| Title: | Differential Abundance Analysis by Consensus |
|---|---|
| Description: | Differential abundance testing in microbiome data challenges both parametric and non-parametric statistical methods, due to its sparsity, high variability and compositional nature. Microbiome-specific statistical methods often assume classical distribution models or take into account compositional specifics. These produce results that range within the specificity vs sensitivity space in such a way that type I and type II error that are difficult to ascertain in real microbiome data when a single method is used. Recently, a consensus approach based on multiple differential abundance (DA) methods was recently suggested in order to increase robustness. With dar, you can use dplyr-like pipeable sequences of DA methods and then apply different consensus strategies. In this way we can obtain more reliable results in a fast, consistent and reproducible way. |
| Authors: | Francesc Catala-Moll [aut, cre]
|
| Maintainer: | Francesc Catala-Moll <[email protected]> |
| License: | MIT + file LICENSE |
| Version: | 1.1.0 |
| Built: | 2024-06-30 06:51:06 UTC |
| Source: | https://github.com/bioc/dar |
Abundance boxplot
abundance_plt( rec, taxa_ids = NULL, type = "boxplot", transform = "compositional", scale = 1, top_n = 20 ) ## S4 method for signature 'Recipe' abundance_plt( rec, taxa_ids = NULL, type = "boxplot", transform = "compositional", scale = 1, top_n = 20 ) ## S4 method for signature 'PrepRecipe' abundance_plt( rec, taxa_ids = NULL, type = "boxplot", transform = "compositional", scale = 1, top_n = 20 )abundance_plt( rec, taxa_ids = NULL, type = "boxplot", transform = "compositional", scale = 1, top_n = 20 ) ## S4 method for signature 'Recipe' abundance_plt( rec, taxa_ids = NULL, type = "boxplot", transform = "compositional", scale = 1, top_n = 20 ) ## S4 method for signature 'PrepRecipe' abundance_plt( rec, taxa_ids = NULL, type = "boxplot", transform = "compositional", scale = 1, top_n = 20 )
rec |
A Recipe or Recipe step. |
taxa_ids |
Character vector with taxa_ids to plot. If taxa_ids is NULL the significant characteristics present in all of the executed methods will be plotted. |
type |
Character vector indicating the type of the result. Options: c("boxoplot", "heatmap"). |
transform |
Transformation to apply. The options include: 'compositional' (ie relative abundance), 'Z', 'log10', 'log10p', 'hellinger', 'identity', 'clr', 'alr', or any method from the vegan::decostand function. If the value is NULL, no normalization is applied and works with the raw counts. |
scale |
Scaling constant for the abundance values when transform = "scale". |
top_n |
Maximum number of taxa to represent. Default: 20. |
ggplot2
data(test_prep_rec) ## Running the function returns a boxplot, abundance_plt(test_prep_rec) ## Giving the value "heatmap" to the type parameter, the resulting graph ## a heatmap. # abundance_plt(test_prep_rec, type = "heatmap") ## By default, those taxa significant in all methods are plotted. If you want ## to graph some determined features, you can pass them as vector through the ## taxa_ids parameter. # taxa_ids <- c("Otu_96", "Otu_78", "Otu_88", "Otu_35", "Otu_94", "Otu_34") # abundance_plt(test_prep_rec, taxa_ids = taxa_ids) # abundance_plt(test_prep_rec, taxa_ids = taxa_ids, type = "heatmap") ## abundance_plt function needs a PrepRecipe. If you pass a a non-prep ## Recipe the output is an error. data(test_rec) err <- testthat::expect_error(abundance_plt(test_rec)) errdata(test_prep_rec) ## Running the function returns a boxplot, abundance_plt(test_prep_rec) ## Giving the value "heatmap" to the type parameter, the resulting graph ## a heatmap. # abundance_plt(test_prep_rec, type = "heatmap") ## By default, those taxa significant in all methods are plotted. If you want ## to graph some determined features, you can pass them as vector through the ## taxa_ids parameter. # taxa_ids <- c("Otu_96", "Otu_78", "Otu_88", "Otu_35", "Otu_94", "Otu_34") # abundance_plt(test_prep_rec, taxa_ids = taxa_ids) # abundance_plt(test_prep_rec, taxa_ids = taxa_ids, type = "heatmap") ## abundance_plt function needs a PrepRecipe. If you pass a a non-prep ## Recipe the output is an error. data(test_rec) err <- testthat::expect_error(abundance_plt(test_rec)) err
Adds taxonomic level of interest in the Recipe.
add_tax(rec, tax_info) ## S4 method for signature 'Recipe' add_tax(rec, tax_info) ## S4 method for signature 'PrepRecipe' add_tax(rec, tax_info)add_tax(rec, tax_info) ## S4 method for signature 'Recipe' add_tax(rec, tax_info) ## S4 method for signature 'PrepRecipe' add_tax(rec, tax_info)
rec |
A |
tax_info |
A character string of taxonomic levels that will be used in any context. |
A Recipe object.
data(metaHIV_phy) ## Define recipe rec <- recipe(metaHIV_phy) ## add var info rec <- add_tax(rec, tax_info = "Species") rec ## add tax info to a prep-Recipe returns an error data(test_prep_rec) err <- testthat::expect_error( add_tax(test_prep_rec, tax_info = "Species") ) errdata(metaHIV_phy) ## Define recipe rec <- recipe(metaHIV_phy) ## add var info rec <- add_tax(rec, tax_info = "Species") rec ## add tax info to a prep-Recipe returns an error data(test_prep_rec) err <- testthat::expect_error( add_tax(test_prep_rec, tax_info = "Species") ) err
Adds variable of interest to the Recipe
add_var(rec, var_info) ## S4 method for signature 'Recipe' add_var(rec, var_info) ## S4 method for signature 'PrepRecipe' add_var(rec, var_info)add_var(rec, var_info) ## S4 method for signature 'Recipe' add_var(rec, var_info) ## S4 method for signature 'PrepRecipe' add_var(rec, var_info)
rec |
A |
var_info |
A character string of column names corresponding to variables that will be used in any context. |
A Recipe object.
data(metaHIV_phy) ## Define recipe rec <- recipe(metaHIV_phy) ## add var info rec <- add_var(rec, var_info = "RiskGroup2") rec ## add var info to a prep-recipe returns an error data(test_prep_rec) err <- testthat::expect_error( add_var(test_prep_rec, var_info = "RiskGroup2") ) errdata(metaHIV_phy) ## Define recipe rec <- recipe(metaHIV_phy) ## add var info rec <- add_var(rec, var_info = "RiskGroup2") rec ## add var info to a prep-recipe returns an error data(test_prep_rec) err <- testthat::expect_error( add_var(test_prep_rec, var_info = "RiskGroup2") ) err
For a prep Recipe adds a consensus strategies to use for result extraction.
bake( rec, count_cutoff = NULL, weights = NULL, exclude = NULL, id = rand_id("bake") ) ## S4 method for signature 'PrepRecipe' bake( rec, count_cutoff = NULL, weights = NULL, exclude = NULL, id = rand_id("bake") ) ## S4 method for signature 'Recipe' bake( rec, count_cutoff = NULL, weights = NULL, exclude = NULL, id = rand_id("bake") )bake( rec, count_cutoff = NULL, weights = NULL, exclude = NULL, id = rand_id("bake") ) ## S4 method for signature 'PrepRecipe' bake( rec, count_cutoff = NULL, weights = NULL, exclude = NULL, id = rand_id("bake") ) ## S4 method for signature 'Recipe' bake( rec, count_cutoff = NULL, weights = NULL, exclude = NULL, id = rand_id("bake") )
rec |
A Recipe object. The step will be added to the sequence of operations for this Recipe. |
count_cutoff |
Indicates the minimum number of methods in which an OTU
must be present (Default: NULL). If count_cutoff is NULL count_cutoff is
equal to |
weights |
Named vector with the ponderation value for each method. |
exclude |
Method ids to exclude. |
id |
A character string that is unique to this step to identify it. |
An object of class PrepRecipe
data(test_prep_rec) rec <- test_prep_rec ## Default bake extracts common OTUs in all DA tested methods ## (In this case the Recipe contains 3 methods) res <- bake(rec) cool(res) ## bake and cool methods needs a PrepRecipe. If you pass a non-PrepRecipe ## the output is an error. data(test_rec) err <- testthat::expect_error(bake(test_rec)) err ## We can use the parameter `cout_cutoff` to for example select those OTUs ## shared with at least two methods res <- bake(rec, count_cutoff = 2) cool(res) ## Furthermore, we can exclude methods from the consensus strategy with the ## `exclude` parameter. res <- bake(rec, exclude = steps_ids(rec, "da")[1]) cool(res) ## Finally, we can use the `weights` parameter to weigh each method. weights <- c(2, 1, 1) names(weights) <- steps_ids(rec, "da") res <- bake(rec, weights = weights) cool(res)data(test_prep_rec) rec <- test_prep_rec ## Default bake extracts common OTUs in all DA tested methods ## (In this case the Recipe contains 3 methods) res <- bake(rec) cool(res) ## bake and cool methods needs a PrepRecipe. If you pass a non-PrepRecipe ## the output is an error. data(test_rec) err <- testthat::expect_error(bake(test_rec)) err ## We can use the parameter `cout_cutoff` to for example select those OTUs ## shared with at least two methods res <- bake(rec, count_cutoff = 2) cool(res) ## Furthermore, we can exclude methods from the consensus strategy with the ## `exclude` parameter. res <- bake(rec, exclude = steps_ids(rec, "da")[1]) cool(res) ## Finally, we can use the `weights` parameter to weigh each method. weights <- c(2, 1, 1) names(weights) <- steps_ids(rec, "da") res <- bake(rec, weights = weights) cool(res)
Checks if Recipe contains a rarefaction step
contains_rarefaction(rec)contains_rarefaction(rec)
rec |
A Recipe object. The step will be added to the sequence of operations for this recipe. |
boolean
data(GlobalPatterns, package = "phyloseq") rec <- phyloseq::subset_samples( GlobalPatterns, SampleType %in% c("Soil", "Skin") ) |> recipe(var_info = "SampleType", tax_info = "Genus") |> step_rarefaction() contains_rarefaction(rec)data(GlobalPatterns, package = "phyloseq") rec <- phyloseq::subset_samples( GlobalPatterns, SampleType %in% c("Soil", "Skin") ) |> recipe(var_info = "SampleType", tax_info = "Genus") |> step_rarefaction() contains_rarefaction(rec)
Extract results from defined bake
cool(rec, bake = 1) ## S4 method for signature 'Recipe' cool(rec, bake = 1) ## S4 method for signature 'PrepRecipe' cool(rec, bake = 1)cool(rec, bake = 1) ## S4 method for signature 'Recipe' cool(rec, bake = 1) ## S4 method for signature 'PrepRecipe' cool(rec, bake = 1)
rec |
A |
bake |
Name or index of the bake to extract. |
tbl_df
data(test_prep_rec) ## First we need to add bakes (extraction strategies) to the PrepRecipe. rec <- bake(test_prep_rec) ## Finally we can extract the results with the cool method cool(rec) ## By default cool extracts the results of the first bake. If we have more ## bakes we can extract the one that you want with the bake parameter. rec <- bake(rec, count_cutoff = 1) cool(rec, 2) ## bake and cool methods needs a prep-Recipe. If you pass a non-PrepRecipe ## the output is an error. data(test_rec) err <- testthat::expect_error(cool(test_rec)) errdata(test_prep_rec) ## First we need to add bakes (extraction strategies) to the PrepRecipe. rec <- bake(test_prep_rec) ## Finally we can extract the results with the cool method cool(rec) ## By default cool extracts the results of the first bake. If we have more ## bakes we can extract the one that you want with the bake parameter. rec <- bake(rec, count_cutoff = 1) cool(rec, 2) ## bake and cool methods needs a prep-Recipe. If you pass a non-PrepRecipe ## the output is an error. data(test_rec) err <- testthat::expect_error(cool(test_rec)) err
overlap_df function as a heatmap.Plot otuput of the overlap_df function as a heatmap.
