plot.fitnessEffects: Plot fitnessEffects objects.
In OncoSimulR: Forward Genetic Simulation of Cancer Progression with Epistasis

Description Usage Arguments Value Note Author(s) See Also Examples

Plot the restriction table/graph of restrictions, the epistasis, and the order effects in a fitnessEffects object. This is not a plot of the fitness landscape; for that, see plotFitnessLandscape.

## S3 method for class 'fitnessEffects'
plot(x, type = "graphNEL", layout = NULL,
expandModules = FALSE, autofit = FALSE,
scale_char = ifelse(type == "graphNEL", 1/10, 5),
return_g = FALSE, lwdf = 1, ...)

`x`	A `fitnessEffects` object, as produced by `allFitnessEffects`.
`type`	Whether you want a "graphNEL" or an "igraph" graph.
`layout`	For "igraph", the layout. For example, if you know you really have only a tree you might want to use `layout.reingold.tilford`. Note that there is very limited support for passing options, etc. In most cases, it is either the default or the `layout.reingold.tilford`.
`expandModules`	If there are modules with multiple genes, if you set this to TRUE modules will be replaced by their genes.
`autofit`	If TRUE, we try to fit the edges to the labels. This is a very experimental feature, likely to be not very robust.
`scale_char`	If using `autofit = TRUE`, the scaling factor for the size of the rectangles as a function of the number of characters. You have to play with this because the best value can depend on a number of things.
`return_g`	It TRUE, the graph object (graphNEL or igrap) is returned.
`lwdf`	The multiplier factor for `lwd` when using "graphNEL".
`...`	Other arguments passed to `plot`. Not used for now.

A plot.

Order and epistatic relationships have orange edges. OR (semimonotone) relationships blue, and XOR red. All others have black edges (so AND and unique edges from root). Epistatic relationships, being symmetrical, have no arrows between nodes and have a dotted line type. Order relationships have an arrow from the earlier to the later event and have a different dotted line (lty 3).

If return_g is TRUE, you are returned also the graph object (igraph or graphNEL) so that you can manipulate it further.

The purpose of the plot is to get a quick idea of the relationships. Note that three-way (or higher order) epistatic relationships cannot be shown as such (we would show all possible pairs, but that is not quite the same thing). Likewise, there is no reasonable way to convey the pressence of a "-" in the epistatic relationship.

Genes without interactions are not shown.

Ramon Diaz-Uriarte

allFitnessEffects, plotFitnessLandscape

cs <-  data.frame(parent = c(rep("Root", 4), "a", "b", "d", "e", "c"),
                 child = c("a", "b", "d", "e", "c", "c", rep("g", 3)),
                 s = 0.1,
                 sh = -0.9,
                 typeDep = "MN")

cbn1 <- allFitnessEffects(cs)
plot(cbn1, "igraph")

library(igraph) ## to make layouts available
plot(cbn1, "igraph", layout = layout.reingold.tilford)


### A DAG with the three types of relationships
p3 <- data.frame(parent = c(rep("Root", 4), "a", "b", "d", "e", "c", "f"),
                  child = c("a", "b", "d", "e", "c", "c", "f", "f", "g", "g"),
                  s = c(0.01, 0.02, 0.03, 0.04, 0.1, 0.1, 0.2, 0.2, 0.3, 0.3),
                  sh = c(rep(0, 4), c(-.9, -.9), c(-.95, -.95), c(-.99, -.99)),
                  typeDep = c(rep("--", 4), 
                      "XMPN", "XMPN", "MN", "MN", "SM", "SM"))
fp3 <- allFitnessEffects(p3)

plot(fp3)

plot(fp3, "igraph", layout = layout.reingold.tilford)


## A more complex example, that includes a restriction table
## order effects, epistasis, genes without interactions, and moduels
p4 <- data.frame(parent = c(rep("Root", 4), "A", "B", "D", "E", "C", "F"),
                 child = c("A", "B", "D", "E", "C", "C", "F", "F", "G", "G"),
                 s = c(0.01, 0.02, 0.03, 0.04, 0.1, 0.1, 0.2, 0.2, 0.3, 0.3),
                 sh = c(rep(0, 4), c(-.9, -.9), c(-.95, -.95), c(-.99, -.99)),
                 typeDep = c(rep("--", 4), 
                     "XMPN", "XMPN", "MN", "MN", "SM", "SM"))

oe <- c("C > F" = -0.1, "H > I" = 0.12)
sm <- c("I:J"  = -1)
sv <- c("-K:M" = -.5, "K:-M" = -.5)
epist <- c(sm, sv)

modules <- c("Root" = "Root", "A" = "a1",
             "B" = "b1, b2", "C" = "c1",
             "D" = "d1, d2", "E" = "e1",
             "F" = "f1, f2", "G" = "g1",
             "H" = "h1, h2", "I" = "i1",
             "J" = "j1, j2", "K" = "k1, k2", "M" = "m1")

noint <- rexp(5, 10)
names(noint) <- paste0("n", 1:5)

fea <- allFitnessEffects(rT = p4, epistasis = epist, orderEffects = oe,
                         noIntGenes = noint, geneToModule = modules)


plot(fea)
plot(fea, expandModules = TRUE)
plot(fea, type = "igraph")