidy data frame

add_total_absolute_abundance(): Add total absolute abundances of samples

add_total_count(): Add total read count per sample

add_total_density(): Add total densities of samples

Plotting

Functions for various plots that can be made with the data in a tidytacos object.

tacoplot_alphas(): Return a boxplot of every alpha metric per group in the samples table of a tidytaco object. If no alpha metrics are present, all available ones are added.

tacoplot_codifab(): Generate a compositional differential abundance plot

tacoplot_euler(): Return an euler diagram of overlapping taxon_ids between conditions

tacoplot_ord(): Return an ordination plot of the samples

tacoplot_ord_ly(): Return an interactive ordination plot of the samples

tacoplot_prevalences(): Return a heatmap of prevalence of taxa in groups of samples

tacoplot_scree(): Return a scree plot to visualize the eigenvalues of the PCA.

tacoplot_stack(): Return a bar plot of the samples

tacoplot_stack_ly(): Return an interactive bar plot of the samples

tacoplot_venn(): Return a venn diagram of overlapping taxon_ids between conditions

tacoplot_venn_ly(): Return an interactive venn diagram of overlapping taxon_ids between conditions

tacoplot_zoom(): Return a visualization designed for a small number of samples

Statistical tests

Functions for various statistical tests that can be performed on a tidytacos object.

perform_adonis(): Perform an adonis test

perform_anosim(): Perform anosim test

perform_mantel_test(): Determine the correlation between the distance of the counts in a tidytacos object and a sample variable, multiple sample variables or another matrix.

add_codifab(): Perform compositional differential abundance analysis

Functions to manipulate taxonomy names.

rank_names(): Return rank names associated with a tidytacos object if these are defined. In case of undefined rank names, the function returns the taxon_id field.

set_rank_names(): Set rank names for a tidytacos object

add_taxon_name(): Add sensible taxon name to taxon table

add_taxon_name_color(): Add taxon color for visualization.

add_eigentaxa(): Calculates eigentaxa values based on SparCC - MCL generated clusters per sample. It is advised to run cluster_taxa() on the tidytacos object before running this function to add the clusters if you want to stray from any default parameters.

Diversity analysis

Functions to add various diversity metrics to the tidytacos object.

add_alpha(): Add alpha diversity measure

add_alphas(): Add alpha diversity measures

betas(): Get beta diversity table

add_ord(): Add ordination

add_copca(): Add compositional principal components to the sample table

add_tree(): Construct a phylogeny from ASV sequences

calculate_unifrac_distances(): Calculate unifrac distance matrix from a tidytacos object with a rooted tree

Clustering

Functions to cluster the sample and taxa table.

cluster_samples(): Clusters samples into n clusters

cluster_taxa(): Performs SparCC network analysis on a tidytacos object and then performs Markov Clustering on the network to annotate taxa of the largest clusters in the tidytacos object.

add_sample_clustered(): Add clustering-based sample order

Network analysis

Functions to perform network inference, clustering and filtering the resulting clusters.

network(): Perform network inference with SparCC on a tidytacos object, after dropping rare taxa. See SpiecEasi::sparcc().

filter_network(): Filters the output of network() to a minimal threshold and transforms to matrix for downstream clustering or heatplot visualization.

cluster_network(): Performs Markov Clustering on a sparcc network matrix generated by filter_network(). and returns a tibble with clusters and taxon identifiers. Optionally, the network can be visualized.

Datasets

Test datasets used as examples.

urt: Upper Respiratory Tract samples

leaf: Phylosphere samples

Miscellaneous

Functions to tidy up the three tables or convert to other data structures.

trim_asvs(): Trim all sequences

classify_taxa(): (Re)classify amplicon sequences

reset_ids(): Reset the taxon and sample IDs

change_id_samples(): Change sample IDs to a given expression

change_id_taxa(): Change taxon IDs to a given expression

remove_empty_samples(): Removes empty samples from the tidytacos object

tidy_count_to_matrix(): Convert counts tidy data frame to matrix

rarefy(): Rarefy the samples to a given number of reads

test_taco(): Create a tidytacos object for testing/example purporses

taxonlist_per_condition(): Return a list of taxon IDs per condition

tacosum(): Return some descriptive numbers