corr_heatmap(rec, steps = steps_ids(rec, "da"), font_size = 15, type = "all") ## S4 method for signature 'Recipe' corr_heatmap(rec, steps = steps_ids(rec, "da"), font_size = 15, type = "all") ## S4 method for signature 'PrepRecipe' corr_heatmap(rec, steps = steps_ids(rec, "da"), font_size = 15, type = "all")corr_heatmap(rec, steps = steps_ids(rec, "da"), font_size = 15, type = "all") ## S4 method for signature 'Recipe' corr_heatmap(rec, steps = steps_ids(rec, "da"), font_size = 15, type = "all") ## S4 method for signature 'PrepRecipe' corr_heatmap(rec, steps = steps_ids(rec, "da"), font_size = 15, type = "all")
rec |
A |
steps |
Character vector with step_ids to take in account. |
font_size |
Size of the axis font. |
type |
Indicates whether to use all taxa ("all") or only those that are differentially abundant in at least one method ("da"). Default as "all". |
heatmap
data(test_prep_rec) ## Running the function returns a UpSet plot ordered by frequency. corr_heatmap(test_prep_rec) ## If you want to exclude a method for the plot, you can remove it with the ## step parameter. In the following example we eliminate from the graph the ## results of maaslin corr_heatmap(test_prep_rec, steps = steps_ids(test_prep_rec, "da")[-1]) ## corr_heatmap function needs a PrepRecipe. If you pass a a non-prep ## Recipe the output is an error. data(test_rec) err <- testthat::expect_error(corr_heatmap(test_rec)) errdata(test_prep_rec) ## Running the function returns a UpSet plot ordered by frequency. corr_heatmap(test_prep_rec) ## If you want to exclude a method for the plot, you can remove it with the ## step parameter. In the following example we eliminate from the graph the ## results of maaslin corr_heatmap(test_prep_rec, steps = steps_ids(test_prep_rec, "da")[-1]) ## corr_heatmap function needs a PrepRecipe. If you pass a a non-prep ## Recipe the output is an error. data(test_rec) err <- testthat::expect_error(corr_heatmap(test_rec)) err
Plot the number of shared DA OTUs between methods.
exclusion_plt(rec, steps = steps_ids(rec, "da")) ## S4 method for signature 'Recipe' exclusion_plt(rec, steps = steps_ids(rec, "da")) ## S4 method for signature 'PrepRecipe' exclusion_plt(rec, steps = steps_ids(rec, "da"))exclusion_plt(rec, steps = steps_ids(rec, "da")) ## S4 method for signature 'Recipe' exclusion_plt(rec, steps = steps_ids(rec, "da")) ## S4 method for signature 'PrepRecipe' exclusion_plt(rec, steps = steps_ids(rec, "da"))
rec |
A |
steps |
Character vector with step_ids to take in account. |
ggplot2-class object
data(test_prep_rec) ## Running the function returns a barplot plot, exclusion_plt(test_prep_rec) ## If you want to exclude a method for the plot, you can remove it with the ## step parameter. In the following example we eliminate from the graph the ## results of maaslin exclusion_plt(test_prep_rec, steps = steps_ids(test_prep_rec, "da")[-1]) ## intersection_plt function needs a PrepRecipe. If you pass a a non-prep ## Recipe the output is an error. data(test_rec) err <- testthat::expect_error(exclusion_plt(test_rec)) errdata(test_prep_rec) ## Running the function returns a barplot plot, exclusion_plt(test_prep_rec) ## If you want to exclude a method for the plot, you can remove it with the ## step parameter. In the following example we eliminate from the graph the ## results of maaslin exclusion_plt(test_prep_rec, steps = steps_ids(test_prep_rec, "da")[-1]) ## intersection_plt function needs a PrepRecipe. If you pass a a non-prep ## Recipe the output is an error. data(test_rec) err <- testthat::expect_error(exclusion_plt(test_rec)) err
Export step parameters as json.
export_steps(rec, file_name)export_steps(rec, file_name)
rec |
A Recipe object. |
file_name |
The path and file name of the optout file. |
invisible
data(metaHIV_phy) ## Create a Recipe with steps rec <- recipe(metaHIV_phy, "RiskGroup2", "Species") |> step_subset_taxa(tax_level = "Kingdom", taxa = c("Bacteria", "Archaea")) |> step_filter_taxa(.f = "function(x) sum(x > 0) >= (0.3 * length(x))") |> step_filter_by_prevalence(0.4) |> step_maaslin() ## Prep Recipe rec <- prep(rec, parallel = TRUE) ## Export to json file export_steps(rec, tempfile(fileext = ".json"))data(metaHIV_phy) ## Create a Recipe with steps rec <- recipe(metaHIV_phy, "RiskGroup2", "Species") |> step_subset_taxa(tax_level = "Kingdom", taxa = c("Bacteria", "Archaea")) |> step_filter_taxa(.f = "function(x) sum(x > 0) >= (0.3 * length(x))") |> step_filter_by_prevalence(0.4) |> step_maaslin() ## Prep Recipe rec <- prep(rec, parallel = TRUE) ## Export to json file export_steps(rec, tempfile(fileext = ".json"))
Finds common OTU between method results
find_intersections(rec, steps = steps_ids(rec, "da"))find_intersections(rec, steps = steps_ids(rec, "da"))
rec |
A Recipe object. |
steps |
character vector with step ids to take in account |
tibble
data(test_prep_rec) ## From a PrepRecipe we can extract a tibble with all intersections intersections <- find_intersections(test_prep_rec) intersections ## Additionally, we can exclude some methods form the table intersections <- find_intersections( test_prep_rec, steps = steps_ids(test_prep_rec, "da")[-1] ) intersectionsdata(test_prep_rec) ## From a PrepRecipe we can extract a tibble with all intersections intersections <- find_intersections(test_prep_rec) intersections ## Additionally, we can exclude some methods form the table intersections <- find_intersections( test_prep_rec, steps = steps_ids(test_prep_rec, "da")[-1] ) intersections
Returns phyloseq from Recipe-class object
get_phy(rec) ## S4 method for signature 'Recipe' get_phy(rec)get_phy(rec) ## S4 method for signature 'Recipe' get_phy(rec)
rec |
A |
Phyloseq class object
data(metaHIV_phy) ## Define recipe rec <- recipe(metaHIV_phy, var_info = "RiskGroup2", tax_info = "Species") ## Extract phyloseq object get_phy(rec)data(metaHIV_phy) ## Define recipe rec <- recipe(metaHIV_phy, var_info = "RiskGroup2", tax_info = "Species") ## Extract phyloseq object get_phy(rec)
Returns tax_info from Recipe-class object
get_tax(rec) ## S4 method for signature 'Recipe' get_tax(rec)get_tax(rec) ## S4 method for signature 'Recipe' get_tax(rec)
rec |
A |
Tibble containing tax_info.
data(metaHIV_phy) ## Define recipe rec <- recipe(metaHIV_phy, var_info = "RiskGroup2", tax_info = "Species") ## Extract taxonomic level get_tax(rec)data(metaHIV_phy) ## Define recipe rec <- recipe(metaHIV_phy, var_info = "RiskGroup2", tax_info = "Species") ## Extract taxonomic level get_tax(rec)
Returns var_info from Recipe-class object
get_var(rec) ## S4 method for signature 'Recipe' get_var(rec)get_var(rec) ## S4 method for signature 'Recipe' get_var(rec)
rec |
A |
Tibble containing var_info.
data(metaHIV_phy) ## Define recipe rec <- recipe(metaHIV_phy, var_info = "RiskGroup2", tax_info = "Species") ## Extract variable of interest get_var(rec)data(metaHIV_phy) ## Define recipe rec <- recipe(metaHIV_phy, var_info = "RiskGroup2", tax_info = "Species") ## Extract variable of interest get_var(rec)
Import steps from json file
import_steps(rec, file, parallel = TRUE, workers = 4)import_steps(rec, file, parallel = TRUE, workers = 4)
rec |
A Recipe object. |
file |
Path to the input file. |
parallel |
if FALSE, no palatalization. if TRUE, parallel execution using future and furrr packages. |
workers |
Number of workers for palatalization. |
recipe-class object
data(metaHIV_phy) ## Initialize the Recipe with a phyloseq object rec <- recipe(metaHIV_phy, "RiskGroup2", "Species") rec ## Import steps json_file <- system.file("extdata", "test.json", package = "dar") rec <- import_steps(rec, json_file) rec ## If the json file contains 'bake', the Recipe is automatically prepared. json_file <- system.file("extdata", "test_bake.json", package = "dar") rec <- recipe(metaHIV_phy, "RiskGroup2", "Species") |> import_steps(json_file) rec cool(rec)data(metaHIV_phy) ## Initialize the Recipe with a phyloseq object rec <- recipe(metaHIV_phy, "RiskGroup2", "Species") rec ## Import steps json_file <- system.file("extdata", "test.json", package = "dar") rec <- import_steps(rec, json_file) rec ## If the json file contains 'bake', the Recipe is automatically prepared. json_file <- system.file("extdata", "test_bake.json", package = "dar") rec <- recipe(metaHIV_phy, "RiskGroup2", "Species") |> import_steps(json_file) rec cool(rec)
Returns data.frame with OTU intersection between methods
intersection_df(rec, steps = steps_ids(rec, "da"), tidy = FALSE) ## S4 method for signature 'Recipe' intersection_df(rec, steps = steps_ids(rec, "da"), tidy = FALSE) ## S4 method for signature 'PrepRecipe' intersection_df(rec, steps = steps_ids(rec, "da"), tidy = FALSE)intersection_df(rec, steps = steps_ids(rec, "da"), tidy = FALSE) ## S4 method for signature 'Recipe' intersection_df(rec, steps = steps_ids(rec, "da"), tidy = FALSE) ## S4 method for signature 'PrepRecipe' intersection_df(rec, steps = steps_ids(rec, "da"), tidy = FALSE)
rec |
A |
steps |
character vector with step_ids to take in account. |
tidy |
Boolan indicating if result must be in tidy format. |
data.frame class object
data(test_prep_rec) df <- intersection_df(test_prep_rec) head(df) ## intersection_df function needs a prep-Recipe. If you pass a a non-prep ## recipe the output is an error. data(test_rec) err <- testthat::expect_error(intersection_df(test_rec)) errdata(test_prep_rec) df <- intersection_df(test_prep_rec) head(df) ## intersection_df function needs a prep-Recipe. If you pass a a non-prep ## recipe the output is an error. data(test_rec) err <- testthat::expect_error(intersection_df(test_rec)) err
Plot results using UpSet plot
intersection_plt( rec, steps = steps_ids(rec, "da"), ordered_by = c("freq", "degree"), font_size = 2 ) ## S4 method for signature 'Recipe' intersection_plt(rec, steps, font_size) ## S4 method for signature 'PrepRecipe' intersection_plt( rec, steps = steps_ids(rec, "da"), ordered_by = c("freq", "degree"), font_size = 2 )intersection_plt( rec, steps = steps_ids(rec, "da"), ordered_by = c("freq", "degree"), font_size = 2 ) ## S4 method for signature 'Recipe' intersection_plt(rec, steps, font_size) ## S4 method for signature 'PrepRecipe' intersection_plt( rec, steps = steps_ids(rec, "da"), ordered_by = c("freq", "degree"), font_size = 2 )
rec |
A |
steps |
Character vector with step_ids to take in account. |
ordered_by |
How the intersections in the matrix should be ordered by. Options include frequency (entered as "freq"), degree, or both in any order. |
font_size |
Size of the font. |
UpSet plot
data(test_prep_rec) ## Running the function returns a UpSet plot ordered by frequency. intersection_plt(test_prep_rec) ## Alternatively, you can order the plot by degree intersection_plt(test_prep_rec, ordered_by = "degree") ## If you want to exclude a method for the plot, you can remove it with the ## step parameter. In the following example we eliminate from the graph the ## results of maaslin intersection_plt(test_prep_rec, steps = steps_ids(test_prep_rec, "da")[-1]) ## intersection_plt function needs a PrepRecipe. If you pass a a non-prep ## Recipe the output is an error. data(test_rec) err <- testthat::expect_error(intersection_plt(test_rec)) errdata(test_prep_rec) ## Running the function returns a UpSet plot ordered by frequency. intersection_plt(test_prep_rec) ## Alternatively, you can order the plot by degree intersection_plt(test_prep_rec, ordered_by = "degree") ## If you want to exclude a method for the plot, you can remove it with the ## step parameter. In the following example we eliminate from the graph the ## results of maaslin intersection_plt(test_prep_rec, steps = steps_ids(test_prep_rec, "da")[-1]) ## intersection_plt function needs a PrepRecipe. If you pass a a non-prep ## Recipe the output is an error. data(test_rec) err <- testthat::expect_error(intersection_plt(test_rec)) err
A Phyloseq object containing abundance counts and sample_data for metaHIV project. Count reads were annotated with Metaphlan3.
