Description Usage Arguments Value Author(s) Examples

calculates residuals (data and optimized network do not match) and visualizes them

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | ```
findResiduals(
bString,
CNOlist,
model,
fc = NULL,
exprs = NULL,
egenes = NULL,
NEMlist = NULL,
parameters = list(cutOffs = c(0, 1, 0), scoring = c(0.1, 0.2, 0.9)),
method = "s",
sizeFac = 10^-10,
main = "residuals for decoupled vertices",
sub = paste0("green residuals are added effects (left positive,",
" right negative) and red residuals are deleted ", "effects"),
cut = TRUE,
approach = "fc",
parallel = NULL,
verbose = TRUE,
...
)
``` |

`bString` |
Binary vector denoting the network given a model |

`CNOlist` |
CNOlist object |

`model` |
Network model object. |

`fc` |
ORS of the data as numeric matrix. |

`exprs` |
Optional activation scheme of the data as numeric matrix. |

`egenes` |
Atachment of the E-genes (optional) as list named after S-genes. |

`NEMlist` |
NEMlist object (optional). |

`parameters` |
see ?bnem |

`method` |
Scoring method (optional). |

`sizeFac` |
Zeta parameter to penelize network size. |

`main` |
Main title of the figure. |

`sub` |
Subtitle of the figure. |

`cut` |
If TRUE does not visualize experiments/S-genes which do not have any residuals. |

`approach` |
see ?bnem |

`parallel` |
the number of threads used for computation. |

`verbose` |
verbose output |

`...` |
additional parameters for ?epiNEM::HeatmapOP |

numeric matrices indicating experiments and/or genes, where the network and the data disagree

Martin Pirkl

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | ```
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,
signal = 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$reacID))
res <- bnem(search = "greedy", CNOlist = CNOlist, fc = fc, model = model,
parallel = NULL, initBstring = initBstring, draw = FALSE, verbose = FALSE,
maxSteps = Inf)
rownames(fc) <- seq_len(nrow(fc))
## val <- validateGraph(CNOlist = CNOlist, fc = fc, model = model,
## bString = res$bString, Egenes = 10, Sgene = 4)
residuals <- findResiduals(res$bString, CNOlist, model, fc = fc)
``` |

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