validateGraph {bnem} | R Documentation |
plotting the observed differential effects of an effect reporter and the expected differential effects of the regulating signalling gene
validateGraph( CNOlist, fc = NULL, expression = NULL, model, bString, Egenes = 25, Sgene = 1, parameters = list(cutOffs = c(0, 1, 0), scoring = c(0.1, 0.2, 0.9)), plot = TRUE, disc = 0, affyIds = TRUE, relFit = FALSE, xrot = 25, Rowv = FALSE, Colv = FALSE, dendrogram = "none", soft = TRUE, colSideColors = NULL, affychip = "hgu133plus2", method = "s", ranks = FALSE, breaks = NULL, col = "RdYlGn", sizeFac = 10^-10, order = "rank", verbose = TRUE, ... )
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. |
bString |
Binary string denoting the hyper-graph. |
Egenes |
Maximal number of visualized E-genes. |
Sgene |
Integer denoting the S-gene. See colnames(getSignals(CNOlist)[[1]]) to match integer with S-gene name. |
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); |
plot |
Plot the heatmap. If FALSE, only corresponding information is printed. |
disc |
Discretize the data. |
affyIds |
Experimental. Turn Affymetrix Ids into HGNC gene symbols. |
relFit |
if TRUE a relative fit for each E-gene is computed (not recommended) |
xrot |
See function epiNEM::HeatmapOP |
Rowv |
See function epiNEM::HeatmapOP |
Colv |
See function epiNEM::HeatmapOP |
dendrogram |
See function epiNEM::HeatmapOP |
soft |
if TRUE, assigns weights to the expected pattern |
colSideColors |
See function epiNEM::HeatmapOP |
affychip |
Define Affymetrix chip used to generate the data (optional and experimental). |
method |
Scoring method can be "cosine", a correlation, or a distance measure. See ?cor and ?dist for details. |
ranks |
if TRUE, turns data into ranks |
breaks |
See function epiNEM::HeatmapOP |
col |
See function epiNEM::HeatmapOP |
sizeFac |
Size factor penelizing the hyper-graph size. |
order |
Order by "rank", "name" or "none" |
verbose |
TRUE for verbose output |
... |
additional arguments for epiNEM::HeatmapOP |
lattice object with matrix information
Martin Pirkl
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)