Package 'MicrobiomeR'

Description

A function similar to the phyloseq::tax_glom function, that assembles abundance data at a specified rank. This removes subtaxa and reassigns the values at the specified rank.

Usage

agglomerate_taxmap(obj, rank, validated = FALSE)

Arguments

Details

This function helps analyzing taxonomic data at intermediate ranks by agglomerating the observation data and the taxmap object.

Value

A taxmap object that has been agglomerated at the specified rank.

See Also

filter_taxa

validate_MicrobiomeR_format

Other Advanced Metacoder Filters: cov_filter(), otu_prevalence_filter(), otu_proportion_filter(), taxa_prevalence_filter()

Examples

## Not run: 
if(interactive()){
# This example uses data that are no longer available in the MicrobiomeR package,
# however, they can be easily generated with \code{\link{MicrobiomeR}{as_basic_format}}.
library(MicrobiomeR)
basic_silva <- as_MicrobiomeR_format(MicrobiomeR::raw_silva_2, "basic_format")
phylum_obj <- agglomerate_taxmap(obj = basic_silva, rank = "Phylum")
class_obj <- agglomerate_taxmap(obj = basic_silva, rank = "Class")
order_obj <- agglomerate_taxmap(obj = basic_silva, rank = "Order")
 }

## End(Not run)

Description

This function generates various alpha diversity measures include Shannon, Fisher, Coverage, GiniSimpson, and InverseSimpson.

Usage

alpha_diversity_measures(obj, group = "TreatmentGroup")

Arguments

Value

Returns a list of alpha diversity measures with metadata.

See Also

View alpha,meta to learn more about generating diversity measures with the microbiome package.

Examples

## Not run: 
if (interactive()) {
  library(MicrobiomeR)
  data <- analyzed_silva
  measures <- alpha_diversity_measures(data)
  measures$Shannon
}

## End(Not run)

Description

Plot the alpha diversity using a violin plot. alpha_diversity_plots generates plots for all alpha diversity measures.

Usage

alpha_diversity_plot(
  obj,
  measure = "Shannon",
  group = "TreatmentGroup",
  select_otu_table = NULL,
  title = NULL
)

alpha_diversity_plots(
  obj,
  measures = c("Shannon", "GiniSimpson", "InverseSimpson"),
  group = "TreatmentGroup"
)

Arguments

Details

Alpha diversity helps to determine the species richness (the number of different species in a sample) or evenness (similar abundance level). We prefer to use Shannon as it is better for data generated using the QIIME pipeline.

Value

Returns an alpha diversity plot.

Returns a melted dataframe.

See Also

alpha_diversity_measures, diversity, ggviolin

Other Visualizations: correlation_data(), correlation_plot(), correlation_plots(), heat_tree_parameters(), heat_tree_plots(), ordination_plot(), plot_limits(), save_alpha_diversity_plots(), save_correlation_plots(), save_heat_tree_plots(), save_ordination_plots(), save_stacked_barplots(), stacked_barplot(), stacked_barplots(), top_coefficients_barplot()

Other Visualizations: correlation_data(), correlation_plot(), correlation_plots(), heat_tree_parameters(), heat_tree_plots(), ordination_plot(), plot_limits(), save_alpha_diversity_plots(), save_correlation_plots(), save_heat_tree_plots(), save_ordination_plots(), save_stacked_barplots(), stacked_barplot(), stacked_barplots(), top_coefficients_barplot()

Examples

## Not run: 
if (interactive()) {
  library(MicrobiomeR)
  data <- analyzed_silva
  plot <- alpha_diversity_plot(obj = data,
                               measure = "Shannon")
  plot
}

## End(Not run)

Description

Converts a Taxmap object to the analyzed_format.

Usage

as_analyzed_format(
  obj,
  cols = NULL,
  groups = NULL,
  combinations = NULL,
  out_names = NULL,
  comp_func = metacoder_comp_func_1
)

Arguments

Details

See the MicrobiomeR_Formats documentation.

Value

A Taxmap object in the "analyzed_format".

See Also

is_phyloseq_format, is_raw_format, is_basic_format, is_analyzed_format, as_raw_format, as_basic_format, order_metacoder_data

compare_groups

taxonomy_table, taxon_ids

Other Formatting: MicrobiomeR_Formats, as_MicrobiomeR_format(), as_basic_format(), as_custom_format(), as_phyloseq_format(), as_raw_format(), melt_taxmap(), treatment_matrix()

Description

Converts a Taxmap object to the basic_format.

Usage

as_basic_format(obj, cols = NULL, out_names = NULL)

Arguments

Details

See the MicrobiomeR_Formats documentation.

Value

A Taxmap object in the "basic_format".

See Also

is_phyloseq_format, is_raw_format, is_basic_format, as_raw_format, order_metacoder_data

calc_taxon_abund, calc_obs_props

Other Formatting: MicrobiomeR_Formats, as_MicrobiomeR_format(), as_analyzed_format(), as_custom_format(), as_phyloseq_format(), as_raw_format(), melt_taxmap(), treatment_matrix()

Description

A function for formatting Taxmap objects in the MicrobiomeR format. This function attempts to give more customization than the as_*_format functions.

Usage

as_custom_format(obj, format, change_name_list = NULL, ...)

Arguments

Details

This function is meant to be more helpful for customizing the Taxmap object.

Value

A Taxmap object that we have tried to format with all of our heart.

See Also

create_taxmap, which_format, order_metacoder_data, as_MicrobiomeR_format

Other Formatting: MicrobiomeR_Formats, as_MicrobiomeR_format(), as_analyzed_format(), as_basic_format(), as_phyloseq_format(), as_raw_format(), melt_taxmap(), treatment_matrix()

Description

Converts a Taxmap object to the specified format.

Usage

as_MicrobiomeR_format(obj, format, ...)

Arguments

Details

See the MicrobiomeR_Formats documentation.

Value

A Taxmap object in the specified format.

See Also

which_format, as_raw_format, as_basic_format, as_analyzed_format, as_phyloseq_format, create_taxmap, order_metacoder_data

Other Formatting: MicrobiomeR_Formats, as_analyzed_format(), as_basic_format(), as_custom_format(), as_phyloseq_format(), as_raw_format(), melt_taxmap(), treatment_matrix()

Description

Converts the Taxmap object to the phyloseq_format.

Usage

as_phyloseq_format(
  obj,
  otu_table = "otu_abundance",
  tax_data = "otu_annotations",
  sample_data = "sample_data",
  phy_tree = "phy_tree"
)

Arguments

Details

See the MicrobiomeR_Formats documentation.

Value

A Taxmap object in the phyloseq_format.

See Also

create_taxmap,order_metacoder_data

Other Formatting: MicrobiomeR_Formats, as_MicrobiomeR_format(), as_analyzed_format(), as_basic_format(), as_custom_format(), as_raw_format(), melt_taxmap(), treatment_matrix()

Description

Converts a Taxmap object to the raw_format.

Usage

as_raw_format(obj)

Arguments

Details

See the MicrobiomeR_Formats documentation.

Value

A Taxmap object in the "raw_format".

See Also

is_phyloseq_format, is_raw_format, order_metacoder_data @importFrom crayon silver red green

Other Formatting: MicrobiomeR_Formats, as_MicrobiomeR_format(), as_analyzed_format(), as_basic_format(), as_custom_format(), as_phyloseq_format(), melt_taxmap(), treatment_matrix()

Description

This function uses dynamic arguments (...) in order to combine multiple color palettes together.

Usage

combination_palette(...)

Arguments

Details

This function allows you to combine a varying number of color palettes and gives you the ability to subset and reverse the palettes that are supplied.

Value

The output of this function is another function (grDevoces::colorRampPalette), which takes a number to generate an interpolated color palette as a character vector.

