DA.ltt2: Welch t-test

In Russel88/DAtest: Comparing Differential Abundance/Expression Methods

Welch t-test

Description

Apply welch t-test to multiple features and one predictor, and with log transformed relative abundances

Usage

DA.ltt2(
  data,
  predictor,
  paired = NULL,
  p.adj = "fdr",
  delta = 0.001,
  testStat = function(case, control) {     log2((mean(exp(case)))/(mean(exp(control))))
    },
  testStat.pair = function(case, control) {     log2(mean((exp(case))/(exp(control))))
    },
  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
p.adj	Character. P-value adjustment. Default "fdr". See p.adjust for details
delta	Numeric. Pseudocount for log transformation. Default 0.001
testStat	Function. Function for calculating fold change. Should take two vectors as arguments. Default is a log fold change: log2(mean(exp(case abundances)) / mean(exp(control abundances)))
testStat.pair	Function. Function for calculating fold change. Should take two vectors as arguments. Default is a log fold change: log2(mean(exp(case abundances) / exp(control abundances)))
allResults	If TRUE will return raw results from the t.test function
...	Additional arguments for the t.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 t-test on each feature
res <- DA.ltt2(data = mat, predictor = pred)

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