data("metaHIV_phy")data("metaHIV_phy")
A phyloseq object with 451 taxas, 30 samples, 3 sample variables and 7 taxonomic ranks.
s3://fcatala-09142020-eu-west-1/cloud_test/SpeciesQuantification/Kraken2
Plots number of differentially abundant features mutually found by defined number of methods, colored by the differential abundance direction and separated by comparison.
mutual_plt( rec, count_cutoff = NULL, comparisons = NULL, steps = steps_ids(rec, type = "da"), top_n = 20 ) ## S4 method for signature 'Recipe' mutual_plt( rec, count_cutoff = NULL, comparisons = NULL, steps = steps_ids(rec, type = "da"), top_n = 20 ) ## S4 method for signature 'PrepRecipe' mutual_plt( rec, count_cutoff = NULL, comparisons = NULL, steps = steps_ids(rec, type = "da"), top_n = 20 )mutual_plt( rec, count_cutoff = NULL, comparisons = NULL, steps = steps_ids(rec, type = "da"), top_n = 20 ) ## S4 method for signature 'Recipe' mutual_plt( rec, count_cutoff = NULL, comparisons = NULL, steps = steps_ids(rec, type = "da"), top_n = 20 ) ## S4 method for signature 'PrepRecipe' mutual_plt( rec, count_cutoff = NULL, comparisons = NULL, steps = steps_ids(rec, type = "da"), top_n = 20 )
rec |
A Recipe or Recipe step. |
count_cutoff |
Indicates the minimum number of methods in which an OTU
must be present (Default: NULL). If count_cutoff is NULL count_cutoff is
equal to |
comparisons |
By default, this function plots all comparisons. However, if the user indicates the comparison or comparisons of interest, only the selected ones will be plotted. |
steps |
Character vector with step_ids to take in account. Default all "da" methods. |
top_n |
Maximum number of taxa to represent. Default: 20. |
ggplot2
data(test_prep_rec) ## Running the function returns a tile plot, mutual_plt(test_prep_rec) ## The count_cutoff indicates the minimum number of methods in which an OTU ## must be present. By default the value is equal to ## length(steps_ids(rec, "da")) * 2 / 3 but it is customizable. mutual_plt( test_prep_rec, count_cutoff = length(steps_ids(test_prep_rec, "da")) ) ## A single comparisons can be plotted through the comparison parameter. mutual_plt(test_prep_rec, comparisons = c("hts_msm")) ## If you want to exclude a method for the plot, you can remove it with the ## step parameter. In the following example we eliminate from the graph the ## results of maaslin. mutual_plt(test_prep_rec, steps = steps_ids(test_prep_rec, "da")[-1]) ## mutual_plt function needs a PrepRecipe. If you pass a a non-PrepRecipe ## the output is an error. data(test_rec) err <- testthat::expect_error(mutual_plt(test_rec)) errdata(test_prep_rec) ## Running the function returns a tile plot, mutual_plt(test_prep_rec) ## The count_cutoff indicates the minimum number of methods in which an OTU ## must be present. By default the value is equal to ## length(steps_ids(rec, "da")) * 2 / 3 but it is customizable. mutual_plt( test_prep_rec, count_cutoff = length(steps_ids(test_prep_rec, "da")) ) ## A single comparisons can be plotted through the comparison parameter. mutual_plt(test_prep_rec, comparisons = c("hts_msm")) ## If you want to exclude a method for the plot, you can remove it with the ## step parameter. In the following example we eliminate from the graph the ## results of maaslin. mutual_plt(test_prep_rec, steps = steps_ids(test_prep_rec, "da")[-1]) ## mutual_plt function needs a PrepRecipe. If you pass a a non-PrepRecipe ## the output is an error. data(test_rec) err <- testthat::expect_error(mutual_plt(test_rec)) err
Extracts otu_table from phyloseq inside a Recipe
otu_table(rec) ## S4 method for signature 'Recipe' otu_table(rec)otu_table(rec) ## S4 method for signature 'Recipe' otu_table(rec)
rec |
A Recipe or Recipe step. |
A tibble
data(metaHIV_phy) ## Define recipe rec <- recipe(metaHIV_phy, var_info = "RiskGroup2", tax_info = "Species") ## Extract otu_table from phyloseq object otu_table(rec)data(metaHIV_phy) ## Define recipe rec <- recipe(metaHIV_phy, var_info = "RiskGroup2", tax_info = "Species") ## Extract otu_table from phyloseq object otu_table(rec)
Overlap of significant OTUs between tested methods.
overlap_df(rec, steps = steps_ids(rec, "da"), type = "all") ## S4 method for signature 'Recipe' overlap_df(rec, steps = steps_ids(rec, "da"), type = "all") ## S4 method for signature 'PrepRecipe' overlap_df(rec, steps = steps_ids(rec, "da"), type = "all")overlap_df(rec, steps = steps_ids(rec, "da"), type = "all") ## S4 method for signature 'Recipe' overlap_df(rec, steps = steps_ids(rec, "da"), type = "all") ## S4 method for signature 'PrepRecipe' overlap_df(rec, steps = steps_ids(rec, "da"), type = "all")
rec |
A |
steps |
Character vector with step_ids to take in account. |
type |
Indicates whether to use all taxa ("all") or only those that are differentially abundant in at least one method ("da"). Default as "all". |
df
data(test_prep_rec) ## Running the function returns a UpSet plot ordered by frequency. df <- overlap_df(test_prep_rec, steps_ids(test_prep_rec, "da")) head(df) ## If you want to exclude a method for the plot, you can remove it with the ## step parameter. In the following example we eliminate from the graph the ## results of maaslin overlap_df(test_prep_rec, steps = steps_ids(test_prep_rec, "da")[-1]) ## overlap_df function needs a prep-Recipe. If you pass a a non-prep ## Recipe the output is an error. data(test_rec) err <- testthat::expect_error(overlap_df(test_rec)) errdata(test_prep_rec) ## Running the function returns a UpSet plot ordered by frequency. df <- overlap_df(test_prep_rec, steps_ids(test_prep_rec, "da")) head(df) ## If you want to exclude a method for the plot, you can remove it with the ## step parameter. In the following example we eliminate from the graph the ## results of maaslin overlap_df(test_prep_rec, steps = steps_ids(test_prep_rec, "da")[-1]) ## overlap_df function needs a prep-Recipe. If you pass a a non-prep ## Recipe the output is an error. data(test_rec) err <- testthat::expect_error(overlap_df(test_rec)) err
phy_qc() returns a tibble. It will have information about some important metrics about the sparsity of the count matrix. The content of the table is as follows:
var_levels: levels of the categorical variable of interest. "all" refers to all rows of the dataset (without splitting by categorical levels).
n: total number of values in the count matrix.
n_zero: number of zeros in the count matrix.
pct_zero: percentage of zeros in the count matrix.
pct_all_zero: percentage of taxa with zero counts in all samples.
pct_singletons: percentage of taxa with counts in a single sample.
pct_doubletons: percentage of taxa with counts in two samples.
count_mean: average of the mean counts per sample.
count_min: average of the min counts per sample.
count_max: average of the max counts per sample.
phy_qc(rec) ## S4 method for signature 'Recipe' phy_qc(rec)phy_qc(rec) ## S4 method for signature 'Recipe' phy_qc(rec)
rec |
A Recipe or Recipe step. |
A tibble
data(metaHIV_phy) ## Define Recipe rec <- recipe(metaHIV_phy, var_info = "RiskGroup2", tax_info = "Species") phy_qc(rec)data(metaHIV_phy) ## Define Recipe rec <- recipe(metaHIV_phy, var_info = "RiskGroup2", tax_info = "Species") phy_qc(rec)
A Recipe is a description of the steps to be applied to a data set in order to prepare it for data analysis.
## S4 method for signature 'PrepRecipe' show(object)## S4 method for signature 'PrepRecipe' show(object)
object |
A Recipe object. |
Recipe-class object
phyloseqPhyloseq-class object.
var_infoA tibble that contains the current set of terms in the data
set. This initially defaults to the same data contained in var_info.
tax_infoA tibble that contains the current set of taxonomic levels that will be used in the analysis.
stepsList of step-class objects that will be used by DA.
For a Recipe with at least one preprocessing or DA operation run the steps in a convenient order.
prep(rec, parallel = TRUE, workers = 4, force = FALSE) ## S4 method for signature 'Recipe' prep(rec, parallel = TRUE, workers = 4, force = FALSE)prep(rec, parallel = TRUE, workers = 4, force = FALSE) ## S4 method for signature 'Recipe' prep(rec, parallel = TRUE, workers = 4, force = FALSE)
rec |
A |
parallel |
if FALSE, no palatalization. if TRUE, parallel execution using future and furrr packages. |
workers |
Number of workers for palatalization. |
force |
Force the reexecution of all steps. This remove previous results. |
A PrepRecipe object.
data(metaHIV_phy) ## Define Recipe rec <- recipe(metaHIV_phy, var_info = "RiskGroup2", tax_info = "Class") |> step_subset_taxa(tax_level = "Kingdom", taxa = c("Bacteria", "Archaea")) |> step_filter_taxa(.f = "function(x) sum(x > 0) >= (0.03 * length(x))") |> step_maaslin() ## Prep Recipe da_results <- prep(rec) ## If you try ## Consensus strategy n_methods <- 2 da_results <- bake(da_results, count_cutoff = n_methods) da_results ## If you try to run prep on an object of class PrepRecipe it returns an ## error. err <- testthat::expect_error(prep(da_results)) err ## You can force the overwrite with: prep(rec, force = TRUE) ## This function can operate in parallel thanks to future and furrr packages. prep(rec, parallel = TRUE, workers = 2)data(metaHIV_phy) ## Define Recipe rec <- recipe(metaHIV_phy, var_info = "RiskGroup2", tax_info = "Class") |> step_subset_taxa(tax_level = "Kingdom", taxa = c("Bacteria", "Archaea")) |> step_filter_taxa(.f = "function(x) sum(x > 0) >= (0.03 * length(x))") |> step_maaslin() ## Prep Recipe da_results <- prep(rec) ## If you try ## Consensus strategy n_methods <- 2 da_results <- bake(da_results, count_cutoff = n_methods) da_results ## If you try to run prep on an object of class PrepRecipe it returns an ## error. err <- testthat::expect_error(prep(da_results)) err ## You can force the overwrite with: prep(rec, force = TRUE) ## This function can operate in parallel thanks to future and furrr packages. prep(rec, parallel = TRUE, workers = 2)
A PrepRecipe is Recipe with the results corresponding to the steps defined in the Recipe.
PrepRecipe-class object
resultsContains the results of all defined analysis in the Recipe.
bakesContains the executed bakes.
