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lefse analysis

Source: R/lefse.R
step_lefse.Rd

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.

Usage

step_lefse(
  rec,
  kruskal.threshold = 0.05,
  wilcox.threshold = 0.05,
  lda.threshold = 2,
  subclassCol = NULL,
  assay = 1L,
  trim.names = FALSE,
  rarefy = TRUE,
  id = rand_id("lefse")
)

# S4 method for class 'Recipe'
step_lefse(
  rec,
  kruskal.threshold = 0.05,
  wilcox.threshold = 0.05,
  lda.threshold = 2,
  subclassCol = NULL,
  assay = 1L,
  trim.names = FALSE,
  rarefy = TRUE,
  id = rand_id("lefse")
)

# S4 method for class 'PrepRecipe'
step_lefse(
  rec,
  kruskal.threshold = 0.05,
  wilcox.threshold = 0.05,
  lda.threshold = 2,
  subclassCol = NULL,
  assay = 1L,
  trim.names = FALSE,
  rarefy = TRUE,
  id = rand_id("lefse")
)

Arguments

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).

subclassCol

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.

Value

An object of class Recipe

See also

Other Diff taxa steps: step_aldex(), step_ancom(), step_corncob(), step_deseq(), step_maaslin(), step_metagenomeseq(), step_wilcox()

Examples

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
#> ── DAR Recipe ──────────────────────────────────────────────────────────────────
#> Inputs:
#> 
#>      ℹ phyloseq object with 451 taxa and 156 samples 
#>      ℹ variable of interes RiskGroup2 (class: character, levels: hts, msm, pwid) 
#>      ℹ taxonomic level Phylum 
#> 
#> Preporcessing steps:
#> 
#>      ◉ step_subset_taxa() id = subset_taxa__Empanada 
#>      ◉ step_filter_taxa() id = filter_taxa__Rugelach 
#> 
#> DA steps:
#> 

## Define step with default parameters
rec <- step_lefse(rec) 
rec
#> ── DAR Recipe ──────────────────────────────────────────────────────────────────
#> Inputs:
#> 
#>      ℹ phyloseq object with 451 taxa and 156 samples 
#>      ℹ variable of interes RiskGroup2 (class: character, levels: hts, msm, pwid) 
#>      ℹ taxonomic level Phylum 
#> 
#> Preporcessing steps:
#> 
#>      ◉ step_subset_taxa() id = subset_taxa__Empanada 
#>      ◉ step_filter_taxa() id = filter_taxa__Rugelach 
#> 
#> DA steps:
#> 
#>      ◉ step_lefse() id = lefse__Kolache 

## Running lefse without rarefaction (not recommended)
rec <- 
  recipe(metaHIV_phy, "RiskGroup2", "Species") |>
  step_lefse(rarefy = FALSE)
#> ! Run lefse without rarefaction is not recommended (id = lefse__Leipziger_Lerche)
  
rec
#> ── DAR Recipe ──────────────────────────────────────────────────────────────────
#> Inputs:
#> 
#>      ℹ phyloseq object with 451 taxa and 156 samples 
#>      ℹ variable of interes RiskGroup2 (class: character, levels: hts, msm, pwid) 
#>      ℹ taxonomic level Species 
#> 
#> Preporcessing steps:
#> 
#> 
#> DA steps:
#> 
#>      ◉ step_lefse() id = lefse__Leipziger_Lerche