Filter phyloseq objects to keep only shared samples and/or taxa

Filters each phyloseq object in a list_phyloseq to retain only the samples and/or taxa that are common across all objects. This is useful for making direct comparisons on a common basis.

Usage

filter_common_lpq(
  x,
  filter_samples = TRUE,
  filter_taxa = FALSE,
  clean = TRUE,
  verbose = TRUE
)

Arguments

x: (required) A list_phyloseq object.
filter_samples: (logical, default TRUE) If TRUE, filter to keep only samples present in all phyloseq objects.
filter_taxa: (logical, default FALSE) If TRUE, filter to keep only taxa present in all phyloseq objects.
clean: (logical, default TRUE) If TRUE, apply MiscMetabar::clean_pq() after filtering to remove empty samples/taxa.
verbose: (logical, default TRUE) If TRUE, print information about the filtering process.

Value

A new list_phyloseq object with filtered phyloseq objects

Details

This function is particularly useful for:

NESTED_ROBUSTNESS comparisons: filter to common samples when comparing original vs rarefied data
EXPLORATION comparisons: filter to common samples/taxa when comparing different sample groups
Any comparison where you need to ensure all phyloseq objects contain the same samples or taxa

Examples

lpq <- list_phyloseq(list(
  original = data_fungi,
  mini = data_fungi_mini,
  rarefied = rarefy_even_depth(data_fungi)
))
#> You set `rngseed` to FALSE. Make sure you've set & recorded
#>  the random seed of your session for reproducibility.
#> See `?set.seed`
#> ...
#> 1007OTUs were removed because they are no longer 
#> present in any sample after random subsampling
#> ...
#> ℹ Building summary table for 3 phyloseq objects...
#> ℹ Computing comparison characteristics...
#> ℹ Checking sample and taxa overlap...
#> ℹ Detected comparison type: NESTED_ROBUSTNESS
#> ℹ 137 common samples, 42 common taxa
#> ✔ list_phyloseq created (NESTED_ROBUSTNESS)

# Filter to keep only common samples (useful for nested comparisons)
lpq_filtered <- filter_common_lpq(lpq, filter_samples = TRUE, verbose = FALSE)
#> Cleaning suppress 10 taxa and 0 samples.
#> Cleaning suppress 61 taxa and 0 samples.

# Filter to keep only common taxa
lpq_filtered <- filter_common_lpq(lpq, filter_samples = FALSE, filter_taxa = TRUE)
#> Filtering to 42 common taxa.
#> Cleaning suppress 0 taxa and 48 samples.
#>   original: 185 -> 137 samples, 1420 -> 42 taxa
#>   mini: 137 -> 137 samples, 45 -> 42 taxa
#> Cleaning suppress 0 taxa and 115 samples.
#>   rarefied: 185 -> 70 samples, 413 -> 42 taxa
#> ℹ Building summary table for 3 phyloseq objects...
#> ℹ Computing comparison characteristics...
#> ℹ Checking sample and taxa overlap...
#> ℹ Detected comparison type: NESTED_ROBUSTNESS
#> ℹ 70 common samples, 42 common taxa
#> ✔ list_phyloseq created (NESTED_ROBUSTNESS)

# Filter both samples and taxa
lpq_filtered <- filter_common_lpq(lpq, filter_samples = TRUE, filter_taxa = TRUE)
#> Filtering to 137 common samples.
#> Filtering to 42 common taxa.
#>   original: 185 -> 137 samples, 1420 -> 42 taxa
#>   mini: 137 -> 137 samples, 45 -> 42 taxa
#> Cleaning suppress 0 taxa and 67 samples.
#>   rarefied: 185 -> 70 samples, 413 -> 42 taxa
#> ℹ Building summary table for 3 phyloseq objects...
#> ℹ Computing comparison characteristics...
#> ℹ Checking sample and taxa overlap...
#> ℹ Detected comparison type: NESTED_ROBUSTNESS
#> ℹ 70 common samples, 42 common taxa
#> ✔ list_phyloseq created (NESTED_ROBUSTNESS)