Make a random identification field for steps
rand_id(prefix = "step")rand_id(prefix = "step")
prefix |
A single character string |
A character string with the prefix and random letters separated by and underscore.
rand_id("step")rand_id("step")
A Recipe is a description of the steps to be applied to a data set in order to prepare it for data analysis.
recipe( microbiome_object = NULL, var_info = NULL, tax_info = NULL, steps = list() )recipe( microbiome_object = NULL, var_info = NULL, tax_info = NULL, steps = list() )
microbiome_object |
Phyloseq-class object or TreeSummarizedExperiment-class object. |
var_info |
A character string of column names corresponding to variables that will be used in any context. |
tax_info |
A character string of taxonomic levels that will be used in any context. |
steps |
list with steps. |
An object of class Recipe with sub-objects:
phyloseq |
object
of class |
var_info |
A
tibble that contains the current set of terms in the data set. This
initially defaults to the same data contained in |
tax_info |
A tibble that contains the current set of taxonomic levels that will be used in the analysis. |
data(metaHIV_phy) ## Define recipe rec <- recipe(metaHIV_phy, var_info = "RiskGroup2", tax_info = "Phylum") |> step_subset_taxa(tax_level = "Kingdom", taxa = c("Bacteria", "Archaea")) |> step_filter_taxa(.f = "function(x) sum(x > 0) >= (0.3 * length(x))") |> step_metagenomeseq(rm_zeros = 0.01) |> step_maaslin() ## Prep recipe da_results <- prep(rec) ## Consensus strategy n_methods <- 2 da_results <- bake(da_results, count_cutoff = n_methods) ## Results cool(da_results) ## You can also crate a recipe without var and tax info rec <- recipe(metaHIV_phy) rec ## And define them later rec <- rec |> add_var("RiskGroup2") |> add_tax("Genus") rec ## When trying to add an identical step to an existing one, the system ## returns an information message. rec <- step_ancom(rec) rec <- step_ancom(rec) ## The same with bake da_results <- bake(da_results) da_results <- bake(da_results)data(metaHIV_phy) ## Define recipe rec <- recipe(metaHIV_phy, var_info = "RiskGroup2", tax_info = "Phylum") |> step_subset_taxa(tax_level = "Kingdom", taxa = c("Bacteria", "Archaea")) |> step_filter_taxa(.f = "function(x) sum(x > 0) >= (0.3 * length(x))") |> step_metagenomeseq(rm_zeros = 0.01) |> step_maaslin() ## Prep recipe da_results <- prep(rec) ## Consensus strategy n_methods <- 2 da_results <- bake(da_results, count_cutoff = n_methods) ## Results cool(da_results) ## You can also crate a recipe without var and tax info rec <- recipe(metaHIV_phy) rec ## And define them later rec <- rec |> add_var("RiskGroup2") |> add_tax("Genus") rec ## When trying to add an identical step to an existing one, the system ## returns an information message. rec <- step_ancom(rec) rec <- step_ancom(rec) ## The same with bake da_results <- bake(da_results) da_results <- bake(da_results)
Extracts sample_data from phyloseq inside a Recipe
sample_data(rec) ## S4 method for signature 'Recipe' sample_data(rec)sample_data(rec) ## S4 method for signature 'Recipe' sample_data(rec)
rec |
A Recipe or Recipe step. |
A tibble
data(metaHIV_phy) ## Define recipe rec <- recipe(metaHIV_phy, var_info = "RiskGroup2", tax_info = "Species") ## Extract sample_data from phyloseq object sample_data(rec)data(metaHIV_phy) ## Define recipe rec <- recipe(metaHIV_phy, var_info = "RiskGroup2", tax_info = "Species") ## Extract sample_data from phyloseq object sample_data(rec)
A differential abundance analysis for the comparison of two or more
conditions. For example, single-organism and meta-RNA-seq high-throughput
sequencing assays, or of selected and unselected values from in-vitro
sequence selections. Uses a Dirichlet-multinomial model to infer abundance
from counts, that has been optimized for three or more experimental
replicates. Infers sampling variation and calculates the expected false
discovery rate given the biological and sampling variation using the Wilcox
rank test or Welches t-test (aldex.ttest) or the glm and Kruskal Wallis tests
(aldex.glm). Reports both P and fdr values calculated by the Benjamini
Hochberg correction (Not supported in dar package).
step_aldex( rec, max_significance = 0.05, mc.samples = 128, denom = "all", rarefy = FALSE, id = rand_id("aldex") ) ## S4 method for signature 'Recipe' step_aldex( rec, max_significance = 0.05, mc.samples = 128, denom = "all", rarefy = FALSE, id = rand_id("aldex") ) ## S4 method for signature 'PrepRecipe' step_aldex( rec, max_significance = 0.05, mc.samples = 128, denom = "all", rarefy = FALSE, id = rand_id("aldex") )step_aldex( rec, max_significance = 0.05, mc.samples = 128, denom = "all", rarefy = FALSE, id = rand_id("aldex") ) ## S4 method for signature 'Recipe' step_aldex( rec, max_significance = 0.05, mc.samples = 128, denom = "all", rarefy = FALSE, id = rand_id("aldex") ) ## S4 method for signature 'PrepRecipe' step_aldex( rec, max_significance = 0.05, mc.samples = 128, denom = "all", rarefy = FALSE, id = rand_id("aldex") )
rec |
A Recipe object. The step will be added to the sequence of operations for this Recipe. |
max_significance |
Benjamini-Hochberg corrected P value of Welch’s t test cutoff. |
mc.samples |
The number of Monte Carlo instances to use to estimate the underlying distributions; since we are estimating central tendencies, 128 is usually sufficient, but larger numbers may be . |
denom |
An any variable (all, iqlr, zero, lvha, median, user) indicating features to use as the denominator for the Geometric Mean calculation The default "all" uses the geometric mean abundance of all features. Using "median" returns the median abundance of all features. Using "iqlr" uses the features that are between the first and third quartile of the variance of the clr values across all samples. Using "zero" uses the non-zero features in each grop as the denominator. This approach is an extreme case where there are many nonzero features in one condition but many zeros in another. Using "lvha" uses features that have low variance (bottom quartile) and high relative abundance (top quartile in every sample). It is also possible to supply a vector of row indices to use as the denominator. Here, the experimentalist is determining a-priori which rows are thought to be invariant. In the case of RNA-seq, this could include ribosomal protein genes and and other house-keeping genes. This should be used with caution because the offsets may be different in the original data and in the data used by the function because features that are 0 in all samples are removed by aldex.clr. |
rarefy |
Boolean indicating if OTU counts must be rarefyed. This rarefaction uses the standard R sample function to resample from the abundance values in the otu_table component of the first argument, physeq. Often one of the major goals of this procedure is to achieve parity in total number of counts between samples, as an alternative to other formal normalization procedures, which is why a single value for the sample.size is expected. If 'no_seed', rarefaction is performed without a set seed. |
id |
A character string that is unique to this step to identify it. |
The run_aldex function is a wrapper that performs log-ratio transformation and statistical testing in a single line of code. Specifically, this function: (a) generates Monte Carlo samples of the Dirichlet distribution for each sample, (b) converts each instance using a log-ratio transform, then (c) returns test results for two sample (Welch's t, Wilcoxon) test. This function also estimates effect size for two sample analyses.
An object of class Recipe
Other Diff taxa steps:
step_ancom(),
step_corncob(),
step_deseq(),
step_lefse(),
step_maaslin(),
step_metagenomeseq(),
step_wilcox()
data(metaHIV_phy) ## Init Recipe rec <- recipe(metaHIV_phy, "RiskGroup2", "Phylum") |> step_subset_taxa(tax_level = "Kingdom", taxa = c("Bacteria", "Archaea")) |> step_filter_taxa(.f = "function(x) sum(x > 0) >= (0.4 * length(x))") rec ## Define ALDEX step with default parameters and prep rec <- step_aldex(rec) |> prep(parallel = FALSE) rec ## Wearing rarefaction only for this step rec <- recipe(metaHIV_phy, "RiskGroup2", "Species") |> step_aldex(rarefy = TRUE) recdata(metaHIV_phy) ## Init Recipe rec <- recipe(metaHIV_phy, "RiskGroup2", "Phylum") |> step_subset_taxa(tax_level = "Kingdom", taxa = c("Bacteria", "Archaea")) |> step_filter_taxa(.f = "function(x) sum(x > 0) >= (0.4 * length(x))") rec ## Define ALDEX step with default parameters and prep rec <- step_aldex(rec) |> prep(parallel = FALSE) rec ## Wearing rarefaction only for this step rec <- recipe(metaHIV_phy, "RiskGroup2", "Species") |> step_aldex(rarefy = TRUE) rec
Determine taxa whose absolute abundances, per unit volume, of the ecosystem (e.g., gut) are significantly different with changes in the covariate of interest (e.g., group). The current version of ancombc2 function implements Analysis of Compositions of Microbiomes with Bias Correction (ANCOM-BC2) in cross-sectional and repeated measurements data. In addition to the two-group comparison, ANCOM-BC2 also supports testing for continuous covariates and multi-group comparisons, including the global test, pairwise directional test, Dunnett's type of test, and trend test.
step_ancom( rec, fix_formula = get_var(rec)[[1]], rand_formula = NULL, p_adj_method = "holm", prv_cut = 0.1, lib_cut = 0, s0_perc = 0.05, group = NULL, struc_zero = FALSE, neg_lb = FALSE, alpha = 0.05, n_cl = 1, verbose = FALSE, global = FALSE, pairwise = FALSE, dunnet = FALSE, trend = FALSE, rarefy = FALSE, id = rand_id("ancom") ) ## S4 method for signature 'Recipe' step_ancom( rec, fix_formula = get_var(rec)[[1]], rand_formula = NULL, p_adj_method = "holm", prv_cut = 0.1, lib_cut = 0, s0_perc = 0.05, group = NULL, struc_zero = FALSE, neg_lb = FALSE, alpha = 0.05, n_cl = 1, verbose = FALSE, global = FALSE, pairwise = FALSE, dunnet = FALSE, trend = FALSE, rarefy = FALSE, id = rand_id("ancom") ) ## S4 method for signature 'PrepRecipe' step_ancom( rec, fix_formula = get_var(rec)[[1]], rand_formula = NULL, p_adj_method = "holm", prv_cut = 0.1, lib_cut = 0, s0_perc = 0.05, group = NULL, struc_zero = FALSE, neg_lb = FALSE, alpha = 0.05, n_cl = 1, verbose = FALSE, global = FALSE, pairwise = FALSE, dunnet = FALSE, trend = FALSE, rarefy = FALSE, id = rand_id("ancom") )step_ancom( rec, fix_formula = get_var(rec)[[1]], rand_formula = NULL, p_adj_method = "holm", prv_cut = 0.1, lib_cut = 0, s0_perc = 0.05, group = NULL, struc_zero = FALSE, neg_lb = FALSE, alpha = 0.05, n_cl = 1, verbose = FALSE, global = FALSE, pairwise = FALSE, dunnet = FALSE, trend = FALSE, rarefy = FALSE, id = rand_id("ancom") ) ## S4 method for signature 'Recipe' step_ancom( rec, fix_formula = get_var(rec)[[1]], rand_formula = NULL, p_adj_method = "holm", prv_cut = 0.1, lib_cut = 0, s0_perc = 0.05, group = NULL, struc_zero = FALSE, neg_lb = FALSE, alpha = 0.05, n_cl = 1, verbose = FALSE, global = FALSE, pairwise = FALSE, dunnet = FALSE, trend = FALSE, rarefy = FALSE, id = rand_id("ancom") ) ## S4 method for signature 'PrepRecipe' step_ancom( rec, fix_formula = get_var(rec)[[1]], rand_formula = NULL, p_adj_method = "holm", prv_cut = 0.1, lib_cut = 0, s0_perc = 0.05, group = NULL, struc_zero = FALSE, neg_lb = FALSE, alpha = 0.05, n_cl = 1, verbose = FALSE, global = FALSE, pairwise = FALSE, dunnet = FALSE, trend = FALSE, rarefy = FALSE, id = rand_id("ancom") )
rec |
A Recipe object. The step will be added to the sequence of operations for this Recipe. |
fix_formula |
the character string expresses how the microbial absolute abundances for each taxon depend on the fixed effects in metadata. When specifying the fix_formula, make sure to include the group variable in the formula if it is not NULL. |
rand_formula |
the character string expresses how the microbial absolute abundances for each taxon depend on the random effects in metadata. ANCOM-BC2 follows the lmerTest package in formulating the random effects. See ?lmerTest::lmer for more details. Default is NULL. |
p_adj_method |
character. method to adjust p-values. Default is "holm". Options include "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none". See ?stats::p.adjust for more details. |
prv_cut |
a numerical fraction between 0 and 1. Taxa with prevalences less than prv_cut will be excluded in the analysis. For instance, suppose there are 100 samples, if a taxon has nonzero counts presented in less than 10 samples, it will not be further analyzed. Default is 0.10. |
lib_cut |
a numerical threshold for filtering samples based on library sizes. Samples with library sizes less than lib_cut will be excluded in the analysis. Default is 0, i.e. do not discard any sample. |
s0_perc |
a numerical fraction between 0 and 1. Inspired by Significance Analysis of Microarrays (SAM) methodology, a small positive constant is added to the denominator of ANCOM-BC2 test statistic corresponding to each taxon to avoid the significance due to extremely small standard errors, especially for rare taxa. This small positive constant is chosen as s0_perc-th percentile of standard error values for each fixed effect. Default is 0.05 (5th percentile). |