See Also

colorRamp

Other Color Palettes: get_color_palette(), scico_palette(), viridis_magma_palette(), viridis_palette()

Examples

## Not run: 
if(interactive()){
# Below is the code for the viridis_magma_palette function.
# It's a good example of how to use the combination_palette function.
viridis_magma_palette <- function(viridis_number = 800,
                                  viridis_range = 300:viridis_number,
                                  viridis_rev = TRUE,
                                  magma_number = 500,
                                  magma_range = 0:magma_number,
                                  magma_rev = FALSE,
                                  ...) {
    if (!missing(...)){
        v_args = list(n=viridis_number, ...)
        m_args = list(n=magma_number, ...)
    } else {
        v_args = list(n=viridis_number)
        m_args = list(n=magma_number)
        }
   crp <- combination_palette(viridis =
                                list(palette = viridis::viridis,
                                      args = v_args,
                                      range = viridis_range,
                                      rev = viridis_rev),
                               magma =
                                 list(palette = viridis::magma,
                                      args = m_args,
                                      range = magma_range,
                                      rev = magma_rev)
                               )
 return(crp)
}
 }

## End(Not run)

Description

Convert Proportions

Usage

convert_proportions(melted_df, tax_level)

Arguments

See Also

Other Data Manipulators: metacoder_comp_func_1(), transformer(), transposer(), vlookup()

Description

Get the correlation plot data comparing all of the treatment groups.

Usage

correlation_data(obj, primary_rank, secondary_rank = TRUE, wp_value = 0.05)

Arguments

Details

DETAILS

Value

Data for the correlation plots.

See Also

agglomerate_taxmap, vlookup

data mask programming, mutate, filter, arrange

Other Visualizations: alpha_diversity_plot(), correlation_plot(), correlation_plots(), heat_tree_parameters(), heat_tree_plots(), ordination_plot(), plot_limits(), save_alpha_diversity_plots(), save_correlation_plots(), save_heat_tree_plots(), save_ordination_plots(), save_stacked_barplots(), stacked_barplot(), stacked_barplots(), top_coefficients_barplot()

Examples

## Not run: 
if(interactive()){
 #EXAMPLE1
 }

## End(Not run)

Description

Create a correlation plot from a metacoder/taxmap object.

Usage

correlation_plot(
  obj,
  primary_rank,
  secondary_rank = TRUE,
  wp_value = 0.05,
  pal_func = NULL,
  trans = "logit"
)

Arguments

Details

Correlation plots help to better explain the heat tree findings.

Value

A 1:1 correlation plot built with ggplot2.

See Also

create_taxmap, validate_MicrobiomeR_format, correlation_data, plot_limits, get_color_palette

ggplot, aes, geom_polygon, geom_point, labs, scale_continuous, scale_manual, guide_legend, geom_abline

geom_label_repel

fct_reorder

Other Visualizations: alpha_diversity_plot(), correlation_data(), correlation_plots(), heat_tree_parameters(), heat_tree_plots(), ordination_plot(), plot_limits(), save_alpha_diversity_plots(), save_correlation_plots(), save_heat_tree_plots(), save_ordination_plots(), save_stacked_barplots(), stacked_barplot(), stacked_barplots(), top_coefficients_barplot()

Examples

## Not run: 
if(interactive()){
# This example uses data that are no longer available in the MicrobiomeR package,
# however, they can be easily generated with \code{\link{MicrobiomeR}{as_analyzed_format}}.
library(MicrobiomeR)
analyzed_silva <- as_MicrobiomeR_format(MicrobiomeR::raw_silva_2, "analyzed_format")
correlation_plot(analyzed_silva, primary_rank = "Class", secondary_rank = "Phylum")
 }

## End(Not run)

Description

This function allows the user to create a list of multiple correlation plots.

Usage

correlation_plots(obj, primary_ranks, secondary_ranks = TRUE, ...)

Arguments

Details

This function makes it easier to generate multiple correlation plots at once.

Value

A list object containing correlation plots. A pairwise comparison returns a nested list.

See Also

Other Visualizations: alpha_diversity_plot(), correlation_data(), correlation_plot(), heat_tree_parameters(), heat_tree_plots(), ordination_plot(), plot_limits(), save_alpha_diversity_plots(), save_correlation_plots(), save_heat_tree_plots(), save_ordination_plots(), save_stacked_barplots(), stacked_barplot(), stacked_barplots(), top_coefficients_barplot()

Examples

## Not run: 
if(interactive()){
# This example uses data that are no longer available in the MicrobiomeR package,
# however, they can be easily generated with \code{\link{MicrobiomeR}{as_analyzed_format}}.
library(MicrobiomeR)
analyzed_silva <- as_MicrobiomeR_format(MicrobiomeR::raw_silva_2, "analyzed_format")
corr_plots <- correlation_plots(analyzed_silva, primary_ranks = c("Phylum", "Class", "Order"),
                      secondary_ranks = c("Phylum", "Class", "Order", "Family", "Genus"))
# Show a plot
corr_plots$Class$Phylum
 }

## End(Not run)

Description

This function filters OTUs that have a variance higher than the specified CoV.

Usage

cov_filter(obj, coefficient_of_variation, validated = FALSE)

Arguments

Details

This function helps remove OTUs that have an unusually high variance using the coefficient of variation.

Value

Returns a taxmap object that contains otu_ids that have passed the above filter.

See Also

validate_MicrobiomeR_format, otu_id_filter

summarise_if

Other Advanced Metacoder Filters: agglomerate_taxmap(), otu_prevalence_filter(), otu_proportion_filter(), taxa_prevalence_filter()

Examples

## Not run: 
if(interactive()){
# Use the cov_filter towards the end of your analysis
library(MicrobiomeR)
library(metacoder)
library(taxa)

# Convert Phyloseq object to taxmap object
metacoder_obj <- as_MicrobiomeR_format(obj = phyloseq_obj, format = "raw_format")

# Remove Archaea from the taxmap object
metacoder_obj <- filter_taxa(
  obj = metacoder_obj,
  taxon_names == "Archaea",
  subtaxa = TRUE,
  invert = TRUE)

# Ambiguous Annotation Filter - Remove taxonomies with ambiguous names
metacoder_obj <- filter_ambiguous_taxa(metacoder_obj, subtaxa = TRUE)

# Low Sample Filter - Remove the low samples
metacoder_obj <- sample_id_filter(obj          = metacoder_obj,
                               .f_filter    = ~sum(.),
                               .f_condition = ~.>= 20, validated = TRUE)

# Master Threshold Filter - Add the otu_proportions table and then filter OTUs based on min %
metacoder_obj <- otu_proportion_filter(
    obj = metacoder_obj,
    otu_percentage = 0.00001
    )
# Taxa Prevalence Filter
# The default minimum abundance is 5 and the sample percentage is 0.5 (5%).
# Phylum
metacoder_obj <- taxa_prevalence_filter(
    obj = metacoder_obj,
    rank = "Phylum"
    )
# Class
metacoder_obj <- taxa_prevalence_filter(
    obj = metacoder_obj,
    rank = "Class",
    validated = TRUE
    )

# OTU prevalence filter
metacoder_obj <- otu_prevalence_filter(obj = metacoder_obj, validated = TRUE)

# Coefficient of Variation Filter - Filter OTUs based on the coefficient of variation
metacoder_obj <- cov_filter(obj = metacoder_obj,
                            coefficient_of_variation = 3,
                            validated = TRUE)
 }

## End(Not run)

Description

Create a phyloseq object using a biom file, phylogenetic tree file, and a metadata file. Alternatively, an .Rdata file can be used. For now this function takes output from Qiime. The data provided by this package was processed by the NIH's Nephele pipeline.

Usage

create_phyloseq(
  biom_file = NULL,
  tree_file = NULL,
  metadata_file = NULL,
  treatment_group = NULL,
  parse_func = NULL,
  rdata_file = NULL,
  save_rooted_tree = FALSE,
  recursive_save = FALSE
)

Arguments

Details

This function heavily relies on the phyloseq package to import data into R. It also requires you to use an absolute or relative path to your data files or for your data files to be in the working directory.

It's very important that you make sure that you have a sample_id and TreatmentGroup column in your metadata file.

Value

A phyloseq object, and a phylogenetic tree file if one does not already exist.

See Also

import_biom, phyloseq::sample_data(), phyloseq::phy_tree(), import_qiime_sample_data, merge_phyloseq

root, is.rooted, read.tree, write.tree

root_by_longest_edge

Other Data Importers: create_taxmap()

Examples

## Not run: 
if(interactive()){
library(MicrobiomeR)
# Get the datafiles from the package
biom_file <- system.file("extdata", "silva_OTU.biom", package = "MicrobiomeR")
tree_file <- system.file("extdata", "silva.tre", package = "MicrobiomeR")
md_file <- system.file("extdata", "nephele_metadata2.txt", package = "MicrobiomeR")
parse_func <- parse_parse_taxonomy_silva_128
}
# Create a phyloseq object from the data files.
phy_obj <- create_phyloseq(biom_file = biom_file,
                           tree_file = tree_file,
                           metadata_file = md_file,
                           parse_func = parse_func)

## End(Not run)

Description

Creates an image of a table from a dataframe.

