bnemBs {bnem} | R Documentation |
Runs Bootstraps on the data
bnemBs(fc, x = 10, f = 0.5, replace = TRUE, startString = NULL, ...)
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 |
list with the accumulation of edges in x and the number of bootstraps in n
Martin Pirkl
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)