phyloseq-cheatsheet

Minimal cheatsheet for functions in the phyloseq R package.

Contents

Workflow
Class accessors
Code snippets
Insights
Useful Resources

Workflow

Attribution: McMurdie, Holmes (2013) https://doi.org/10.1371/journal.pone.0061217.g002

Class accessors

Attribution: McMurdie, Holmes (2013) https://doi.org/10.1371/journal.pone.0061217.g003

Code snippets

The table here is meant to be a quick look up on how to use common functions.

The table below has four columns:

Type = Input, Data, Wrangle, Plot
Function = Function as called in script
Description = Terse description of function
Example = Minimal code example to demonstrate function; will often make use of built-in data from phyloseq

Type	Function	Description	Example
Data	`enterotype`		`data(enterotype)`
Data	`GlobalPatterns`		`data(GlobalPatterns)`
Data	`esophagus`	Subset of esophageal	`data(esophagus)`
Wrangle	`psmelt`	Change to data frame	`psmelt(esophagus)`

Insights

`prune_` versus `subset_`

There is a subtle difference between the prune_* and subset_* functions.

The prune_ functions (e.g., prune_samples()) keep the set of observations based on the data in the phyloseq object itself, such as conditioning on one of the columns.

The subset_ functions (e.g., subset_samples()) keep the set of observations based auxillary data and/or evaluated expressions. In other words, it is a wrapper function around the base R subset() function.

Here are some examples:

# Source: https://joey711.github.io/phyloseq/preprocess.html
GP.chl <- subset_taxa(GlobalPatterns, Phylum == "Chlamydiae")
GP.chl <- prune_samples(sample_sums(GP.chl) >= 20, GP.chl)

# Subset using sample names themselves
GP.subset <- prune_samples(c("CL3", "CC1", "SV1"), GlobalPatters)

The subset_taxa() function uses the already present Phylum column in the taxonomy table to subset the data.

Meanwhile, the prune_samples() function uses

an expression that evaluates to TRUE/FALSE
a character vector of sample names

in order to subset the samples. This sort of expression could not be evaluated using the subset_samples() function.

See https://joey711.github.io/phyloseq/preprocess.html#preprocessing for more.

Using `subset_*` functions

The subset_* functions (for example, subset_samples()) uses base R's subset() function. The documentation of this function notes:

This is a convenience function intended for use interactively. For programming it is better to use the standard subsetting functions like [, and in particular the non-standard evaluation of argument subset can have unanticipated consequences.

In other words, the subset() function and phyloseq::subset_* function by association don't work well within functions.

To get around this, I have been using phyloseq::prune_samples() and having code prior to it to get the sample names I want based on a boolean check.

Note: doesn't run reproducibly (yet), but the sentiment remains.

# Get sample names
keep_samples <- sample_data(GlobalPatterns)$sample_name %in% c("CL3", "CC1", "SV1")

# Subset using sample names themselves
GP.subset <- prune_samples(keep_samples, GlobalPatters)

Source: https://www.rdocumentation.org/packages/base/versions/3.6.2/topics/subset

Extracting data frames into tibbles

When extracting data frames from phyloseq using accessors like sample_data() can end up with unintended consequences, as noted here.

In this example, the code shows various ways to access the sample data and converting it to a non-phyloseq data frame and looking at the dimensions of the object.

Notice the cases when the dimensions go from 26 x 7 to 8 x 8.

library(phyloseq)
library(magrittr)
library(tibble)

data("GlobalPatterns")

# Natural use but with phyloseq object
GlobalPatterns %>%
    sample_data %>%
    dim()
#> [1] 26  7

# Using base R
GlobalPatterns %>%
    sample_data %>%
    data.frame() %>%
    dim()
#> [1] 26  7

# Using base R "as" convention
GlobalPatterns %>%
    sample_data %>%
    as.data.frame() %>%
    dim()
#> [1] 26  7

# Use tibble convention
GlobalPatterns %>%
    sample_data() %>%
    as_tibble() %>%
    dim()
#> Warning in class(x) <- c(subclass, tibble_class): Setting class(x) to
#> multiple strings ("tbl_df", "tbl", ...); result will no longer be an S4
#> object
#> [1] 26  7