Usage

create_pub_table(dataframe)

Arguments

Value

The text table as an object that can be saved to png/tiff/jpg/pdf.

Examples

## Not run: 
if(interactive()){
# This example uses data that are no longer available in the MicrobiomeR package,
# however, they can be easily generated with \code{\link{MicrobiomeR}{as_basic_format}}.
 library(MicrobiomeR)
 data <- MicrobiomeR::basic_silva$data$taxa_abundance
 create_pub_table(data)
 }

## End(Not run)

Description

A function that handles the conversion of objects to Taxmap (taxa::taxmap) objects.

Usage

create_taxmap(obj)

Arguments

Details

This function is used to convert data to metacoder/taxmap objects for microbiome analysis. This function is used at the beginning of every other function to support multiple types of input for the obj parameter in those functions. For phyloseq inputs, this function uses parse_phyloseq when possible and falls back to an internal parser for known upstream metacoder namespace breakage.

Value

The output generated is a taxa::taxmap object.

See Also

parse_phyloseq

fileutils

Other Data Importers: create_phyloseq()

Examples

## Not run: 
if(interactive()){
 library(MicrobiomeR)
 phy_obj <- MicrobiomeR::phyloseq_silva_2
 mc_obj <- create_taxmap(phy_obj)
 }

## End(Not run)

Description

Get a color palette with a specific number of colors.

Usage

get_color_palette(
  pal_func = viridis_magma_palette,
  color_no = 20,
  display = TRUE,
  rev = FALSE,
  ...
)

Arguments

Details

This function is meant to be a plugin style function for users to created palettes.

Value

Returns a color palette in the form of a character vector.

See Also

Other Color Palettes: combination_palette(), scico_palette(), viridis_magma_palette(), viridis_palette()

Examples

## Not run: 
if(interactive()){
# This example uses data that are no longer available in the MicrobiomeR package,
# however, they can be easily generated with \code{\link{MicrobiomeR}{as_analyzed_format}}.
 library(MicrobiomeR)
analyzed_silva <- as_MicrobiomeR_format(MicrobiomeR::raw_silva_2, "analyzed_format")
 data <- analyzed_silva$data$stats_tax_data$Phylum
 data_len <- length(unique(data))
 custom_pal <- get_color_palette(color_no=data_len)
 print(custom_pal)
 }

## End(Not run)

Description

This function gets the parameters used for the heat_tree_plots function.

Usage

heat_tree_parameters(obj, title, treatment_no, ...)

Arguments

Value

A list used with do.call and the metacoder::heat_tree function.

See Also

heat_tree

n_obs, taxon_names

list_modify

enquos, is_quosure, eval_tidy

Other Visualizations: alpha_diversity_plot(), correlation_data(), correlation_plot(), correlation_plots(), heat_tree_plots(), ordination_plot(), plot_limits(), save_alpha_diversity_plots(), save_correlation_plots(), save_heat_tree_plots(), save_ordination_plots(), save_stacked_barplots(), stacked_barplot(), stacked_barplots(), top_coefficients_barplot()

Description

A function for getting multiple heat_tree plots per rank.

Usage

heat_tree_plots(obj, rank_list = NULL, title = TRUE, seed = 1, ...)

Arguments

Details

By default, node size represents the number of retained OTUs assigned to each taxon or any of its descendants in the source otu_abundance table. Counts are pooled across samples after upstream filtering and do not represent read abundance.

Value

A list of heat_tree plots.

See Also

heat_tree

create_taxmap, validate_MicrobiomeR_format, heat_tree_parameters

filter_obs

crayon

theme, margin, labs

Other Visualizations: alpha_diversity_plot(), correlation_data(), correlation_plot(), correlation_plots(), heat_tree_parameters(), ordination_plot(), plot_limits(), save_alpha_diversity_plots(), save_correlation_plots(), save_heat_tree_plots(), save_ordination_plots(), save_stacked_barplots(), stacked_barplot(), stacked_barplots(), top_coefficients_barplot()

Examples

## Not run: 
if(interactive()){
# This example uses data that are no longer available in the MicrobiomeR package,
# however, they can be easily generated with \code{\link{MicrobiomeR}{as_analyzed_format}}.
library(MicrobiomeR)
analyzed_silva <- as_MicrobiomeR_format(MicrobiomeR::raw_silva_2, "analyzed_format")
h_trees <- heat_tree_plots(analyzed_silva, rank_list = c("Phylum", "Class"))
h_trees$Class
 }

## End(Not run)

Description

This function returns a logical based on weather or not the object is in the analyzed_format.

Usage

is_analyzed_format(obj)

Arguments

Details

The "analyzed_format" is Level 3. in the MicrobiomeR_Formats hierarchy.

Value

A logical (TRUE/FALSE).

See Also

Other Validation: is_basic_format(), is_phyloseq_format(), is_raw_format(), order_metacoder_data(), validate_MicrobiomeR_format(), which_format()

Examples

## Not run: 
if(interactive()){
library(MicrobiomeR)
if(is_analyzed_format(MicrobiomeR::raw_silva_2)){
    print("It's analyzed!")
} else {
    fmt <- which_format(MicrobiomeR::raw_silva_2)
    print("It's not analyzed!")
    print(sprintf("It's %s!", fmt))
}
 }

## End(Not run)

Description

This function returns a logical based on weather or not the object is in the basic_format.

Usage

is_basic_format(obj)

Arguments

Details

The "basic_format" is Level 2. in the MicrobiomeR_Formats hierarchy.

Value

A logical (TRUE/FALSE).

See Also

Other Validation: is_analyzed_format(), is_phyloseq_format(), is_raw_format(), order_metacoder_data(), validate_MicrobiomeR_format(), which_format()

Examples

## Not run: 
if(interactive()){
library(MicrobiomeR)
if(is_basic_format(MicrobiomeR::raw_silva_2)){
    print("It's basic!")
} else {
    fmt <- which_format(MicrobiomeR::raw_silva_2)
    print("It's not analyzed!")
    print(sprintf("It's %s!", fmt))
}
 }

## End(Not run)

Description

This function returns a logical based on weather or not the object is in the phyloseq_format.

Usage

is_phyloseq_format(obj)

Arguments

Details

The "phyloseq_format" is Level 0. in the MicrobiomeR_Formats hierarchy.

Value

A logical (TRUE/FALSE).

See Also

Other Validation: is_analyzed_format(), is_basic_format(), is_raw_format(), order_metacoder_data(), validate_MicrobiomeR_format(), which_format()

Examples

## Not run: 
if(interactive()){
library(MicrobiomeR)
if(is_analyzed_format(MicrobiomeR::raw_silva_2)){
    print("It's phyloseq format!")
} else {
    fmt <- which_format(MicrobiomeR::raw_silva_2)
    print("It's not phyloseq fomrat!")
    print(sprintf("It's %s!", fmt))
}
 }

## End(Not run)

Description

This function returns a logical based on weather or not the object is in the raw_format.

Usage

is_raw_format(obj)

Arguments

Details

The "raw_format" is Level 1. in the MicrobiomeR_Formats hierarchy.

Value

A logical (TRUE/FALSE).

See Also

Other Validation: is_analyzed_format(), is_basic_format(), is_phyloseq_format(), order_metacoder_data(), validate_MicrobiomeR_format(), which_format()

Examples

## Not run: 
if(interactive()){
library(MicrobiomeR)
if(is_raw_format(MicrobiomeR::raw_silva_2)){
    print("It's raw!")
} else {
    fmt <- which_format(MicrobiomeR::raw_silva_2)
    print("It's not analyzed!")
    print(sprintf("It's %s!", fmt))
}
 }

## End(Not run)

Description

Melt Taxmap

Usage

melt_taxmap(obj)

Arguments

See Also

Other Formatting: MicrobiomeR_Formats, as_MicrobiomeR_format(), as_analyzed_format(), as_basic_format(), as_custom_format(), as_phyloseq_format(), as_raw_format(), treatment_matrix()

Description

A comparison function for metacoder::compare_groups "func" parameter.

Usage

metacoder_comp_func_1(abund_1, abund_2)

Arguments

Details

This function is used by metacoder::compare_groups in order to compare every combination of treatment groups.

Value

A list of statistical results used to compare groups.

