Expression Methods

DA.ttc

R Documentation

Welch t-test - Multiplicative zero-correction and center log-ratio normalization.

Description

Apply welch t-test to multiple features with one predictor

Usage

DA.ttc(
  data,
  predictor,
  paired = NULL,
  p.adj = "fdr",
  delta = 1,
  testStat = function(case, control) {     mean(case) - mean(control) },
  testStat.pair = function(case, control) {     mean(case - 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 zero-correction.
`testStat`	Function. Function for calculating fold change. Should take two vectors as arguments. Default is a simple difference: `mean(case abundances)-mean(control abundances)`
`testStat.pair`	Function. Function for calculating fold change. Should take two vectors as arguments. Default is a simple difference: `mean(case abundances-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.ttc(data = mat, predictor = pred)

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