TRONCO 2.33.0

TRONCO provides functions to plot a model, access information about the probabilities used to extract it from data, and two types of confidence measures: those used to infer the model, and those computed a posteriori from it.

Function `view`

provides updated information about a model if this is available.

`view(model.capri)`

```
## Description: CAPRI - aCML.
## -- TRONCO Dataset: n=64, m=26, |G|=10, patterns=9.
## Events (types): Ins/Del, Missense point, Nonsense point, Pattern.
## Colors (plot): #7FC97F, #4483B0, #fab3d8, slateblue.
## Events (5 shown):
## gene 4 : Ins/Del TET2
## gene 5 : Ins/Del EZH2
## gene 6 : Ins/Del CBL
## gene 7 : Ins/Del ASXL1
## gene 29 : Missense point SETBP1
## Genotypes (5 shown):
##
## -- TRONCO Model(s): CAPRI
## Score optimization via Hill-Climbing.
## BIC, AIC regularizers.
## BIC: score -566.1804 | logLik -499.6382 | 6 selective advantage relations.
## AIC: score -528.7683 | logLik -492.7683 | 10 selective advantage relations.
## Available confidence measures:
## Temporal priority | Probability raising | Hypergeometric
```

We can plot a model by using function . Here, we plot the aCML model inferred by CAPRI with BIC and AIC as a regolarizator. We set some parameters to get a nice plot (scaling etc.), and distinguish the edges detected by the two regularization techniques. The confidence of each edge is shown in terms of temporal priority and probability raising (selective advantage scores) and hypergeometric testing (statistical relevance of the dataset of input). Events are annotated as in the oncoprint, edge p-values above a minium threshold (default 0.05) are red.

```
tronco.plot(model.capri,
fontsize = 12,
scale.nodes = 0.6,
confidence = c('tp', 'pr', 'hg'),
height.logic = 0.25,
legend.cex = 0.35,
pathways = list(priors = gene.hypotheses),
label.edge.size = 10)
```

```
## *** Expanding hypotheses syntax as graph nodes:
## *** Rendering graphics
## Nodes with no incoming/outgoing edges will not be displayed.
## Annotating nodes with pathway information.
## Annotating pathways with RColorBrewer color palette Set1 .
## Adding confidence information: tp, pr, hg
## RGraphviz object prepared.
## Plotting graph and adding legends.
```

We can also make a multiplot with this function, which in this case we do by showing the models inferred by the other algorithms based on Minimum Spanning Trees.

```
par(mfrow = c(2,2))
tronco.plot(model.caprese, fontsize = 22, scale.nodes = 0.6, legend = FALSE)
```

```
## *** Expanding hypotheses syntax as graph nodes:
## *** Rendering graphics
## Nodes with no incoming/outgoing edges will not be displayed.
## Set automatic fontsize for edge labels: 11
## RGraphviz object prepared.
## Plotting graph and adding legends.
```

`tronco.plot(model.edmonds, fontsize = 22, scale.nodes = 0.6, legend = FALSE)`

```
## *** Expanding hypotheses syntax as graph nodes:
## *** Rendering graphics
## Nodes with no incoming/outgoing edges will not be displayed.
## Set automatic fontsize for edge labels: 11
## RGraphviz object prepared.
## Plotting graph and adding legends.
```

`tronco.plot(model.chowliu, fontsize = 22, scale.nodes = 0.6, legend.cex = .7)`

```
## *** Expanding hypotheses syntax as graph nodes:
## *** Rendering graphics
## Nodes with no incoming/outgoing edges will not be displayed.
## Set automatic fontsize for edge labels: 11
## RGraphviz object prepared.
## Plotting graph and adding legends.
```

`tronco.plot(model.prim, fontsize = 22, scale.nodes = 0.6, legend = FALSE)`

```
## *** Expanding hypotheses syntax as graph nodes:
## *** Rendering graphics
## Nodes with no incoming/outgoing edges will not be displayed.
## Set automatic fontsize for edge labels: 11
## RGraphviz object prepared.
## Plotting graph and adding legends.
```