group |
character. The name of the group variable in metadata. group should be discrete. Specifying group is required for detecting structural zeros and performing multi-group comparisons (global test, pairwise directional test, Dunnett's type of test, and trend test). Default is NULL. If the group of interest contains only two categories, leave it as NULL. |
struc_zero |
logical. Whether to detect structural zeros based on group. Default is FALSE. See Details for a more comprehensive discussion on structural zeros. |
neg_lb |
logical. Whether to classify a taxon as a structural zero using its asymptotic lower bound. Default is FALSE. |
alpha |
numeric. Level of significance. Default is 0.05. |
n_cl |
numeric. The number of nodes to be forked. For details, see ?parallel::makeCluster. Default is 1 (no parallel computing). |
verbose |
logical. Whether to generate verbose output during the ANCOM-BC2 fitting process. Default is FALSE. |
global |
logical. Whether to perform the global test. Default is FALSE. |
pairwise |
logical. Whether to perform the pairwise directional test. Default is FALSE. |
dunnet |
logical. Whether to perform the Dunnett's type of test. Default is FALSE. |
trend |
logical. Whether to perform trend test. Default is FALSE. |
rarefy |
Boolean indicating if OTU counts must be rarefyed. This rarefaction uses the standard R sample function to resample from the abundance values in the otu_table component of the first argument, physeq. Often one of the major goals of this procedure is to achieve parity in total number of counts between samples, as an alternative to other formal normalization procedures, which is why a single value for the sample.size is expected. If 'no_seed', rarefaction is performed without a set seed. |
id |
A character string that is unique to this step to identify it. |
An object of class Recipe
Other Diff taxa steps:
step_aldex(),
step_corncob(),
step_deseq(),
step_lefse(),
step_maaslin(),
step_metagenomeseq(),
step_wilcox()
data(metaHIV_phy) ## Init Recipe rec <- recipe(metaHIV_phy, "RiskGroup2", "Phylum") |> step_subset_taxa(tax_level = "Kingdom", taxa = c("Bacteria", "Archaea")) |> step_filter_taxa(.f = "function(x) sum(x > 0) >= (0.4 * length(x))") rec ## Define step with default parameters and prep rec <- step_ancom(rec) |> prep(parallel = FALSE) rec ## Wearing rarefaction only for this step rec <- recipe(metaHIV_phy, "RiskGroup2", "Species") |> step_ancom(rarefy = TRUE) recdata(metaHIV_phy) ## Init Recipe rec <- recipe(metaHIV_phy, "RiskGroup2", "Phylum") |> step_subset_taxa(tax_level = "Kingdom", taxa = c("Bacteria", "Archaea")) |> step_filter_taxa(.f = "function(x) sum(x > 0) >= (0.4 * length(x))") rec ## Define step with default parameters and prep rec <- step_ancom(rec) |> prep(parallel = FALSE) rec ## Wearing rarefaction only for this step rec <- recipe(metaHIV_phy, "RiskGroup2", "Species") |> step_ancom(rarefy = TRUE) rec
Corncob an individual taxon regression model that uses abundance tables and sample data. corncob is able to model differential abundance and differential variability, and addresses each of the challenges presented below:
step_corncob( rec, phi.formula = stats::formula(~1), formula_null = stats::formula(~1), phi.formula_null = stats::formula(~1), link = "logit", phi.link = "logit", test = "Wald", boot = FALSE, B = 1000, filter_discriminant = TRUE, fdr_cutoff = 0.05, fdr = "fdr", log2FC = 0, rarefy = FALSE, id = rand_id("corncob") ) ## S4 method for signature 'Recipe' step_corncob( rec, phi.formula = stats::formula(~1), formula_null = stats::formula(~1), phi.formula_null = stats::formula(~1), link = "logit", phi.link = "logit", test = "Wald", boot = FALSE, B = 1000, filter_discriminant = TRUE, fdr_cutoff = 0.05, fdr = "fdr", log2FC = 0, rarefy = FALSE, id = rand_id("corncob") ) ## S4 method for signature 'PrepRecipe' step_corncob( rec, phi.formula = stats::formula(~1), formula_null = stats::formula(~1), phi.formula_null = stats::formula(~1), link = "logit", phi.link = "logit", test = "Wald", boot = FALSE, B = 1000, filter_discriminant = TRUE, fdr_cutoff = 0.05, fdr = "fdr", log2FC = 0, rarefy = FALSE, id = rand_id("corncob") )step_corncob( rec, phi.formula = stats::formula(~1), formula_null = stats::formula(~1), phi.formula_null = stats::formula(~1), link = "logit", phi.link = "logit", test = "Wald", boot = FALSE, B = 1000, filter_discriminant = TRUE, fdr_cutoff = 0.05, fdr = "fdr", log2FC = 0, rarefy = FALSE, id = rand_id("corncob") ) ## S4 method for signature 'Recipe' step_corncob( rec, phi.formula = stats::formula(~1), formula_null = stats::formula(~1), phi.formula_null = stats::formula(~1), link = "logit", phi.link = "logit", test = "Wald", boot = FALSE, B = 1000, filter_discriminant = TRUE, fdr_cutoff = 0.05, fdr = "fdr", log2FC = 0, rarefy = FALSE, id = rand_id("corncob") ) ## S4 method for signature 'PrepRecipe' step_corncob( rec, phi.formula = stats::formula(~1), formula_null = stats::formula(~1), phi.formula_null = stats::formula(~1), link = "logit", phi.link = "logit", test = "Wald", boot = FALSE, B = 1000, filter_discriminant = TRUE, fdr_cutoff = 0.05, fdr = "fdr", log2FC = 0, rarefy = FALSE, id = rand_id("corncob") )
rec |
A Recipe object. The step will be added to the sequence of operations for this Recipe. |
phi.formula |
An object of class formula without the response: a symbolic description of the model to be fitted to the dispersion. |
formula_null |
Formula for mean under null, without response. |
phi.formula_null |
Formula for overdispersion under null, without response. |
link |
Link function for abundance covariates, defaults to "logit". |
phi.link |
Link function for dispersion covariates, defaults to "logit". |
test |
Character. Hypothesis testing procedure to use. One of "Wald" or "LRT" (likelihood ratio test). |
boot |
Boolean. Defaults to FALSE. Indicator of whether or not to use parametric bootstrap algorithm. (See pbWald and pbLRT). |
B |
Optional integer. Number of bootstrap iterations. Ignored if boot is FALSE. Otherwise, defaults to 1000. |
filter_discriminant |
Boolean. Defaults to TRUE. If FALSE, discriminant taxa will not be filtered out. |
fdr_cutoff |
Integer. Defaults to 0.05. Desired type 1 error rate. |
fdr |
Character. Defaults to "fdr". False discovery rate control method, see p.adjust for more options. |
log2FC |
log2FC cutoff. |
rarefy |
Boolean indicating if OTU counts must be rarefyed. This rarefaction uses the standard R sample function to resample from the abundance values in the otu_table component of the first argument, physeq. Often one of the major goals of this procedure is to achieve parity in total number of counts between samples, as an alternative to other formal normalization procedures, which is why a single value for the sample.size is expected. If 'no_seed', rarefaction is performed without a set seed. |
id |
A character string that is unique to this step to identify it. |
different sequencing depth
excessive zeros from unobserved taxa
high variability of empirical relative abundances (overdispersion)
within-taxon correlation
hypothesis testing with categorical and continuous covariates
An object of class Recipe
Other Diff taxa steps:
step_aldex(),
step_ancom(),
step_deseq(),
step_lefse(),
step_maaslin(),
step_metagenomeseq(),
step_wilcox()
data(metaHIV_phy) ## Init Recipe rec <- recipe(metaHIV_phy, "RiskGroup2", "Phylum") |> step_subset_taxa(tax_level = "Kingdom", taxa = c("Bacteria", "Archaea")) |> step_filter_taxa(.f = "function(x) sum(x > 0) >= (0.3 * length(x))") rec ## Define step with default parameters and prep rec <- step_corncob(rec) |> prep(parallel = FALSE) recdata(metaHIV_phy) ## Init Recipe rec <- recipe(metaHIV_phy, "RiskGroup2", "Phylum") |> step_subset_taxa(tax_level = "Kingdom", taxa = c("Bacteria", "Archaea")) |> step_filter_taxa(.f = "function(x) sum(x > 0) >= (0.3 * length(x))") rec ## Define step with default parameters and prep rec <- step_corncob(rec) |> prep(parallel = FALSE) rec
Differential expression analysis based on the Negative Binomial (a.k.a.
Gamma-Poisson) distribution. This function performs a default analysis
through the steps: 1) estimation of size factors: estimateSizeFactors. 2)
estimation of dispersion: estimateDispersions. 3) Negative Binomial GLM
fitting and Wald statistics: nbinomWaldTest. For complete details on each
step, see the manual pages of the respective functions. After the DESeq
function returns a DESeqDataSet object, results tables (log2 fold changes and
p-values) can be generated using the results function. Shrunken LFC can then
be generated using the lfcShrink function.
step_deseq( rec, test = "Wald", fitType = "local", betaPrior = FALSE, type = "ashr", max_significance = 0.05, log2FC = 0, rarefy = FALSE, id = rand_id("deseq") ) ## S4 method for signature 'Recipe' step_deseq( rec, test = "Wald", fitType = "local", betaPrior = FALSE, type = "ashr", max_significance = 0.05, log2FC = 0, rarefy = FALSE, id = rand_id("deseq") ) ## S4 method for signature 'PrepRecipe' step_deseq( rec, test = "Wald", fitType = "local", betaPrior = FALSE, type = "ashr", max_significance = 0.05, log2FC = 0, rarefy = FALSE, id = rand_id("deseq") )step_deseq( rec, test = "Wald", fitType = "local", betaPrior = FALSE, type = "ashr", max_significance = 0.05, log2FC = 0, rarefy = FALSE, id = rand_id("deseq") ) ## S4 method for signature 'Recipe' step_deseq( rec, test = "Wald", fitType = "local", betaPrior = FALSE, type = "ashr", max_significance = 0.05, log2FC = 0, rarefy = FALSE, id = rand_id("deseq") ) ## S4 method for signature 'PrepRecipe' step_deseq( rec, test = "Wald", fitType = "local", betaPrior = FALSE, type = "ashr", max_significance = 0.05, log2FC = 0, rarefy = FALSE, id = rand_id("deseq") )
rec |
A Recipe object. The step will be added to the sequence of operations for this Recipe. |
test |
Either "Wald" or "LRT", which will then use either Wald significance tests (defined by nbinomWaldTest), or the likelihood ratio test on the difference in deviance between a full and reduced model formula (defined by nbinomLRT). |
fitType |
either "parametric", "local", "mean", or "glmGamPoi" for the type of fitting of dispersions to the mean intensity. See estimateDispersions for description. |
betaPrior |
whether or not to put a zero-mean normal prior on the non-intercept coefficients See nbinomWaldTest for description of the calculation of the beta prior. In versions >=1.16, the default is set to FALSE, and shrunken LFCs are obtained afterwards using lfcShrink. |
type |
"apeglm" is the adaptive Student's t prior shrinkage estimator from the 'apeglm' package; "ashr" is the adaptive shrinkage estimator from the 'ashr' package, using a fitted mixture of normals prior - see the Stephens (2016) reference below for citation; "normal" is the 2014 DESeq2 shrinkage estimator using a Normal prior. |
max_significance |
The q-value threshold for significance. |
log2FC |
log2FC cutoff. |
rarefy |
Boolean indicating if OTU counts must be rarefyed. This rarefaction uses the standard R sample function to resample from the abundance values in the otu_table component of the first argument, physeq. Often one of the major goals of this procedure is to achieve parity in total number of counts between samples, as an alternative to other formal normalization procedures, which is why a single value for the sample.size is expected. If 'no_seed', rarefaction is performed without a set seed. |
id |
A character string that is unique to this step to identify it. |
An object of class Recipe
Other Diff taxa steps:
step_aldex(),
step_ancom(),
step_corncob(),
step_lefse(),
step_maaslin(),
step_metagenomeseq(),
step_wilcox()
data(metaHIV_phy) ## Init Recipe rec <- recipe(metaHIV_phy, "RiskGroup2", "Phylum") |> step_subset_taxa(tax_level = "Kingdom", taxa = c("Bacteria", "Archaea")) |> step_filter_taxa(.f = "function(x) sum(x > 0) >= (0.4 * length(x))") rec ## Define step with default parameters and prep rec <- step_deseq(rec) |> prep(parallel = FALSE) rec ## Wearing rarefaction only for this step rec <- recipe(metaHIV_phy, "RiskGroup2", "Species") |> step_deseq(rarefy = TRUE) recdata(metaHIV_phy) ## Init Recipe rec <- recipe(metaHIV_phy, "RiskGroup2", "Phylum") |> step_subset_taxa(tax_level = "Kingdom", taxa = c("Bacteria", "Archaea")) |> step_filter_taxa(.f = "function(x) sum(x > 0) >= (0.4 * length(x))") rec ## Define step with default parameters and prep rec <- step_deseq(rec) |> prep(parallel = FALSE) rec ## Wearing rarefaction only for this step rec <- recipe(metaHIV_phy, "RiskGroup2", "Species") |> step_deseq(rarefy = TRUE) rec
This is a convenience wrapper around the filter_taxa function. It is intended to speed up filtering complex experimental objects with one function call. In the case of filter_by_abundance, the filtering will be based on the relative abundance of each taxon. The taxa retained in the dataset are those where the sum of their abundance is greater than the product of the total abundance and the provided threshold.