# Use tibble function to keep row names
GlobalPatterns %>%
    sample_data() %>%
    rownames_to_column() %>%
    dim()
#> [1] 8 8

# Using base R and row names to column
GlobalPatterns %>%
    sample_data %>%
    data.frame() %>%
    rownames_to_column() %>%
    dim()
#> [1] 26  8

# Using base R "as" convention and row names to column
GlobalPatterns %>%
    sample_data %>%
    as.data.frame() %>%
    rownames_to_column() %>%
    dim()
#> [1] 8 8

# Use tibble convention and row names to column
GlobalPatterns %>%
    sample_data() %>%
    as_tibble() %>%
    rownames_to_column() %>%
    dim()
#> Warning in class(x) <- c(subclass, tibble_class): Setting class(x) to
#> multiple strings ("tbl_df", "tbl", ...); result will no longer be an S4
#> object
#> [1] 26  8

^{Created on 2019-06-17 by the reprex package (v0.2.1)}

Creating new variables in data

A common task is to quickly create new variables in the data (e.g., sample data).

Instead of extracting the entire data and the reassigning it, you can simply access the variable itself directly using the $ accessor.

# Load phyloseq and data
library(phyloseq)
data("enterotype")
enterotype
#> phyloseq-class experiment-level object
#> otu_table()   OTU Table:         [ 553 taxa and 280 samples ]
#> sample_data() Sample Data:       [ 280 samples by 9 sample variables ]
#> tax_table()   Taxonomy Table:    [ 553 taxa by 1 taxonomic ranks ]

# See variables we can change
head(sample_data(enterotype))
#>           Enterotype Sample_ID SeqTech  SampleID     Project Nationality Gender
#> AM.AD.1         <NA>   AM.AD.1  Sanger   AM.AD.1      gill06    american      F
#> AM.AD.2         <NA>   AM.AD.2  Sanger   AM.AD.2      gill06    american      M
#> AM.F10.T1       <NA> AM.F10.T1  Sanger AM.F10.T1 turnbaugh09    american      F
#> AM.F10.T2          3 AM.F10.T2  Sanger AM.F10.T2 turnbaugh09    american      F
#> DA.AD.1            2   DA.AD.1  Sanger   DA.AD.1     MetaHIT      danish      F
#> DA.AD.1T        <NA>  DA.AD.1T  Sanger      <NA>        <NA>        <NA>   <NA>
#>           Age ClinicalStatus
#> AM.AD.1    28        healthy
#> AM.AD.2    37        healthy
#> AM.F10.T1  NA          obese
#> AM.F10.T2  NA          obese
#> DA.AD.1    59        healthy
#> DA.AD.1T   NA           <NA>

# Change variable
sample_data(enterotype)$Over30 <- sample_data(enterotype)$Age > 30

# See changes
head(sample_data(enterotype)[, c("Age", "Over30")])
#>           Age Over30
#> AM.AD.1    28  FALSE
#> AM.AD.2    37   TRUE
#> AM.F10.T1  NA     NA
#> AM.F10.T2  NA     NA
#> DA.AD.1    59   TRUE
#> DA.AD.1T   NA     NA

^{Created on 2020-09-10 by the reprex package (v0.3.0)}

Useful Resources

phyloseq Official Website
DIY: Public Restroom Bacteria
Vignette for phyloseq: Analysis of high-throughput microbiome census data

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README.md

README.md

phyloseq-cheatsheet

Workflow

Class accessors

Code snippets

Insights

`prune_` versus `subset_`

Using `subset_*` functions

Extracting data frames into tibbles

Creating new variables in data

Useful Resources

License

Files

README.md

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README.md

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phyloseq-cheatsheet

Workflow

Class accessors

Code snippets

Insights

prune_* versus subset_*

Using subset_* functions

Extracting data frames into tibbles

Creating new variables in data

Useful Resources

License

`prune_` versus `subset_`

Using `subset_*` functions