R/transform-filter.R

In mikemc/tidyseq: Phyloseq extensions and improvements

# Transform sample counts -----------------------------------------------------

#' Transform abundance data in an \code{otu_table}, sample-by-sample
#' 
#' This is a wrapper around phyloseq::transform_sample_counts that allows use
#' of a purrr-style anonymous function for `.f`.
#' 
#' @seealso 
#' phyloseq::transform_sample_counts
#' tidyseq::filter_taxa
#'
#' @export
#' @examples
#' library(phyloseq)
#' library(tidyseq)
#' data(GlobalPatterns)
#' # Filter low prevalence taxa and then convert to proportions
#' gp.prop <- GlobalPatterns %>%
#'   filter_taxa2(~ sum(. > 0) > 5) %>%
#'   transform_sample_counts(~ . / sum(.))
transform_sample_counts <- function(physeq, .f, ...){
    fun <- purrr::as_mapper(.f)
    phyloseq::transform_sample_counts(physeq, fun, ...)
}

# Filter taxa -----------------------------------------------------------------

#' Filter taxa based on across-sample OTU abundance criteria
#' 
#' Wrappers around phyloseq::filter_taxa that allow using a purrr-style
#' anonymous function for `.f`. 
#'
#' @export
#' @seealso 
#' phyloseq::filter_taxa
#' tidyseq::transform_sample_counts
#' 
#' @examples
#' library(phyloseq)
#' library(tidyseq)
#' data(GlobalPatterns)
#' # Filter low prevalence taxa and then convert to proportions
#' gp.prop <- GlobalPatterns %>%
#'   filter_taxa2(~ sum(. > 0) > 5) %>%
#'   transform_sample_counts(~ . / sum(.))
filter_taxa <- function(physeq, .f, prune = FALSE){
    fun <- purrr::as_mapper(.f)
    phyloseq::filter_taxa(physeq, fun, prune = prune)
}

#' @export
#' @describeIn filter_taxa Sets `prune = TRUE`
filter_taxa2 <- function(physeq, .f){
    fun <- purrr::as_mapper(.f)
    phyloseq::filter_taxa(physeq, fun, prune = TRUE)
}