scoreDnf {bnem} | R Documentation |
computes the score of a boolean network given the model and data
scoreDnf( bString, CNOlist, fc, expression = NULL, model, method = "cosine", sizeFac = 10^-10, NAFac = 1, parameters = list(cutOffs = c(0, 1, 0), scoring = c(0.25, 0.5, 2)), NEMlist = NULL, relFit = FALSE, verbose = FALSE )
bString |
binary string denoting the boolean network |
CNOlist |
CNOlist object (see package CellNOptR), if available. |
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. |
expression |
Optional normalized m x l matrix of gene expression data for m E-genes and l experiments. |
model |
Model object including the search space, if available. See CellNOptR::preprocessing. |
method |
Scoring method can be "cosine", a correlation, or a distance measure. See ?cor and ?dist for details. |
sizeFac |
Size factor penelizing the hyper-graph size. |
NAFac |
factor penelizing NAs in the data. |
parameters |
parameters for discrete case (not recommended); has to be a list with entries cutOffs and scoring: cutOffs = c(a,b,c) with a (cutoff for real zeros), b (cutoff for real effects), c = -1 for normal scoring, c between 0 and 1 for keeping only relevant between -1 and 0 for keeping only a specific quantile of E-genes, and c > 1 for keeping the top c E-genes; scoring = c(a,b,c) with a (weight for real effects), c (weight for real zeros), b (multiplicator for effects/zeros between a and c); |
NEMlist |
NEMlist object (optional) |
relFit |
if TRUE a relative fit for each E-gene is computed (not recommended) |
verbose |
TRUE for verbose output |
numeric value (score)
Martin Pirkl
sim <- simBoolGtn() scoreDnf(sim$bString, sim$CNOlist, sim$fc, model=sim$model)