See Also

dip.test

Other Data Manipulators: convert_proportions(), transformer(), transposer(), vlookup()

Description

The formats are listed in order from lowest to highest level. To get a taxmap object into the higher formats they must be in one of the lower formats. It is also inherently important to understand the MicrobiomeR_Workflow, which describes some of the important dependencies used to create the MicrobiomeR formats listed below:

phyloseq_format

Level 0. The "phyloseq_format" is the format given to a taxmap object that has just been converted from a phyloseq object using metacoder via create_phyloseq. A taxmap object identified by MicrobiomeR to be in this format will contain "otu_table", "tax_data", "sample_data" and "phy_tree" data in the observation tables (metacoder_obj$data), which represents the matching phyloseq::otu_table, phyloseq::tax_table, phyloseq::sample_data, and phyloseq::phy_tree data in the original phyloseq object.

raw_format

Level 1. The "raw_format" is the format given to a taxmap object that has been processed with as_raw_format. It contains the "otu_abundance" and "otu_annotations" data in the observation tables, which are just name conversions of the otu_table and tax_data table from the "phyloseq_format".

basic_format

Level 2. The "basic_format" is the format given to a taxmap object that has been processed with as_basic_format. It contains the tables available in the "raw_format" along with "taxa_abundance", "otu_proportions", and "taxa_proportions" tables.

analyzed_format

Level 3. The "analyzed_format" is the format given to a taxmap object that has been processed with as_analyzed_format. It contains the tables available in the "basic_format" along with "statistical_data" and "stats_tax_data" tables.

See Also

Other Formatting: as_MicrobiomeR_format(), as_analyzed_format(), as_basic_format(), as_custom_format(), as_phyloseq_format(), as_raw_format(), melt_taxmap(), treatment_matrix()

Description

In order to understand the MicrobiomeR packages workflow it is necessary to understand a few other packages. Primarily phyloseq, taxa, and metacoder.

Data Import

Data is imported with the create_phyloseq function or something proprietary that creates a phyloseq object with an phyloseq::otu_table, a phyloseq::tax_table, a phyloseq::sample_data, and a phyloseq::phy_tree. The data is preferably not filtered with phyloseq at this stage.

Data Manipulation

Data is manipulated into a metacoder::taxmap object using the metacoder::parse_phyloseq function. Custom MicrobiomeR_Formats are used to validate the data for consistency. MicrobiomeR and metacoder filtering functions can be used to preprocess the data before visualization.

Visualizations

MicrobiomeR contains useful functions for creating visualizations such as heat_trees, correlations plots, stacked bar charts, violin plots, and PCA plots. These functions are also coupled with save functions that organize saved output by experiment.

Description

A function for changing the order of the observation data in a Taxmap object

Usage

order_metacoder_data(obj)

Arguments

Details

Changes the order of the observation tables in metacoder_object$data to otu_abundance, otu_annotations, otu_proportions, sample_data, phy_tree, taxa_abundance, taxa_proportions, statistical_data, and stats_tax_data respectively.

Value

A taxmap object with observation data in the proper order for downstream analysis.

See Also

Other Validation: is_analyzed_format(), is_basic_format(), is_phyloseq_format(), is_raw_format(), validate_MicrobiomeR_format(), which_format()

Description

This function allows for ordination (which helps us to distinguish beta diversity relationships) to be plotted as well as for the corresponding data to be returned.

Generate plots for a list of ordination methods and distances.

Usage

ordination_plot(
  obj,
  method = "PCoA",
  distance = "wunifrac",
  color = "TreatmentGroup",
  title = NULL,
  only_data = FALSE
)

ordination_plots(
  obj,
  methods = c("PCoA", "NMDS"),
  distances = c("wunifrac", "unifrac", "bray"),
  color = "TreatmentGroup",
  select_otu_table = "otu_proportions"
)

Arguments

Value

By default, it returns an ordination plot.

Returns a melted dataframe.

See Also

View save_ordination_plots to save your ordination plot or multiple ordination plots.

Other Visualizations: alpha_diversity_plot(), correlation_data(), correlation_plot(), correlation_plots(), heat_tree_parameters(), heat_tree_plots(), plot_limits(), save_alpha_diversity_plots(), save_correlation_plots(), save_heat_tree_plots(), save_ordination_plots(), save_stacked_barplots(), stacked_barplot(), stacked_barplots(), top_coefficients_barplot()

Other Visualizations: alpha_diversity_plot(), correlation_data(), correlation_plot(), correlation_plots(), heat_tree_parameters(), heat_tree_plots(), plot_limits(), save_alpha_diversity_plots(), save_correlation_plots(), save_heat_tree_plots(), save_ordination_plots(), save_stacked_barplots(), stacked_barplot(), stacked_barplots(), top_coefficients_barplot()

Examples

## Not run: 
if (interactive()) {
  # An example ordination plot
  library(MicrobiomeR)
  data <- analyzed_silva
  plot <- ordination_plot(obj = data)
  plot
}

## End(Not run)

Description

This function provides a flexible way to filter unwanted otu_ids from the taxmap object and from the observations of a MicrobiomeR formatted object.

Usage

otu_id_filter(
  obj,
  .f_transform = NULL,
  .f_filter = NULL,
  .f_condition = NULL,
  validated = FALSE,
  ...
)

Arguments

Details

Get the otu_ids to keep by using purr and the user supplied transform and filter + condition formulas. The purr package allows the use of anonymous functions as described in the link below:

https://jennybc.github.io/purrr-tutorial/ls03_map-function-syntax.html#anonymous_function,_formula

Value

Returns a taxmap object with otu_ids that pass the filters.

See Also

validate_MicrobiomeR_format, transposer, transformer

get_dataset, filter_taxa

select_all

map, keep

Other Basic Metacoder Filters: sample_id_filter(), taxon_id_filter()

Examples

## Not run: 
if(interactive()){
# Below is the code for the otu_prevelence_filter function.
# It's a good example of how to use the otu_id_filter function.
library(MicrobiomeR)
otu_proportion_filter <- function(obj, otu_percentage = 0.00005, validated = FALSE) {
    mo_clone <- obj$clone()
    mo_clone <- validate_MicrobiomeR_format(obj = mo_clone,
                                            valid_formats = c("raw_format", "basic_format"),
                                            force_format = TRUE,
                                            validated = validated,
                                            min_or_max = min)
    # Filter OTU ids
    mo_clone <- otu_id_filter(obj = mo_clone,
                              .f_transform = ~./sum(.),
                              .f_filter = ~mean(.),
                              .f_condition = ~.> otu_percentage)
return(mo_clone)
}
 }

## End(Not run)

Description

This function filters observations by their prevalence across samples.

Usage

otu_prevalence_filter(
  obj,
  minimum_abundance = 5,
  rel_sample_percentage = 0.5,
  validated = FALSE
)

Arguments

Details

The otu_prevalence_filter filters taxon_ids that do not appear more than a certain amount of times (minimum abundance) in a certain percentage of samples (rel_sample_percentage). The phyloseq workflow calls for a minimum abundance of 5 across %50 of the samples. This filtering method is considered unsupervised, because it solely relies on the data in this experiment (OTU ids).

Value

Returns a taxmap object that contains taxon_ids that have passed the above filter.

See Also

validate_MicrobiomeR_format

calc_prop_samples

filter

filter_taxa

Other Advanced Metacoder Filters: agglomerate_taxmap(), cov_filter(), otu_proportion_filter(), taxa_prevalence_filter()

Examples

## Not run: 
if(interactive()){
library(MicrobiomeR)
library(metacoder)
library(taxa)

# Convert Phyloseq object to taxmap object
metacoder_obj <- as_MicrobiomeR_format(obj = phyloseq_obj, format = "raw_format")

# Remove Archaea from the taxmap object
metacoder_obj <- filter_taxa(
  obj = metacoder_obj,
  taxon_names == "Archaea",
  subtaxa = TRUE,
  invert = TRUE)

# Ambiguous Annotation Filter - Remove taxonomies with ambiguous names
metacoder_obj <- filter_ambiguous_taxa(metacoder_obj, subtaxa = TRUE)

# Low Sample Filter - Remove the low samples
metacoder_obj <- sample_id_filter(obj          = metacoder_obj,
                               .f_filter    = ~sum(.),
                               .f_condition = ~.>= 20, validated = TRUE)

# Master Threshold Filter - Add the otu_proportions table and then filter OTUs based on min %
metacoder_obj <- otu_proportion_filter(
    obj = metacoder_obj,
    otu_percentage = 0.00001
    )

# OTU prevalence filter
metacoder_obj <- otu_prevalence_filter(obj = metacoder_obj, validated = TRUE)
 }

## End(Not run)

Description

This function filters OTU values from the observation data and the taxmap object based on a minimum proportional mean across samples per OTU.

