Execute NOISeq gene Expression analysis

Execute NOISeq gene Expression analysis

Usage

runNoiSeq(
  countMatrix,
  designExperiment,
  groups = c(""),
  normParm = "rpkm",
  kParam = 0.5,
  factorParam = c("Tissue"),
  lcParam = 0,
  replicatesParam = "technical",
  condExp = c("")
)

Arguments

countMatrix

either a matrix of raw (read) counts.

designExperiment

replicate and treatment by samples.

groups

text list separated by comma, name of samples or treatment.

normParm

Normalization method t can be one of "rpkm" (default), "uqua" (upper quartile), "tmm" (trimmed mean of M) or "n" (no normalization).

kParam

Counts equal to 0 are replaced by k. By default, k = 0.5

factorParam

A string indicating the name of factor whose levels are the conditions to be compared.

lcParam

Length correction is done by dividing expression by length^lc. By default, lc = 0

replicatesParam

In this argument, the type of replicates to be used is defined: "technical", "biological" or "no" replicates. By default, "technical" replicates option is chosen.

condExp

A vector containing the two conditions to be compared by the differential expression algorithm (needed when the factor contains more than 2 different conditions).

Value

A data.frame with output of noiseqOut@results

Examples

set.seed(42)
counts <- matrix(
  as.integer(c(
    rnbinom(200, mu = 10, size = 1),
    rnbinom(200, mu = 100, size = 1)
  )),
  nrow = 100,
  dimnames = list(
    paste0("gene", seq_len(100)),
    paste0("sample", seq_len(4))
  )
)
treats <- c("control", "treated")
toolResult <- NULL
toolResult$noiseq <- runNoiSeq(countMatrix = counts, designExperiment = rep(treats, each = 2))
#> [1] "Computing (M,D) values..."
#> [1] "Computing probability of differential expression..."