scoreDnf: score a boolean network

In MartinFXP/B-NEM: Training of logical models from indirect measurements of perturbation experiments

score a boolean network

Description

computes the score of a boolean network given the model and data

Usage

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
)

Arguments

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

Value

numeric value (score)

Author(s)

Martin Pirkl

Examples

sim <- simBoolGtn()
scoreDnf(sim$bString, sim$CNOlist, sim$fc, model=sim$model)

MartinFXP/B-NEM documentation built on Oct. 27, 2023, 8:24 p.m.