bnemBs {bnem} | R Documentation

## Bootstraped Network

### Description

Runs Bootstraps on the data

### Usage

    bnemBs(fc, x = 10, f = 0.5, replace = TRUE, startString = NULL, ...)

### Arguments

`fc` | m x l matrix of foldchanges of gene expression values or equivalent input (normalized pvalues, logodds, ...) for m E-genes and l contrasts. If left NULL, the gene expression data is used to calculate naive foldchanges.
`x` | number of bootstraps
`f` | percentage to sample, e.g. f = 0.5 samples only 50 the amount of E-genes as the original data
`replace` | if TRUE classical bootstrap, if FALSE sub-sampling without replacement
`startString` | matrix with each row being a string denoting a network to start inference several times with a specific network
`...` | additional parameters for the bnem function

### Value

list with the accumulation of edges in x and the number of bootstraps in n

### Author(s)

Martin Pirkl

### Examples

    sifMatrix <- rbind(c("A", 1, "B"), c("A", 1, "C"), c("B", 1, "D"),
    c("C", 1, "D"))
    temp.file <- tempfile(pattern="interaction",fileext=".sif")
    write.table(sifMatrix, file = temp.file, sep = "\t",
    row.names = FALSE, col.names = FALSE,
    quote = FALSE)
    PKN <- CellNOptR::readSIF(temp.file)
    CNOlist <- dummyCNOlist("A", c("B","C","D"), maxStim = 1,
    maxInhibit = 2, signals = c("A", "B","C","D"))
    model <- CellNOptR::preprocessing(CNOlist, PKN, maxInputsPerGate = 100)
    expression <- matrix(rnorm(nrow(slot(CNOlist, "cues"))*10), 10,
    nrow(slot(CNOlist, "cues")))
    fc <- computeFc(CNOlist, expression)
    initBstring <- rep(0, length(model$reacID))
    res <- bnemBs(search = "greedy", model = model, CNOlist = CNOlist,
    fc = fc, pkn = PKN, stimuli = "A", inhibitors = c("B","C","D"),
    parallel = NULL, initBstring = initBstring, draw = FALSE, verbose = FALSE,
    maxSteps = Inf)

[Package bnem version 1.0.0 Index]