Description Usage Arguments Value Author(s) See Also Examples

This function takes a prior network and normalized perturbation data as input and trains logical functions on that prior network

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 | ```
bnem(
search = "greedy",
fc = NULL,
exprs = NULL,
egenes = NULL,
pkn = NULL,
design = NULL,
stimuli = NULL,
inhibitors = NULL,
signals = NULL,
CNOlist = NULL,
model = NULL,
sizeFac = 10^-10,
NAFac = 1,
parameters = list(cutOffs = c(0, 1, 0), scoring = c(0.1, 0.2, 0.9)),
parallel = NULL,
method = "s",
approach = "fc",
relFit = FALSE,
verbose = TRUE,
reduce = TRUE,
parallel2 = 1,
initBstring = NULL,
popSize = 100,
pMutation = 0.5,
maxTime = Inf,
maxGens = Inf,
stallGenMax = 10,
relTol = 0.01,
priorBitString = NULL,
selPress = c(1.2, 1e-04),
fit = "linear",
targetBstring = "none",
elitism = NULL,
inversion = NULL,
selection = c("t"),
type = "SOCK",
exhaustive = FALSE,
delcyc = FALSE,
seeds = 1,
maxSteps = Inf,
node = NULL,
absorpII = TRUE,
draw = TRUE,
prior = NULL,
maxInputsPerGate = 2
)
``` |

`search` |
Type of search heuristic. Either "greedy", "genetic" or "exhaustive". "greedy" uses a greedy algorithm to move through the local neighbourhood of a initial hyper-graph. "genetic" uses a genetic algorithm. "exhaustive" searches through the complete search space and is not recommended. |

`fc` |
Foldchanges of gene expression values or equivalent input (normalized pvalues, logodds, ...). If left NULL, the gene expression data is used to calculate naive foldchanges. |

`exprs` |
Optional normalized gene expression data. |

`egenes` |
list object; each list entry is named after an S-gene and contains the egenes which are potential children |

`pkn` |
Prior knowledge network. |

`design` |
Optional design matrix for the gene expression values, if available. If kept NULL, bnem needs either stimuli, inhibitors or a CNOlist object. |

`stimuli` |
Character vector of stimuli names. |

`inhibitors` |
Character vector of inhibitors. |

`signals` |
Optional character vector of signals. Signals are S-genes, which can directly regulate E-genes. If left NULL, alls stimuli and inhibitors are defined as signals. |

`CNOlist` |
CNOlist object if available. |

`model` |
Model object including the search space, if available. |

`sizeFac` |
Size factor penelizing the hyper-graph size. |

`NAFac` |
factor penelizing NAs in the data. |

`parameters` |
parameters for discrete case (not recommended); has to ba 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); |

`parallel` |
Parallelize the search. An integer value specifies the number of threads on the local machine. A list object as in list(c(1,2,3), c("machine1", "machine2", "machine3")) specifies the threads distributed on different machines (local or others). |

`method` |
Scoring method can be "cosine" a correlation, distance measure or a probability based score "llr". See ?cor and ?dist for details. |

`approach` |
default "fc" for foldchanges or signed effect probabilities, "abs" for absolute effects or probabilities |

`relFit` |
if TRUE a relative fit for each E-gene is computed (not recommended) |

`verbose` |
TRUE gives additional information during the search. |

`reduce` |
if TRUE reduces the search space for exhaustive search |

`parallel2` |
if TRUE parallelises the starts and not the search itself |

`initBstring` |
Binary string of the initial hyper-graph. |

`popSize` |
Population size (only "genetic"). |

`pMutation` |
Probability for mutation (only "genetic"). |

`maxTime` |
Define a maximal time (seconds) for the search. |

`maxGens` |
Maximal number of generations (only "genetic"). |

`stallGenMax` |
Maximum number of stall generations (only "genetic"). |

`relTol` |
Score tolerance for networks defined as optimal but with a lower score as the real optimum (only "genetic"). |

`priorBitString` |
Binary string defining hyper-edges which are added to every hyper-graph. E.g. if you know hyper-edge 55 is definitly there and to fix that, set priorBitString[55] <- 1 (only "genetic"). |

`selPress` |
Selection pressure for the stochastic universal sampling (only "genetic"). |

`fit` |
"linear" or "nonlinear fit for stochastic universal sampling |

`targetBstring` |
define a binary string representing a network; if this network is found, the computation stops |

`elitism` |
Number of best hyper-graphs transferred to the next generation (only "genetic"). |

`inversion` |
Number of worst hyper-graphs for which their binary strings are inversed (only "genetic"). |

`selection` |
"t" for tournament selection and "s" for stochastic universal sampling (only "genetic"). |

`type` |
type of the paralellisation on multpile machines (default: SOCK) |

`exhaustive` |
If TRUE an exhaustive search is conducted if the genetic algorithm would take longer (only "genetic"). |

`delcyc` |
If TRUE deletes cycles in all hyper-graphs (not recommended). |

`seeds` |
how many starts for the greedy search? (default: 1) |

`maxSteps` |
Maximal number of steps (only "greedy"). |

`node` |
vector of S-gene names, which are used in the greedy search; if node = NULL all nodes are considered |

`absorpII` |
Use inverse absorption. |

`draw` |
If TRUE draws the network evolution. |

`prior` |
Binary vector. A 1 specifies hyper-edges which should not be optimized (only "greedy"). |

`maxInputsPerGate` |
If no model is supplied, one is created with maxInputsPerGate as maximum number of parents for each hyper-edge. |

List object including the optimized hyper-graph and its corresponding binary string.

Martin Pirkl

nem

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | ```
sifMatrix <- rbind(c("A", 1, "B"), c("A", 1, "C"), c("B", 1, "D"),
c("C", 1, "D"))
write.table(sifMatrix, file = "temp.sif", sep = "\t",
row.names = FALSE, col.names = FALSE,
quote = FALSE)
PKN <- CellNOptR::readSIF("temp.sif")
unlink('temp.sif')
CNOlist <- dummyCNOlist("A", c("B","C","D"), maxStim = 1,
maxInhibit = 2, signals = c("A", "B","C","D"))
model <- CellNOptR::preprocessing(CNOlist, PKN, maxInputsPerGate = 100)
exprs <- matrix(rnorm(nrow(slot(CNOlist, "cues"))*10), 10,
nrow(slot(CNOlist, "cues")))
fc <- computeFc(CNOlist, exprs)
initBstring <- rep(0, length(model$reacID))
res <- bnem(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)
``` |

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