Usage

otu_proportion_filter(obj, otu_percentage = 5e-05, validated = FALSE)

Arguments

Details

This type of filtering is used to remove OTUs that do not have a specified mean proportion. This function must be used conservatively, hence why the default otu_percentage is so low.

Value

Returns a taxmap object that contains otu_ids that have passed the above filter.

See Also

validate_MicrobiomeR_format,otu_id_filter

Other Advanced Metacoder Filters: agglomerate_taxmap(), cov_filter(), otu_prevalence_filter(), taxa_prevalence_filter()

Examples

## Not run: 
if(interactive()){
# Use the otu_proportions filter early on in your analysis
library(MicrobiomeR)
library(metacoder)
library(taxa)

# Convert Phyloseq object to taxmap object
metacoder_obj <- as_MicrobiomeR_format(obj = phyloseq_obj, format = "raw_format")

# Remove Archaea from the Taxmap object
metacoder_obj <- filter_taxa(
  obj = metacoder_obj,
  taxon_names == "Archaea",
  subtaxa = TRUE,
  invert = TRUE)

# Ambiguous Annotation Filter - Remove taxonomies with ambiguous names
metacoder_obj <- filter_ambiguous_taxa(metacoder_obj, subtaxa = TRUE)

# Low Sample Filter - Remove the low samples
metacoder_obj <- sample_id_filter(obj          = metacoder_obj,
                               .f_filter    = ~sum(.),
                               .f_condition = ~.>= 20, validated = TRUE)

# Master Threshold Filter - Add the otu_proportions table and then filter OTUs based on min %
metacoder_obj <- otu_proportion_filter(
    obj = metacoder_obj,
    otu_percentage = 0.00001
    )
 }

## End(Not run)

Description

A function for generating a consistent file system for data files and visualizations. If none of the parameters are set the full path generated by this function will be root_path/output

Usage

output_dir(
  start_path = NULL,
  experiment = NULL,
  plot_type = NULL,
  end_path = NULL,
  root_path = NULL,
  custom_path = NULL,
  overwrite = FALSE,
  mkdir = TRUE
)

Arguments

Details

This function is incredibly useful on it's own but also for various other plotting/saving functions within the package. It helps keep data organized using a standard workflow.

Value

Creates a directory for output and returns the path as a string.

See Also

glue

case

Examples

## Not run: 
if(interactive()){
 # Get the path to an output directory without creating
 library(MicrobiomeR)
 output_dir <- output_dir(start_path="output", experiment="microbiome-proj", mkdir=FALSE)
 print(output_dir)

 # Create a folder for your plot types
 output_dir <- output_dir(plot_type="scatter-plots")
 print(output_dir)

 }

## End(Not run)

Description

These are provided as both example and default functions for parsing a character vector of taxonomic rank information for a single taxa. As default functions, these are intended for cases where the data adheres to the naming convention used by greengenes the naming convention used by greengenes and silva. (http://greengenes.lbl.gov/cgi-bin/nph-index.cgi) or where the convention is unknown, respectively. To work, these functions – and any similar custom function you may want to create and use – must take as input a single character vector of taxonomic ranks for a single OTU, and return a named character vector that has been modified appropriately (according to known naming conventions, desired length limits, etc. The length (number of elements) of the output named vector does not need to be equal to the input, which is useful for the cases where the source data files have extra meaningless elements that should probably be removed, like the ubiquitous “Root” element often found in greengenes/QIIME taxonomy labels. In the case of parse_taxonomy_default, no naming convention is assumed and so dummy rank names are added to the vector. More usefully if your taxonomy data is based on greengenes, the parse_taxonomy_greengenes function clips the first 3 characters that identify the rank, and uses these to name the corresponding element according to the appropriate taxonomic rank name used by greengenes (e.g. "p__" at the beginning of an element means that element is the name of the phylum to which this OTU belongs). If you taxonomy data is based on SILVA, the parse_taxonomy_silva_128 function clips the first 5 characters that identify rank, and uses these to name the corresponding element according to the appropriate taxonomic rank name used by SILVA (e.g. "D_1__" at the beginning of an element means that element is the name of the phylum to which this OTU belongs. Alternatively you can create your own function to parse this data. Most importantly, the expectations for these functions described above make them compatible to use during data import, specifically the import_biom function, but it is a flexible structure that will be implemented soon for all phyloseq import functions that deal with taxonomy (e.g. import_qiime).

Usage

parse_taxonomy_silva_128(char.vec)

Arguments

Details

This function is currently under PR review by phyloseq in a well supported pull request: https://github.com/joey711/phyloseq/pull/854. If you use this function, then please comment on the GitHub PR to encourage merging this feature.

Value

A character vector in which each element is a different taxonomic rank of the same OTU, and each element name is the name of the rank level. For example, an element might be "Firmicutes" and named "phylum". These parsed, named versions of the taxonomic vector should reflect embedded information, naming conventions, desired length limits, etc; or in the case of parse_taxonomy_default, not modified at all and given dummy rank names to each element.

See Also

parse_taxonomy_default

parse_taxonomy_greengenes

parse_taxonomy_qiime

import_biom

import_qiime

Examples

## Not run: 

 > taxvec1 = c("Root", "k__Bacteria", "p__Firmicutes", "c__Bacilli", "o__Bacillales",
 "f__Staphylococcaceae")
 > parse_taxonomy_default(taxvec1)
 > parse_taxonomy_greengenes(taxvec1)
 > taxvec2 = c("Root;k__Bacteria;p__Firmicutes;c__Bacilli;o__Bacillales;f__Staphylococcaceae")
 > parse_taxonomy_qiime(taxvec2)
 > taxvec3 = c("D_0__Bacteria", "D_1__Firmicutes", "D_2__Bacilli", "D_3__Staphylococcaceae")
 > parse_taxonomy_silva_128(taxvec3)
 
## End(Not run)

Description

Evaluate whether the group(s) has a significant effect on overall gut microbiota composition.

Usage

permanova(obj, distance_method = "bray", group = "TreatmentGroup", seed = 1)

Arguments

Value

Returns a list which includes permanova, anova, coefficients, and top coefficients.

See Also

View top_coefficients_barplot to plot the top_coefficients returned from this function. See adonis2 and betadisper to understand more about how the permanova data was generated

Examples

## Not run: 
if (interactive()) {
  library(MicrobiomeR)
  data <- analyzed_silva
  p <- permanova(obj = data, group = "TreatmentGroup")
  p$permanova
}

## End(Not run)

Description

Data from the Nephele-16S-Qiime-Silva microbiome pipeline in the form of a phyloseq object.

Usage

data(phyloseq_silva_2)

Format

An object of class "phyloseq"

Source

Nephele-16S Qiime Pipeline

References

Zhang et al. (2019) (PubMed)

Description

Data from the Nephele-16S-Qiime-Silva microbiome pipeline in the form of a phyloseq object.

Usage

data(phyloseq_silva_3)

Format

An object of class "phyloseq"

Source

Nephele-16S Qiime Pipeline

References

Zhang et al. (2019) (PubMed)

Description

Pick an outgroup for rooting a phylogenetic tree.

Usage

pick_new_outgroup(unrooted_tree)

Arguments

Details

This function preprocess a phylogenetic tree for rooting by the longest edge. Please see this issue for more details https://github.com/joey711/phyloseq/issues/597.

Value

A new tree with the longest branch as the outgroup.

See Also

data.table

summary.phylo

Other Phylogenetic Tree Manipulators: root_by_longest_edge(), root_phyloseq_tree()

Description

Get the limits of the plot using data values.

Usage

plot_limits(x, y)

Arguments

Value

A vector that supplies the overarching x and y limits.

See Also

Other Visualizations: alpha_diversity_plot(), correlation_data(), correlation_plot(), correlation_plots(), heat_tree_parameters(), heat_tree_plots(), ordination_plot(), save_alpha_diversity_plots(), save_correlation_plots(), save_heat_tree_plots(), save_ordination_plots(), save_stacked_barplots(), stacked_barplot(), stacked_barplots(), top_coefficients_barplot()

Description

Data from the Nephele-16S-Qiime-Silva microbiome pipeline in the form of a taxmap object. This taxmap object has been formatted into the MicrobiomeR "raw_format".