step_filter_by_abundance( rec, threshold = 0.01, id = rand_id("filter_by_abundance") ) ## S4 method for signature 'Recipe' step_filter_by_abundance( rec, threshold = 0.01, id = rand_id("filter_by_abundance") ) ## S4 method for signature 'PrepRecipe' step_filter_by_abundance( rec, threshold = 0.01, id = rand_id("filter_by_abundance") )step_filter_by_abundance( rec, threshold = 0.01, id = rand_id("filter_by_abundance") ) ## S4 method for signature 'Recipe' step_filter_by_abundance( rec, threshold = 0.01, id = rand_id("filter_by_abundance") ) ## S4 method for signature 'PrepRecipe' step_filter_by_abundance( rec, threshold = 0.01, id = rand_id("filter_by_abundance") )
rec |
A Recipe object. The step will be added to the sequence of operations for this Recipe. |
threshold |
The relative abundance threshold for filtering taxa, expressed as a proportion of the total abundance. For example, a threshold of 0.01 means that a taxon must make up at least 1% of the total abundance to be retained. The default value is 0.01. |
id |
A character string that is unique to this step to identify it. |
The function calculates the total abundance of all taxa in the phyloseq object. It then compares this total abundance to the abundance of each individual taxon. If a taxon's abundance is less than the threshold times the total abundance, that taxon is removed from the phyloseq object.
A Recipe object that has been filtered based on abundance.
This function modifies rec in place, you might want to make a copy of
rec before modifying it if you need to preserve the original object.
Other filter phy steps:
step_filter_by_prevalence(),
step_filter_by_rarity(),
step_filter_by_variance(),
step_filter_taxa()
data(metaHIV_phy) ## Init Recipe rec <- recipe(metaHIV_phy, "RiskGroup2", "Phylum") rec ## Define filter_by_abundance step with default parameters rec <- step_filter_by_abundance(rec, threshold = 0.01) recdata(metaHIV_phy) ## Init Recipe rec <- recipe(metaHIV_phy, "RiskGroup2", "Phylum") rec ## Define filter_by_abundance step with default parameters rec <- step_filter_by_abundance(rec, threshold = 0.01) rec
This is a convenience function around the filter_taxa function. It is designed to speed up filtering complex experimental objects with one function call. In the case of run_filter_by_prevalence, the filtering will be based on the prevalence of each taxon. The taxa retained in the dataset are those where the prevalence is greater than the provided threshold.
step_filter_by_prevalence( rec, threshold = 0.01, id = rand_id("filter_by_prevalence") ) ## S4 method for signature 'Recipe' step_filter_by_prevalence( rec, threshold = 0.01, id = rand_id("filter_by_prevalence") ) ## S4 method for signature 'PrepRecipe' step_filter_by_prevalence( rec, threshold = 0.01, id = rand_id("filter_by_prevalence") )step_filter_by_prevalence( rec, threshold = 0.01, id = rand_id("filter_by_prevalence") ) ## S4 method for signature 'Recipe' step_filter_by_prevalence( rec, threshold = 0.01, id = rand_id("filter_by_prevalence") ) ## S4 method for signature 'PrepRecipe' step_filter_by_prevalence( rec, threshold = 0.01, id = rand_id("filter_by_prevalence") )
rec |
A Recipe object. The step will be added to the sequence of operations for this Recipe. |
threshold |
The prevalence threshold for filtering taxa, expressed as a proportion of the total number of samples. For example, a threshold of 0.01 means that a taxon must be present in at least 1% of the samples to be retained. The default value is 0.01. |
id |
A character string that is unique to this step to identify it. |
The function calculates the prevalence of all taxa in the phyloseq object as the proportion of samples in which they are present. It then compares this prevalence to the threshold. If a taxon's prevalence is less than the threshold, that taxon is removed from the phyloseq object.
A Recipe object that has been filtered based on prevalence.
This function modifies rec in place, you might want to make a copy of
rec before modifying it if you need to preserve the original object.
Other filter phy steps:
step_filter_by_abundance(),
step_filter_by_rarity(),
step_filter_by_variance(),
step_filter_taxa()
data(metaHIV_phy) ## Init Recipe rec <- recipe(metaHIV_phy, "RiskGroup2", "Phylum") rec ## Define step_filter_by_prevalence step with default parameters rec <- step_filter_by_prevalence(rec, threshold = 0.01) recdata(metaHIV_phy) ## Init Recipe rec <- recipe(metaHIV_phy, "RiskGroup2", "Phylum") rec ## Define step_filter_by_prevalence step with default parameters rec <- step_filter_by_prevalence(rec, threshold = 0.01) rec
This is a convenience function around the filter_taxa function. It is designed to speed up filtering complex experimental objects with one function call. In the case of run_filter_by_rarity, the filtering will be based on the rarity of each taxon. The taxa retained in the dataset are those where the sum of their rarity is less than the provided threshold.
step_filter_by_rarity(rec, threshold = 0.01, id = rand_id("filter_by_rarity")) ## S4 method for signature 'Recipe' step_filter_by_rarity(rec, threshold = 0.01, id = rand_id("filter_by_rarity")) ## S4 method for signature 'PrepRecipe' step_filter_by_rarity(rec, threshold = 0.01, id = rand_id("filter_by_rarity"))step_filter_by_rarity(rec, threshold = 0.01, id = rand_id("filter_by_rarity")) ## S4 method for signature 'Recipe' step_filter_by_rarity(rec, threshold = 0.01, id = rand_id("filter_by_rarity")) ## S4 method for signature 'PrepRecipe' step_filter_by_rarity(rec, threshold = 0.01, id = rand_id("filter_by_rarity"))
rec |
A Recipe object. The step will be added to the sequence of operations for this Recipe. |
threshold |
The rarity threshold for filtering taxa, expressed as a proportion of the total number of samples. For example, a threshold of 0.01 means that a taxon must be present in less than 1% of the samples to be retained. The default value is 0.01. |
id |
A character string that is unique to this step to identify it. |
The function calculates the rarity of all taxa in the phyloseq object as the proportion of samples in which they are present. It then compares this rarity to the threshold. If a taxon's rarity is greater than the threshold, that taxon is removed from the phyloseq object.
A Recipe object that has been filtered based on rarity.
This function modifies rec in place, you might want to make a copy of
rec before modifying it if you need to preserve the original object.
Other filter phy steps:
step_filter_by_abundance(),
step_filter_by_prevalence(),
step_filter_by_variance(),
step_filter_taxa()
data(metaHIV_phy) ## Init Recipe rec <- recipe(metaHIV_phy, "RiskGroup2", "Phylum") rec ## Define step_filter_by_rarity step with default parameters rec <- step_filter_by_rarity(rec, threshold = 0.01) recdata(metaHIV_phy) ## Init Recipe rec <- recipe(metaHIV_phy, "RiskGroup2", "Phylum") rec ## Define step_filter_by_rarity step with default parameters rec <- step_filter_by_rarity(rec, threshold = 0.01) rec
This is a convenience function around the filter_taxa function. It is designed to speed up filtering complex experimental objects with one function call. In the case of run_filter_by_variance, the filtering will be based on the variance of each taxon. The taxa retained in the dataset are those where the variance of their abundance is greater than the provided threshold.
step_filter_by_variance( rec, threshold = 0.01, id = rand_id("filter_by_variance") ) ## S4 method for signature 'Recipe' step_filter_by_variance( rec, threshold = 0.01, id = rand_id("filter_by_variance") ) ## S4 method for signature 'PrepRecipe' step_filter_by_variance( rec, threshold = 0.01, id = rand_id("filter_by_variance") )step_filter_by_variance( rec, threshold = 0.01, id = rand_id("filter_by_variance") ) ## S4 method for signature 'Recipe' step_filter_by_variance( rec, threshold = 0.01, id = rand_id("filter_by_variance") ) ## S4 method for signature 'PrepRecipe' step_filter_by_variance( rec, threshold = 0.01, id = rand_id("filter_by_variance") )
rec |
A Recipe object. The step will be added to the sequence of operations for this Recipe. |
threshold |
The variance threshold for filtering taxa. The default value is 0.01. |
id |
A character string that is unique to this step to identify it. |
The function calculates the variance of all taxa in the phyloseq object. It then compares this variance to the variance of each individual taxon. If a taxon's variance is less than the threshold, that taxon is removed from the phyloseq object.
A Recipe object that has been filtered based on variance.
This function modifies rec in place, you might want to make a copy of
rec before modifying it if you need to preserve the original object.
Other filter phy steps:
step_filter_by_abundance(),
step_filter_by_prevalence(),
step_filter_by_rarity(),
step_filter_taxa()
data(metaHIV_phy) ## Init Recipe rec <- recipe(metaHIV_phy, "RiskGroup2", "Phylum") rec ## Define step_filter_by_variance step with default parameters rec <- step_filter_by_variance(rec, threshold = 0.01) recdata(metaHIV_phy) ## Init Recipe rec <- recipe(metaHIV_phy, "RiskGroup2", "Phylum") rec ## Define step_filter_by_variance step with default parameters rec <- step_filter_by_variance(rec, threshold = 0.01) rec
This function is directly analogous to the genefilter function for microarray filtering, but is used for filtering OTUs from phyloseq objects. It applies an arbitrary set of functions — as a function list, for instance, created by filterfun — as across-sample criteria, one OTU at a time. It takes as input a phyloseq object, and returns a logical vector indicating whether or not each OTU passed the criteria. Alternatively, if the "prune" option is set to FALSE, it returns the already-trimmed version of the phyloseq object.
step_filter_taxa(rec, .f, id = rand_id("filter_taxa")) ## S4 method for signature 'Recipe' step_filter_taxa(rec, .f, id = rand_id("filter_taxa"))step_filter_taxa(rec, .f, id = rand_id("filter_taxa")) ## S4 method for signature 'Recipe' step_filter_taxa(rec, .f, id = rand_id("filter_taxa"))
rec |
A Recipe object. The step will be added to the sequence of operations for this Recipe. |
.f |
A function or list of functions that take a vector of abundance values and return a logical. Some canned useful function types are included in the genefilter-package. |
id |
A character string that is unique to this step to identify it. |
An object of class Recipe
Other filter phy steps:
step_filter_by_abundance(),
step_filter_by_prevalence(),
step_filter_by_rarity(),
step_filter_by_variance()
data(metaHIV_phy) ## Init Recipe rec <- recipe(metaHIV_phy, "RiskGroup2", "Phylum") rec ## Define filter taxa step with default parameters rec <- step_filter_taxa(rec, .f = "function(x) sum(x > 0) >= (0.03 * length(x))") recdata(metaHIV_phy) ## Init Recipe rec <- recipe(metaHIV_phy, "RiskGroup2", "Phylum") rec ## Define filter taxa step with default parameters rec <- step_filter_taxa(rec, .f = "function(x) sum(x > 0) >= (0.03 * length(x))") rec
Lefser is metagenomic biomarker discovery tool that is based on LEfSe tool and is published by Huttenhower et al. 2011. Lefser is the R implementation of the LEfSe method. Using statistical analyses, lefser compares microbial populations of healthy and diseased subjects to discover differencially expressed microorganisms. Lefser than computes effect size, which estimates magnitude of differential expression between the populations for each differentially expressed microorganism. Subclasses of classes can also be assigned and used within the analysis.
