DA.wil: Wilcoxon Rank Sum and Signed Rank Test

In Russel88/DAtest: Comparing Differential Abundance/Expression Methods

Wilcoxon Rank Sum and Signed Rank Test

Description

Apply wilcoxon test for multiple features with one predictor

Usage

DA.wil(
  data,
  predictor,
  paired = NULL,
  relative = TRUE,
  p.adj = "fdr",
  testStat = function(case, control) {     log2((mean(case) + 0.001)/(mean(control) +
    0.001)) },
  testStat.pair = function(case, control) {     log2(mean((case + 0.001)/(control +
    0.001))) },
  allResults = FALSE,
  ...
)

Arguments

data	Either a matrix with counts/abundances, OR a phyloseq object. If a matrix/data.frame is provided rows should be taxa/genes/proteins and columns samples
predictor	The predictor of interest. Factor, OR if data is a phyloseq object the name of the variable in sample_data(data) in quotation
paired	For paired/blocked experimental designs. Either a Factor with Subject/Block ID for running paired/blocked analysis, OR if data is a phyloseq object the name of the variable in sample_data(data) in quotation
relative	Logical. Should data be normalized to relative abundances. Default TRUE
p.adj	Character. P-value adjustment. Default "fdr". See p.adjust for details
testStat	Function. Function for calculating fold change. Should take two vectors as arguments. Default is a log fold change: log2((mean(case abundances)+0.001)/(mean(control abundances)+0.001))
testStat.pair	Function. Function for calculating fold change. Should take two vectors as arguments. Default is a log fold change: log2(mean((case abundances+0.001)/(control abundances+0.001)))
allResults	If TRUE will return raw results from the wilcox.test function
...	Additional arguments for the wilcox.test function

Value

A data.frame with with results.

Examples

# Creating random count_table and predictor
set.seed(4)
mat <- matrix(rnbinom(1000, size = 0.1, mu = 500), nrow = 100, ncol = 10)
rownames(mat) <- 1:100
pred <- c(rep("Control", 5), rep("Treatment", 5))

# Running Wilcoxon test on each feature
res <- DA.wil(data = mat, predictor = pred)

Russel88/DAtest documentation built on March 24, 2022, 3:50 p.m.