Usage

data(raw_silva_2)

Format

An object of class "Taxmap" with customized observation tables:

otu_abundance

An abundance table with otu_id, taxon_id, and Sample_* columns.

otu_annotations

A taxonomy annotations table with otu_id, taxon_id, and multiple rank columns.

sample_data

A metadata table with SampleID, BarcodeSequence, LinkerPrimerSequence, ForwardFastqFile, ReverseFastqFile, TreatmentGroup, SampleName, Description

phy_tree

A phylogenetic tree.

Source

Nephele-16S Qiime Pipeline

References

Zhang et al. (2019) (PubMed)

Description

Data from the Nephele-16S-Qiime-Silva microbiome pipeline in the form of a taxmap object. This taxmap object has been formatted into the MicrobiomeR "raw_format".

Usage

data(raw_silva_3)

Format

An object of class "Taxmap" with customized observation tables:

otu_abundance

An abundance table with otu_id, taxon_id, and Sample_* columns.

otu_annotations

A taxonomy annotations table with otu_id, taxon_id, and multiple rank columns.

sample_data

A metadata table with SampleID, BarcodeSequence, LinkerPrimerSequence, ForwardFastqFile, ReverseFastqFile, TreatmentGroup, SampleName, Description

phy_tree

A phylogenetic tree.

Source

Nephele-16S Qiime Pipeline

References

Zhang et al. (2019) (PubMed)

Description

This function roots a phylogenetic tree object by it's longest edge.

Usage

root_by_longest_edge(unrooted_tree)

Arguments

Details

Please see this issue for more details https://github.com/joey711/phyloseq/issues/597.

Value

A rooted phlogenetic tree object.

See Also

root pick_new_outgroup

Other Phylogenetic Tree Manipulators: pick_new_outgroup(), root_phyloseq_tree()

Description

A function for rooting and saving a phylogenetic tree.

Usage

root_phyloseq_tree(
  phyloseq_object,
  tree_path,
  save_rooted_tree,
  recursive = TRUE
)

Arguments

Details

This function is a helper function to get a proper phyloseq object for downstream analysis. Some analyses require a rooted tree. The function saves the rooted tree in the phyloseq object. It can also save the tree as a file if desired.

Value

Returns a phyloseq object with a rooted tree.

See Also

root, read.tree, write.tree

phy_tree

fileutils

Other Phylogenetic Tree Manipulators: pick_new_outgroup(), root_by_longest_edge()

Examples

## Not run: 
if(interactive()){
 #EXAMPLE1
 }

## End(Not run)

Description

This function provides a flexible way to filter unwanted samples from the "otu_abundance" and "sample_data" observations of a MicrobiomeR formatted object.

Usage

sample_id_filter(
  obj,
  .f_transform = NULL,
  .f_filter = NULL,
  .f_condition = NULL,
  validated = FALSE,
  ...
)

Arguments

Details

Get the samples to keep by using purr and the user supplied transform and filter + condition formulas. The purr package allows the use of anonymous functions as described in the link below:

https://jennybc.github.io/purrr-tutorial/ls03_map-function-syntax.html#anonymous_function,_formula

Value

Returns a Taxmap object with samples that pass the filters.

See Also

validate_MicrobiomeR_format, transformer

get_dataset

select_all, select, filter

map, keep

Other Basic Metacoder Filters: otu_id_filter(), taxon_id_filter()

Examples

## Not run: 
if(interactive()){
# Use the sample_id_filter early on in your analysis
library(MicrobiomeR)
library(metacoder)
library(taxa)
# Convert Phyloseq object to Taxmap object
metacoder_obj <- as_MicrobiomeR_format(obj = phyloseq_silva_2, format = "raw_format")

# Remove Archaea from the Taxmap object
metacoder_obj <- filter_taxa(
  obj = metacoder_obj,
  taxon_names == "Archaea",
  subtaxa = TRUE,
  invert = TRUE)

# Ambiguous Annotation Filter - Remove taxonomies with ambiguous names
metacoder_obj <- filter_ambiguous_taxa(metacoder_obj, subtaxa = TRUE)

# Low Sample Filter - Remove the low samples
metacoder_obj <- sample_id_filter(obj          = metacoder_obj,
                               .f_filter    = ~sum(.),
                               .f_condition = ~.>= 20, validated = TRUE)
 }

## End(Not run)

Description

This function saves alpha diversity plots stored in a list object to an output folder.

Usage

save_alpha_diversity_plots(
  alpha_div_plots,
  format = "tiff",
  start_path = "output",
  ...
)

Arguments

Details

This function creates an appropriate output directory, where it saves publication ready plots.

Value

An output directory that contains alpha diversity plots.

See Also

output_dir

ggsave

Other Visualizations: alpha_diversity_plot(), correlation_data(), correlation_plot(), correlation_plots(), heat_tree_parameters(), heat_tree_plots(), ordination_plot(), plot_limits(), save_correlation_plots(), save_heat_tree_plots(), save_ordination_plots(), save_stacked_barplots(), stacked_barplot(), stacked_barplots(), top_coefficients_barplot()

Examples

## Not run: 
if (interactive()) {
  # This example uses data that are no longer available in the MicrobiomeR package,
  # however, they can be easily generated with \code{\link{MicrobiomeR}{as_analyzed_format}}.
  library(MicrobiomeR)
  analyzed_silva <- as_MicrobiomeR_format(MicrobiomeR::raw_silva_2, "analyzed_format")
  alpha_div_plots <- alpha_diversity_plots(analyzed_silva)
  # Save to \emph{./output/alpha_diversity} folder.
  save_alpha_diversity_plot(alpha_div_plots)
}

## End(Not run)

Description

This function saves correlation plots stored in a listlike object to an output folder.

Usage

save_correlation_plots(corr, format = "tiff", start_path = "output", ...)

Arguments

Details

This function creates an appropriate output directory, where it saves publication ready plots.

Value

An output directory that contains correlation plots.

See Also

output_dir

ggsave

Other Visualizations: alpha_diversity_plot(), correlation_data(), correlation_plot(), correlation_plots(), heat_tree_parameters(), heat_tree_plots(), ordination_plot(), plot_limits(), save_alpha_diversity_plots(), save_heat_tree_plots(), save_ordination_plots(), save_stacked_barplots(), stacked_barplot(), stacked_barplots(), top_coefficients_barplot()

Examples

## Not run: 
if(interactive()){
# This example uses data that are no longer available in the MicrobiomeR package,
# however, they can be easily generated with \code{\link{MicrobiomeR}{as_analyzed_silva}}.
library(MicrobiomeR)
analyzed_silva <- as_MicrobiomeR_format(MicrobiomeR::raw_silva_2, "analyzed_format")
corr_plot <- correlation_plot(analyzed_silva, primary_rank = "Class", secondary_rank = "Phylum")
# Save to \emph{./output/corr_plot} folder.
save_correlation_plots(corr_plot)
 }

## End(Not run)

Description

This function saves heat tree plots stored in a list object to an output folder.

Usage

save_heat_tree_plots(htrees, format = "tiff", start_path = "output", ...)

Arguments

Details

This function creates an appropriate output directory, where it saves publication ready plots.

Value

An output directory that contains heat tree plots.

See Also

output_dir

ggsave

Other Visualizations: alpha_diversity_plot(), correlation_data(), correlation_plot(), correlation_plots(), heat_tree_parameters(), heat_tree_plots(), ordination_plot(), plot_limits(), save_alpha_diversity_plots(), save_correlation_plots(), save_ordination_plots(), save_stacked_barplots(), stacked_barplot(), stacked_barplots(), top_coefficients_barplot()

Examples

## Not run: 
if(interactive()){
# This example uses data that are no longer available in the MicrobiomeR package,
# however, they can be easily generated with \code{\link{MicrobiomeR}{as_analyzed_format}}.
library(MicrobiomeR)
analyzed_silva <- as_MicrobiomeR_format(MicrobiomeR::raw_silva_2, "analyzed_format")
h_trees <- heat_tree_plots(analyzed_silva, rank_list = c("Phylum", "Class"))
# Save to \emph{./output/heat_trees} folder.
save_heat_tree_plots(h_trees)
 }

## End(Not run)

Description

This function saves ordination plots stored in a listlike object to an output folder.

Usage

save_ordination_plots(ord_plots, format = "tiff", start_path = "output", ...)