step_lefse( rec, kruskal.threshold = 0.05, wilcox.threshold = 0.05, lda.threshold = 2, blockCol = NULL, assay = 1L, trim.names = FALSE, rarefy = TRUE, id = rand_id("lefse") ) ## S4 method for signature 'Recipe' step_lefse( rec, kruskal.threshold = 0.05, wilcox.threshold = 0.05, lda.threshold = 2, blockCol = NULL, assay = 1L, trim.names = FALSE, rarefy = TRUE, id = rand_id("lefse") ) ## S4 method for signature 'PrepRecipe' step_lefse( rec, kruskal.threshold = 0.05, wilcox.threshold = 0.05, lda.threshold = 2, blockCol = NULL, assay = 1L, trim.names = FALSE, rarefy = TRUE, id = rand_id("lefse") )step_lefse( rec, kruskal.threshold = 0.05, wilcox.threshold = 0.05, lda.threshold = 2, blockCol = NULL, assay = 1L, trim.names = FALSE, rarefy = TRUE, id = rand_id("lefse") ) ## S4 method for signature 'Recipe' step_lefse( rec, kruskal.threshold = 0.05, wilcox.threshold = 0.05, lda.threshold = 2, blockCol = NULL, assay = 1L, trim.names = FALSE, rarefy = TRUE, id = rand_id("lefse") ) ## S4 method for signature 'PrepRecipe' step_lefse( rec, kruskal.threshold = 0.05, wilcox.threshold = 0.05, lda.threshold = 2, blockCol = NULL, assay = 1L, trim.names = FALSE, rarefy = TRUE, id = rand_id("lefse") )
rec |
A Recipe object. The step will be added to the sequence of operations for this Recipe. |
kruskal.threshold |
numeric(1) The p-value for the Kruskal-Wallis Rank Sum Test (default 0.05). |
wilcox.threshold |
numeric(1) The p-value for the Wilcoxon Rank-Sum Test when 'blockCol' is present (default 0.05). |
lda.threshold |
numeric(1) The effect size threshold (default 2.0). |
blockCol |
character(1) Optional column name in 'colData(expr)' indicating the blocks, usually a factor with two levels (e.g., 'c("adult", "senior")'; default NULL). |
assay |
The i-th assay matrix in the ‘SummarizedExperiment' (’expr'; default 1). |
trim.names |
If 'TRUE' extracts the most specific taxonomic rank of organism. |
rarefy |
Boolean indicating if OTU counts must be rarefyed. This rarefaction uses the standard R sample function to resample from the abundance values in the otu_table component of the first argument, physeq. Often one of the major goals of this procedure is to achieve parity in total number of counts between samples, as an alternative to other formal normalization procedures, which is why a single value for the sample.size is expected. If 'no_seed', rarefaction is performed without a set seed. |
id |
A character string that is unique to this step to identify it. |
An object of class Recipe
Other Diff taxa steps:
step_aldex(),
step_ancom(),
step_corncob(),
step_deseq(),
step_maaslin(),
step_metagenomeseq(),
step_wilcox()
data(metaHIV_phy) ## Init Recipe rec <- recipe(metaHIV_phy, "RiskGroup2", "Phylum") |> step_subset_taxa(tax_level = "Kingdom", taxa = c("Bacteria", "Archaea")) |> step_filter_taxa(.f = "function(x) sum(x > 0) >= (0.3 * length(x))") rec ## Define step with default parameters rec <- step_lefse(rec) rec ## Running lefse without rarefaction (not recommended) rec <- recipe(metaHIV_phy, "RiskGroup2", "Species") |> step_lefse(rarefy = FALSE) recdata(metaHIV_phy) ## Init Recipe rec <- recipe(metaHIV_phy, "RiskGroup2", "Phylum") |> step_subset_taxa(tax_level = "Kingdom", taxa = c("Bacteria", "Archaea")) |> step_filter_taxa(.f = "function(x) sum(x > 0) >= (0.3 * length(x))") rec ## Define step with default parameters rec <- step_lefse(rec) rec ## Running lefse without rarefaction (not recommended) rec <- recipe(metaHIV_phy, "RiskGroup2", "Species") |> step_lefse(rarefy = FALSE) rec
MaAsLin2 finds associations between microbiome meta-omics features and complex metadata in population-scale epidemiological studies. The software includes multiple analysis methods (including support for multiple covariates and repeated measures), filtering, normalization, and transform options to customize analysis for your specific study.
step_maaslin( rec, min_abundance = 0, min_prevalence = 0.1, min_variance = 0, normalization = "TSS", transform = "LOG", analysis_method = "LM", max_significance = 0.25, random_effects = NULL, correction = "BH", standardize = TRUE, reference = NULL, rarefy = FALSE, id = rand_id("maaslin") ) ## S4 method for signature 'Recipe' step_maaslin( rec, min_abundance = 0, min_prevalence = 0.1, min_variance = 0, normalization = "TSS", transform = "LOG", analysis_method = "LM", max_significance = 0.25, random_effects = NULL, correction = "BH", standardize = TRUE, reference = NULL, rarefy = FALSE, id = rand_id("maaslin") ) ## S4 method for signature 'PrepRecipe' step_maaslin( rec, min_abundance = 0, min_prevalence = 0.1, min_variance = 0, normalization = "TSS", transform = "LOG", analysis_method = "LM", max_significance = 0.25, random_effects = NULL, correction = "BH", standardize = TRUE, reference = NULL, rarefy = FALSE, id = rand_id("maaslin") )step_maaslin( rec, min_abundance = 0, min_prevalence = 0.1, min_variance = 0, normalization = "TSS", transform = "LOG", analysis_method = "LM", max_significance = 0.25, random_effects = NULL, correction = "BH", standardize = TRUE, reference = NULL, rarefy = FALSE, id = rand_id("maaslin") ) ## S4 method for signature 'Recipe' step_maaslin( rec, min_abundance = 0, min_prevalence = 0.1, min_variance = 0, normalization = "TSS", transform = "LOG", analysis_method = "LM", max_significance = 0.25, random_effects = NULL, correction = "BH", standardize = TRUE, reference = NULL, rarefy = FALSE, id = rand_id("maaslin") ) ## S4 method for signature 'PrepRecipe' step_maaslin( rec, min_abundance = 0, min_prevalence = 0.1, min_variance = 0, normalization = "TSS", transform = "LOG", analysis_method = "LM", max_significance = 0.25, random_effects = NULL, correction = "BH", standardize = TRUE, reference = NULL, rarefy = FALSE, id = rand_id("maaslin") )
rec |
A Recipe object. The step will be added to the sequence of operations for this Recipe. |
min_abundance |
The minimum abundance for each feature. |
min_prevalence |
The minimum percent of samples for which a feature is detected at minimum abundance. |
min_variance |
Keep features with variance greater than. |
normalization |
The normalization method to apply. Default: "TSS". Choices: "TSS", "CLR", "CSS", "NONE", "TMM". |
transform |
The transform to apply. Default: "LOG". Choices: "LOG", "LOGIT", "AST", "NONE". |
analysis_method |
The analysis method to apply. Default: "LM". Choices: "LM", "CPLM", "ZICP", "NEGBIN", "ZINB". |
max_significance |
The q-value threshold for significance. |
random_effects |
The random effects for the model, comma-delimited for multiple effects. |
correction |
The correction method for computing the q-value. |
standardize |
Apply z-score so continuous metadata are on the same scale. |
reference |
The factor to use as a reference for a variable with more than two levels provided as a string of 'variable,reference' semi-colon delimited for multiple variables. |
rarefy |
Boolean indicating if OTU counts must be rarefyed. This rarefaction uses the standard R sample function to resample from the abundance values in the otu_table component of the first argument, physeq. Often one of the major goals of this procedure is to achieve parity in total number of counts between samples, as an alternative to other formal normalization procedures, which is why a single value for the sample.size is expected. If 'no_seed', rarefaction is performed without a set seed. |
id |
A character string that is unique to this step to identify it. |
An object of class Recipe
Other Diff taxa steps:
step_aldex(),
step_ancom(),
step_corncob(),
step_deseq(),
step_lefse(),
step_metagenomeseq(),
step_wilcox()
data(metaHIV_phy) ## Init Recipe rec <- recipe(metaHIV_phy, "RiskGroup2", "Phylum") |> step_subset_taxa(tax_level = "Kingdom", taxa = c("Bacteria", "Archaea")) |> step_filter_taxa(.f = "function(x) sum(x > 0) >= (0.4 * length(x))") rec ## Define step with default parameters and prep rec <- step_maaslin(rec) |> prep(parallel = FALSE) rec ## Wearing rarefaction only for this step rec <- recipe(metaHIV_phy, "RiskGroup2", "Species") |> step_maaslin(rarefy = TRUE) recdata(metaHIV_phy) ## Init Recipe rec <- recipe(metaHIV_phy, "RiskGroup2", "Phylum") |> step_subset_taxa(tax_level = "Kingdom", taxa = c("Bacteria", "Archaea")) |> step_filter_taxa(.f = "function(x) sum(x > 0) >= (0.4 * length(x))") rec ## Define step with default parameters and prep rec <- step_maaslin(rec) |> prep(parallel = FALSE) rec ## Wearing rarefaction only for this step rec <- recipe(metaHIV_phy, "RiskGroup2", "Species") |> step_maaslin(rarefy = TRUE) rec
metagenomeSeq is designed to determine features (be it Operational Taxanomic Unit (OTU), species, etc.) that are differentially abundant between two or more groups of multiple samples. metagenomeSeq is designed to address the effects of both normalization and under-sampling of microbial communities on disease association detection and the testing of feature correlations.
step_metagenomeseq( rec, zeroMod = NULL, useCSSoffset = TRUE, useMixedModel = FALSE, max_significance = 0.05, log2FC = 0, rarefy = FALSE, rm_zeros = 0, id = rand_id("metagenomeseq") ) ## S4 method for signature 'Recipe' step_metagenomeseq( rec, zeroMod = NULL, useCSSoffset = TRUE, useMixedModel = FALSE, max_significance = 0.05, log2FC = 0, rarefy = FALSE, rm_zeros = 0, id = rand_id("metagenomeseq") ) ## S4 method for signature 'PrepRecipe' step_metagenomeseq( rec, zeroMod = NULL, useCSSoffset = TRUE, useMixedModel = FALSE, max_significance = 0.05, log2FC = 0, rarefy = FALSE, rm_zeros = 0, id = rand_id("metagenomeseq") )step_metagenomeseq( rec, zeroMod = NULL, useCSSoffset = TRUE, useMixedModel = FALSE, max_significance = 0.05, log2FC = 0, rarefy = FALSE, rm_zeros = 0, id = rand_id("metagenomeseq") ) ## S4 method for signature 'Recipe' step_metagenomeseq( rec, zeroMod = NULL, useCSSoffset = TRUE, useMixedModel = FALSE, max_significance = 0.05, log2FC = 0, rarefy = FALSE, rm_zeros = 0, id = rand_id("metagenomeseq") ) ## S4 method for signature 'PrepRecipe' step_metagenomeseq( rec, zeroMod = NULL, useCSSoffset = TRUE, useMixedModel = FALSE, max_significance = 0.05, log2FC = 0, rarefy = FALSE, rm_zeros = 0, id = rand_id("metagenomeseq") )
rec |
A Recipe object. The step will be added to the sequence of operations for this Recipe. |
zeroMod |
The zero model, the model to account for the change in the number of OTUs observed as a linear effect of the depth of coverage. |
useCSSoffset |
Boolean, whether to include the default scaling parameters in the model or not. |
useMixedModel |
Estimate the correlation between duplicate features or replicates using duplicateCorrelation. |
max_significance |
The q-value threshold for significance. |
log2FC |
log2FC cutoff. |
rarefy |
Boolean indicating if OTU counts must be rarefyed. This rarefaction uses the standard R sample function to resample from the abundance values in the otu_table component of the first argument, physeq. Often one of the major goals of this procedure is to achieve parity in total number of counts between samples, as an alternative to other formal normalization procedures, which is why a single value for the sample.size is expected. If 'no_seed', rarefaction is performed without a set seed. |
rm_zeros |
Proportion of samples of the same categorical level with more than 0 counts. |
id |
A character string that is unique to this step to identify it. |
An object of class Recipe
Other Diff taxa steps:
step_aldex(),
step_ancom(),
step_corncob(),
step_deseq(),
step_lefse(),
step_maaslin(),
step_wilcox()
data(metaHIV_phy) ## Init Recipe rec <- recipe(metaHIV_phy, "RiskGroup2", "Phylum") |> step_subset_taxa(tax_level = "Kingdom", taxa = c("Bacteria", "Archaea")) |> step_filter_taxa(.f = "function(x) sum(x > 0) >= (0.02 * length(x))") rec ## Define step with default parameters and prep rec <- step_metagenomeseq(rec, rm_zeros = 0.01) |> prep(parallel = FALSE) rec ## Wearing rarefaction only for this step rec <- recipe(metaHIV_phy, "RiskGroup2", "Species") |> step_metagenomeseq(rarefy = TRUE) recdata(metaHIV_phy) ## Init Recipe rec <- recipe(metaHIV_phy, "RiskGroup2", "Phylum") |> step_subset_taxa(tax_level = "Kingdom", taxa = c("Bacteria", "Archaea")) |> step_filter_taxa(.f = "function(x) sum(x > 0) >= (0.02 * length(x))") rec ## Define step with default parameters and prep rec <- step_metagenomeseq(rec, rm_zeros = 0.01) |> prep(parallel = FALSE) rec ## Wearing rarefaction only for this step rec <- recipe(metaHIV_phy, "RiskGroup2", "Species") |> step_metagenomeseq(rarefy = TRUE) rec
Please note that the authors of phyloseq do not advocate using this as a normalization procedure, despite its recent popularity. Our justifications for using alternative approaches to address disparities in library sizes have been made available as an article in PLoS Computational Biology. See phyloseq_to_deseq2 for a recommended alternative to rarefying directly supported in the phyloseq package, as well as the supplemental materials for the PLoS-CB article and the phyloseq extensions repository on GitHub. Nevertheless, for comparison and demonstration, the rarefying procedure is implemented here in good faith and with options we hope are useful. This function uses the standard R sample function to resample from the abundance values in the otu_table component of the first argument, physeq. Often one of the major goals of this procedure is to achieve parity in total number of counts between samples, as an alternative to other formal normalization procedures, which is why a single value for the sample.size is expected. This kind of resampling can be performed with and without replacement, with replacement being the more computationally-efficient, default setting. See the replace parameter documentation for more details. We recommended that you explicitly select a random number generator seed before invoking this function, or, alternatively, that you explicitly provide a single positive integer argument as rngseed.
