findResiduals {bnem} | R Documentation

## Compute residuals

### Description

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

### Usage

    findResiduals(
      bString,
      CNOlist,
      model,
      fc = NULL,
      expression = NULL,
      egenes = 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,
      parallel = NULL,
      verbose = TRUE,
      ...
    )

### Arguments

`bString` | Binary vector denoting the network given a model
`CNOlist` | CNOlist object (see package CellNOptR), if available.
`model` | Model object including the search space, if available. See CellNOptR::preprocessing.
`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.
`egenes` | list object; each list entry is named after an S-gene and contains the names of egenes which are potential children
`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);
`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.
`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.
`parallel` | Parallelize the search. An integer value specifies the number of threads on the local machine or a list object as in list(c(1,2,3), c("machine1", "machine2", "machine3")) specifies the threads distributed on different machines (local or others).
`verbose` | TRUE for verbose output
`...` | additional parameters for epiNEM::HeatmapOP

### Value

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

### Author(s)

Martin Pirkl

### Examples

    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,
    signal = 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$reacID))
    res <- bnem(search = "greedy", CNOlist = CNOlist, fc = fc, model = model,
    parallel = NULL, initBstring = initBstring, draw = FALSE, verbose = FALSE,
    maxSteps = Inf)
    rownames(fc) <- seq_len(nrow(fc))
    ## val <- validateGraph(CNOlist = CNOlist, fc = fc, model = model,
    ## bString = res$bString, Egenes = 10, Sgene = 4)
    residuals <- findResiduals(res$bString, CNOlist, model, fc = fc)

[Package bnem version 1.0.0 Index]