Arguments

Details

This function creates an appropriate output directory, where it saves publication ready plots.

Value

An output directory that contains ordination plots.

See Also

output_dir

ggsave

Other Visualizations: alpha_diversity_plot(), correlation_data(), correlation_plot(), correlation_plots(), heat_tree_parameters(), heat_tree_plots(), ordination_plot(), plot_limits(), save_alpha_diversity_plots(), save_correlation_plots(), save_heat_tree_plots(), save_stacked_barplots(), stacked_barplot(), stacked_barplots(), top_coefficients_barplot()

Examples

## Not run: 
if (interactive()) {
}

## End(Not run)

Description

This function saves stacked barplot stored in a list object to an output folder.

Usage

save_stacked_barplots(sb_plots, format = "tiff", start_path = "output", ...)

Arguments

Details

This function creates an appropriate output directory, where it saves publication ready plots.

Value

An output directory that contains stacked barplot.

See Also

output_dir

ggsave

Other Visualizations: alpha_diversity_plot(), correlation_data(), correlation_plot(), correlation_plots(), heat_tree_parameters(), heat_tree_plots(), ordination_plot(), plot_limits(), save_alpha_diversity_plots(), save_correlation_plots(), save_heat_tree_plots(), save_ordination_plots(), stacked_barplot(), stacked_barplots(), top_coefficients_barplot()

Examples

## Not run: 
if (interactive()) {
  # This example uses data that are no longer available in the MicrobiomeR package,
  # however, they can be easily generated with \code{\link{MicrobiomeR}{as_analyzed_format}}.
  library(MicrobiomeR)
  analyzed_silva <- as_MicrobiomeR_format(MicrobiomeR::raw_silva_2, "analyzed_format")
  alpha_div_plots <- alpha_diversity_plots(analyzed_silva)
  # Save to \emph{./output/alpha_diversity} folder.
  save_alpha_diversity_plot(salpha_div_plots)
}

## End(Not run)

Description

Save a top coefficients barplot.

Usage

save_top_coefficients_barplot(
  plot,
  filename,
  format = "tiff",
  start_path = "output",
  ...
)

Arguments

Examples

## Not run: 
if (interactive()) {
  library(MicrobiomeR)
  save_top_coefficients_barplot(plot = plot, filename = "stacked_bar_phylum")
}

## End(Not run)

Description

A function that returns a color palette function based off of the scico package.

Usage

scico_palette(
  scico_palette = "batlow",
  scico_number = 800,
  scico_range = 0:scico_number
)

Arguments

Details

The purpose of this function is to provide an interpolated scico palette for using with the get_color_palette function.

Value

The output of this function is another function (grDevoces::colorRampPalette), which takes a number to generate an interpolated color palette as a character vector.

See Also

colorRamp scico

Other Color Palettes: combination_palette(), get_color_palette(), viridis_magma_palette(), viridis_palette()

Examples

## Not run: 
if(interactive()){
library(MicrobiomeR)
basic_pal <- scico_palette(scico_palette="hawaii")(25)
better_pal <- get_color_palette(pal_func = scico_palette, color_no = 25)
 }

## End(Not run)

Description

Create a stacked barplot to show relative abundance of taxa. convert_proportions converts the dataframe abundance values to percent 100 and returns a transformed dataframe. melt_metacoder melts the metacoder or phyloseq tables into a dataframe and returns a melted dataframe. stacked_barplots creates a stacked barplots for multiple taxonomic levels and returns a list of stacked barplots.

Usage

stacked_barplot(
  obj,
  tax_level = "Phylum",
  fill = "Phylum",
  xlabel = "Samples",
  faceted = FALSE,
  title = NULL,
  palette_values = NULL
)

Arguments

Value

Returns a stacked barplot.

See Also

Other Visualizations: alpha_diversity_plot(), correlation_data(), correlation_plot(), correlation_plots(), heat_tree_parameters(), heat_tree_plots(), ordination_plot(), plot_limits(), save_alpha_diversity_plots(), save_correlation_plots(), save_heat_tree_plots(), save_ordination_plots(), save_stacked_barplots(), stacked_barplots(), top_coefficients_barplot()

Examples

## Not run: 
if (interactive()) {
  # An example stacked bar plot
  library(MicrobiomeR)
  data <- analyzed_silva
  palette <- get_color_palette(color_no = 12)
  plot <- stacked_barplot(obj = data, palette_values = palette)
  plot
}

## End(Not run)

Description

Stacked Barplots

Usage

stacked_barplots(obj, tax_levels = c("Phylum", "Class", "Order"))

Arguments

See Also

Other Visualizations: alpha_diversity_plot(), correlation_data(), correlation_plot(), correlation_plots(), heat_tree_parameters(), heat_tree_plots(), ordination_plot(), plot_limits(), save_alpha_diversity_plots(), save_correlation_plots(), save_heat_tree_plots(), save_ordination_plots(), save_stacked_barplots(), stacked_barplot(), top_coefficients_barplot()

Description

This function filters observations at a specific rank by their prevalence across samples.

Usage

taxa_prevalence_filter(
  obj,
  rank,
  minimum_abundance = 5,
  rel_sample_percentage = 0.5,
  validated = FALSE
)

Arguments

Details

The taxa_prevalence_filter filters taxon_ids that do not appear more than a certain amount of times (minimum abundance) in a certain percentage of samples (rel_sample_percentage) at the specified agglomerated rank (rank). The phyloseq workflow calls for a minimum abundance of 5 across %50 of the samples. This method is considered supervised, because the filtering is done based on taxonomic annotation (taxon_ids), which is assigned based on a reference database (SILVA or GreenGenes).

Value

Returns a taxmap object that contains taxon_ids that have passed the above filter.

See Also

validate_MicrobiomeR_format

calc_prop_samples

filter

filter_taxa

Other Advanced Metacoder Filters: agglomerate_taxmap(), cov_filter(), otu_prevalence_filter(), otu_proportion_filter()

Examples

## Not run: 
if(interactive()){
library(MicrobiomeR)
library(metacoder)
library(taxa)

# Convert Phyloseq object to taxmap object
metacoder_obj <- as_MicrobiomeR_format(obj = phyloseq_obj, format = "raw_format")

# Remove Archaea from the taxmap object
metacoder_obj <- filter_taxa(
  obj = metacoder_obj,
  taxon_names == "Archaea",
  subtaxa = TRUE,
  invert = TRUE)

# Ambiguous Annotation Filter - Remove taxonomies with ambiguous names
metacoder_obj <- filter_ambiguous_taxa(metacoder_obj, subtaxa = TRUE)

# Low Sample Filter - Remove the low samples
metacoder_obj <- sample_id_filter(obj          = metacoder_obj,
                               .f_filter    = ~sum(.),
                               .f_condition = ~.>= 20, validated = TRUE)

# Master Threshold Filter - Add the otu_proportions table and then filter OTUs based on min %
metacoder_obj <- otu_proportion_filter(
    obj = metacoder_obj,
    otu_percentage = 0.00001
    )
# Taxa Prevalence Filter
# The default minimum abundance is 5 and the sample percentage is 0.5 (5%).
# Phylum
metacoder_obj <- taxa_prevalence_filter(
    obj = metacoder_obj,
    rank = "Phylum"
    )
# Class
metacoder_obj <- taxa_prevalence_filter(
    obj = metacoder_obj,
    rank = "Class",
    validated = TRUE
    )
 }

## End(Not run)

Description

This function provides a flexible way to filter unwanted taxon_ids from the taxmap object and from the observations of a MicrobiomeR formatted object.

Usage

taxon_id_filter(
  obj,
  .f_transform = NULL,
  .f_filter = NULL,
  .f_condition = NULL,
  validated = FALSE,
  ...
)

Arguments

Details

Get the taxon_ids to keep by using purr and the user supplied transform and filter + condition formulas. The purr package allows the use of anonymous functions as described in the link below:

https://jennybc.github.io/purrr-tutorial/ls03_map-function-syntax.html#anonymous_function,_formula

Value

Returns a Taxmap object with taxon_ids that pass the filters.