step_rarefaction(rec, id = rand_id("rarefaction")) ## S4 method for signature 'Recipe' step_rarefaction(rec, id = rand_id("rarefaction")) ## S4 method for signature 'PrepRecipe' step_rarefaction(rec, id = rand_id("rarefaction"))step_rarefaction(rec, id = rand_id("rarefaction")) ## S4 method for signature 'Recipe' step_rarefaction(rec, id = rand_id("rarefaction")) ## S4 method for signature 'PrepRecipe' step_rarefaction(rec, id = rand_id("rarefaction"))
rec |
A Recipe object. The step will be added to the sequence of operations for this Recipe. |
id |
A character string that is unique to this step to identify it. |
An object of class Recipe
data(metaHIV_phy) ## Init Recipe rec <- recipe(metaHIV_phy, var_info = "RiskGroup2", tax_info = "Phylum") |> step_subset_taxa(tax_level = "Kingdom", taxa = c("Bacteria", "Archaea")) |> step_filter_taxa(.f = "function(x) sum(x > 0) >= (0.03 * length(x))") rec ## Define step with default parameters and prep rec <- step_rarefaction(rec) recdata(metaHIV_phy) ## Init Recipe rec <- recipe(metaHIV_phy, var_info = "RiskGroup2", tax_info = "Phylum") |> step_subset_taxa(tax_level = "Kingdom", taxa = c("Bacteria", "Archaea")) |> step_filter_taxa(.f = "function(x) sum(x > 0) >= (0.03 * length(x))") rec ## Define step with default parameters and prep rec <- step_rarefaction(rec) rec
This is a convenience function around the subset_taxa function from the phyloseq package. It is designed to speed up subsetting complex experimental objects with one function call. In the case of run_subset_taxa, the subsetting will be based on the taxonomic level of each taxon. The taxa retained in the dataset are those where the taxonomic level matches the provided taxa.
step_subset_taxa(rec, tax_level, taxa, id = rand_id("subset_taxa")) ## S4 method for signature 'Recipe' step_subset_taxa(rec, tax_level, taxa, id = rand_id("subset_taxa")) ## S4 method for signature 'PrepRecipe' step_subset_taxa(rec, tax_level, taxa, id = rand_id("subset_taxa"))step_subset_taxa(rec, tax_level, taxa, id = rand_id("subset_taxa")) ## S4 method for signature 'Recipe' step_subset_taxa(rec, tax_level, taxa, id = rand_id("subset_taxa")) ## S4 method for signature 'PrepRecipe' step_subset_taxa(rec, tax_level, taxa, id = rand_id("subset_taxa"))
rec |
A Recipe object. The step will be added to the sequence of operations for this Recipe. |
tax_level |
The taxonomic level for subsetting taxa. |
taxa |
The taxa to be retained in the dataset. |
id |
A character string that is unique to this step to identify it. |
The function subsets the taxa in the phyloseq object based on the provided taxonomic level and taxa. Only the taxa that match the provided taxa at the given taxonomic level are retained in the phyloseq object.
A Recipe object that has been subsetted based on taxonomic level.
This function modifies rec in place, you might want to make a copy of
rec before modifying it if you need to preserve the original object.
data(metaHIV_phy) ## Init Recipe rec <- recipe(metaHIV_phy, "RiskGroup2", "Species") rec ## Define step_subset_taxa step with default parameters rec <- step_subset_taxa( rec, tax_level = "Kingdom", taxa = c("Bacteria", "Archaea") ) recdata(metaHIV_phy) ## Init Recipe rec <- recipe(metaHIV_phy, "RiskGroup2", "Species") rec ## Define step_subset_taxa step with default parameters rec <- step_subset_taxa( rec, tax_level = "Kingdom", taxa = c("Bacteria", "Archaea") ) rec
Performs a wilcox test to determine features (be it Operational Taxanomic Unit (OTU), species, etc.) that are differentially abundant between two or more groups of multiple samples.
step_wilcox( rec, norm_method = "compositional", max_significance = 0.05, p_adj_method = "BH", rarefy = FALSE, id = rand_id("wilcox") ) ## S4 method for signature 'Recipe' step_wilcox( rec, norm_method = "compositional", max_significance = 0.05, p_adj_method = "BH", rarefy = FALSE, id = rand_id("wilcox") ) ## S4 method for signature 'PrepRecipe' step_wilcox( rec, norm_method = "compositional", max_significance = 0.05, p_adj_method = "BH", rarefy = FALSE, id = rand_id("wilcox") )step_wilcox( rec, norm_method = "compositional", max_significance = 0.05, p_adj_method = "BH", rarefy = FALSE, id = rand_id("wilcox") ) ## S4 method for signature 'Recipe' step_wilcox( rec, norm_method = "compositional", max_significance = 0.05, p_adj_method = "BH", rarefy = FALSE, id = rand_id("wilcox") ) ## S4 method for signature 'PrepRecipe' step_wilcox( rec, norm_method = "compositional", max_significance = 0.05, p_adj_method = "BH", rarefy = FALSE, id = rand_id("wilcox") )
rec |
A Recipe object. The step will be added to the sequence of operations for this Recipe. |
norm_method |
Transformation to apply. The options include: 'compositional' (ie relative abundance), 'Z', 'log10', 'log10p', 'hellinger', 'identity', 'clr', 'alr', or any method from the vegan::decostand function. |
max_significance |
The q-value threshold for significance. |
p_adj_method |
Character. Specifying the method to adjust p-values for multiple comparisons. Default is “BH” (Benjamini-Hochberg procedure). |
rarefy |
Boolean indicating if OTU counts must be rarefyed. This rarefaction uses the standard R sample function to resample from the abundance values in the otu_table component of the first argument, physeq. Often one of the major goals of this procedure is to achieve parity in total number of counts between samples, as an alternative to other formal normalization procedures, which is why a single value for the sample.size is expected. If 'no_seed', rarefaction is performed without a set seed. |
id |
A character string that is unique to this step to identify it. |
An object of class Recipe
Other Diff taxa steps:
step_aldex(),
step_ancom(),
step_corncob(),
step_deseq(),
step_lefse(),
step_maaslin(),
step_metagenomeseq()
data(metaHIV_phy) ## Init Recipe rec <- recipe(metaHIV_phy, "RiskGroup2", "Phylum") |> step_subset_taxa(tax_level = "Kingdom", taxa = c("Bacteria", "Archaea")) |> step_filter_taxa(.f = "function(x) sum(x > 0) >= (0.4 * length(x))") rec ## Define step with default parameters and prep rec <- step_wilcox(rec) |> prep(parallel = FALSE) rec ## Wearing rarefaction only for this step rec <- recipe(metaHIV_phy, "RiskGroup2", "Species") |> step_wilcox(rarefy = TRUE) recdata(metaHIV_phy) ## Init Recipe rec <- recipe(metaHIV_phy, "RiskGroup2", "Phylum") |> step_subset_taxa(tax_level = "Kingdom", taxa = c("Bacteria", "Archaea")) |> step_filter_taxa(.f = "function(x) sum(x > 0) >= (0.4 * length(x))") rec ## Define step with default parameters and prep rec <- step_wilcox(rec) |> prep(parallel = FALSE) rec ## Wearing rarefaction only for this step rec <- recipe(metaHIV_phy, "RiskGroup2", "Species") |> step_wilcox(rarefy = TRUE) rec
Get step_ids from recipe
steps_ids(rec, type = "all")steps_ids(rec, type = "all")
rec |
A Recipe object. |
type |
character vector indicating the type class. Options |
character vector
data(test_rec) ## We can extract the step identifiers from a Recipe with `step_ids` ids <- steps_ids(test_rec) ids ## With the `type` parameter, extract the prepro and da steps separately. da_ids <- steps_ids(test_rec, type = "da") da_ids prepro_ids <- steps_ids(test_rec, type = "prepro") prepro_idsdata(test_rec) ## We can extract the step identifiers from a Recipe with `step_ids` ids <- steps_ids(test_rec) ids ## With the `type` parameter, extract the prepro and da steps separately. da_ids <- steps_ids(test_rec, type = "da") da_ids prepro_ids <- steps_ids(test_rec, type = "prepro") prepro_ids
Extracts tax_table from phyloseq inside a Recipe
tax_table(rec) ## S4 method for signature 'Recipe' tax_table(rec)tax_table(rec) ## S4 method for signature 'Recipe' tax_table(rec)
rec |
A Recipe or Recipe step. |
A tibble
data(metaHIV_phy) ## Define recipe rec <- recipe(metaHIV_phy, var_info = "RiskGroup2", tax_info = "Species") ## Extract tax_table from phyloseq object tax_table(rec)data(metaHIV_phy) ## Define recipe rec <- recipe(metaHIV_phy, var_info = "RiskGroup2", tax_info = "Species") ## Extract tax_table from phyloseq object tax_table(rec)
A Recipe created for a metaHIV_phy object uning "Riskgroup2" as a var_info
and "Genus" as a tax_info. Also includes step_deseq, step_maaslin and
step_metagenomeSeq.
data("test_prep_rec")data("test_prep_rec")
A PrepRecipe object.
A Recipe created for a metaHIV_phy object uning "Riskgroup2" as a var_info
and "Genus" as a tax_info.
data("test_rec")data("test_rec")
A Recipe object.
Extract outs with all 0 values in at least on level of the variable
zero_otu(obj, var = NULL, pct_cutoff = 0) ## S4 method for signature 'Recipe' zero_otu(obj, var = NULL, pct_cutoff = 0) ## S4 method for signature 'phyloseq' zero_otu(obj, var = NULL, pct_cutoff = 0)zero_otu(obj, var = NULL, pct_cutoff = 0) ## S4 method for signature 'Recipe' zero_otu(obj, var = NULL, pct_cutoff = 0) ## S4 method for signature 'phyloseq' zero_otu(obj, var = NULL, pct_cutoff = 0)
obj |
A |
var |
Variable of interest. Must be present in the metadata. |
pct_cutoff |
Minimum of pct counts samples with counts for each taxa. |
character vector
data(metaHIV_phy) ## Init Recipe rec <- recipe(metaHIV_phy, "RiskGroup2", "Species") ## Extract outs with all 0 values zero_otu(rec)data(metaHIV_phy) ## Init Recipe rec <- recipe(metaHIV_phy, "RiskGroup2", "Species") ## Extract outs with all 0 values zero_otu(rec)