See Also

validate_MicrobiomeR_format, transposer, transformer

get_dataset, filter_taxa

select_all

map, keep

Other Basic Metacoder Filters: otu_id_filter(), sample_id_filter()

Examples

## Not run: 
if(interactive()){
library(MicrobiomeR)
library(metacoder)
library(taxa)
# Convert Phyloseq object to Taxmap object
metacoder_obj <- as_MicrobiomeR_format(obj = phyloseq_obj, format = "raw_format")

# Remove Archaea from the Taxmap object
metacoder_obj <- filter_taxa(
  obj = metacoder_obj,
  taxon_names == "Archaea",
  subtaxa = TRUE,
  invert = TRUE)

# Ambiguous Annotation Filter - Remove taxonomies with ambiguous names
metacoder_obj <- filter_ambiguous_taxa(metacoder_obj, subtaxa = TRUE)

# Low taxa filter - Remove the low taxon_ids
metacoder_obj <- taxon_id_filter(obj        = metacoder_obj,
                               .f_filter    = ~sum(.),
                               .f_condition = ~.>= 2000, validated = TRUE)
 }

## End(Not run)

Description

A barplot to visualize which taxa have had the greatest impact on the overall gut composition.

Usage

top_coefficients_barplot(top_coefficients, title = NULL)

Arguments

Value

Returns a barplot of the top coefficients

See Also

View permanova to understand how to generate top_coefficents

Other Visualizations: alpha_diversity_plot(), correlation_data(), correlation_plot(), correlation_plots(), heat_tree_parameters(), heat_tree_plots(), ordination_plot(), plot_limits(), save_alpha_diversity_plots(), save_correlation_plots(), save_heat_tree_plots(), save_ordination_plots(), save_stacked_barplots(), stacked_barplot(), stacked_barplots()

Examples

## Not run: 
if (interactive()) {
  library(MicrobiomeR)
  data <- analyzed_silva
  p <- permanova(obj = data)
  top_coefficients_barplot(p$top_coefficients)
}

## End(Not run)

Description

This function transforms a table by rows or by columns.

Usage

transformer(
  .data,
  func,
  by = "column",
  ids = NULL,
  header_name = NULL,
  preserved_categories = TRUE,
  separated_categories = NULL,
  ...
)

Arguments

Details

This function transforms the supplied data using the func parameter, which is used in the purrr package. The purr package allows the use of anonymous functions as described in the link below:

https://jennybc.github.io/purrr-tutorial/ls03_map-function-syntax.html#anonymous_function,_formula

Value

A tibble that has been transformed.

See Also

transposer select_all, select modify

Other Data Manipulators: convert_proportions(), metacoder_comp_func_1(), transposer(), vlookup()

Examples

## Not run: 
if(interactive()){
# This example uses data that are no longer available in the MicrobiomeR package,
# however, they can be easily generated with \code{\link{MicrobiomeR}{as_basic_format}}.
 library(MicrobiomeR)
 basic_silva <- as_MicrobiomeR_format(MicrobiomeR::raw_silva_2, "basic_format")
 data <- basic_silva$data$taxa_abundance
 # Get proportions using the anonymous functions
 tax_props <- data %>% transformer(~./sum(.))
 # Get proportions using explicit functions
 alt_tax_props <- data %>% transformer(function(x)x/sum(x))
 }

## End(Not run)

Description

This function transposes tables containing numeric and categorical data using the tidyr package.

Usage

transposer(
  .data,
  ids = NULL,
  header_name,
  preserved_categories = TRUE,
  separated_categories = NULL
)

Arguments

Details

Transposing can help with preforming operations on the rows of your tibbles.

Value

A transposed data table as a tibble.

See Also

is_tibble

select_all, select, reexports, sym, one_of

gather, unite, spread, separate

str_detect, str_count

Other Data Manipulators: convert_proportions(), metacoder_comp_func_1(), transformer(), vlookup()

Examples

## Not run: 
if(interactive()){
# This example uses data that are no longer available in the MicrobiomeR package,
# however, they can be easily generated with \code{\link{MicrobiomeR}{as_basic_format}}.
 library(MicrobiomeR)
 basic_silva <- as_MicrobiomeR_format(MicrobiomeR::raw_silva_2, "basic_format")
 data <- basic_silva$data$taxa_abundance
 trans_data <- data %>%
   transposer(ids = "taxon_id", header_name = "samples")
 retrans_data <- trans_data %>%
   transposer(ids="samples", header_name="taxon_id")
 }

## End(Not run)

Description

A function that returns a matrix with used for comparing treatment data.

Usage

treatment_matrix(obj)

Arguments

Details

Use this when you want to do pairwise comparisons.

Value

A matrix with each column representing a comparison to be made.

See Also

Other Formatting: MicrobiomeR_Formats, as_MicrobiomeR_format(), as_analyzed_format(), as_basic_format(), as_custom_format(), as_phyloseq_format(), as_raw_format(), melt_taxmap()

Examples

## Not run: 
if(interactive()){
 #EXAMPLE1
 }

## End(Not run)

Description

This function validates that Taxmap objects are in a valid format MicrobiomeR format.

Usage

validate_MicrobiomeR_format(
  obj,
  validated = FALSE,
  valid_formats,
  force_format = FALSE,
  min_or_max = base::max,
  ...
)

Arguments

Details

This function can provide a way to check if a taxmap object has undergone a MicrobiomeR Style Workflow.

Value

If the object is validated, a Taxmap object.

See Also

which_format, as_MicrobiomeR_format

Other Validation: is_analyzed_format(), is_basic_format(), is_phyloseq_format(), is_raw_format(), order_metacoder_data(), which_format()

Description

A function that returns a color palette function based off of the viridis package.

Usage

viridis_magma_palette(
  viridis_number = 800,
  viridis_range = 300:viridis_number,
  viridis_rev = TRUE,
  magma_number = 500,
  magma_range = 50:magma_number,
  magma_rev = FALSE,
  ...
)

Arguments

Details

The primary purpose of this function is to return a palette-function for generating virdis style color palettes. By taking the viridis::viridis() and the viridis::magma() colors, and manipulating them, this function can help create a unique set of colors that you can distinguish on a busy plot. The hopes of this function is to help improve plots that use more than 20 colors. Use the provided example to view the color palette.

Value

The output of this function is another function (grDevoces::colorRampPalette), which takes a number to generate an interpolated color palette as a character vector.

See Also

combination_palette

viridis, magma

colorRamp

Other Color Palettes: combination_palette(), get_color_palette(), scico_palette(), viridis_palette()

Examples

## Not run: 
if(interactive()){
 # Use the default values
 > pal_func <- viridis_palette()

 # Get a palette with 20 colors
 > pal <- pal_func(20)

 # Make a pie plot of the colros.
 > pie(rep(1, length(pal)), col=pal)
 }

## End(Not run)

Description

A function that returns a color palette function based off of the viridis package.

Usage

viridis_palette(
  viridis_palette = "viridis",
  viridis_number = 800,
  viridis_range = 0:viridis_number
)

Arguments

Details

The purpose of this function is to provide an interpolated viridis palette for using with the get_color_palette function.

Value

The output of this function is another function (grDevoces::colorRampPalette), which takes a number to generate an interpolated color palette as a character vector.

See Also

colorRamp

viridis

Other Color Palettes: combination_palette(), get_color_palette(), scico_palette(), viridis_magma_palette()

Examples

## Not run: 
if(interactive()){
library(MicrobiomeR)
basic_pal <- viridis::viridis(n=25, option="magma")
better_pal <- get_color_palette(pal_func = viridis_palette, color_no = 25)
 }

## End(Not run)

Description

A function that mimics excels vlookup, but for R's dataframe.

Usage

vlookup(lookup_vector, df, match_var, return_var)

Arguments

Details

A function that works like the VLOOKUP function in excel. This function was borrowed from https://www.r-bloggers.com/an-r-vlookup-not-so-silly-idea/.

Value

A vector that contains the items of interest.

See Also

Other Data Manipulators: convert_proportions(), metacoder_comp_func_1(), transformer(), transposer()

Examples

## Not run: 
if(interactive()){
 #EXAMPLE1
 }

## End(Not run)

Description

A function for looking at a Taxmap object and returning the identified MicrobiomeR format.

Usage

which_format(obj)

Arguments

Details

This function is used to get basic information about the format of the taxmap object that is supplied by the user.

Value

If the format is verified it returns a character string denoting the identified format.

See Also

Other Validation: is_analyzed_format(), is_basic_format(), is_phyloseq_format(), is_raw_format(), order_metacoder_data(), validate_MicrobiomeR_format()

Examples

## Not run: 
if(interactive()){
library(MicrobiomeR)
fmt <- which fmt(MicrobiomeR::raw_silva_2)
print(fmt)
 }

## End(Not run)