In BIMIB-DISCo/TRONCO: TRONCO, an R package for TRanslational ONCOlogy

library(TRONCO)
data(aCML)
data(crc_maf)
data(crc_gistic)
data(crc_plain)

All examples in this section will be done with the the aCML dataset as reference.

Modifying events and samples

TRONCO provides functions for renaming the events that were included in a dataset, or the type associated to a set of events (e.g., a Mutation could be renamed to a Missense Mutation).

dataset = rename.gene(aCML, 'TET2', 'new name')
dataset = rename.type(dataset, 'Ins/Del', 'new type')
as.events(dataset, type = 'new type')

and return a modified TRONCO object. More complex operations are also possible. For instance, two events with the same signature -- i.e., appearing in the same samples -- can be joined to a new event (see also Data Consolidation in Model Inference) with the same signature and a new name.

dataset = join.events(aCML, 
    'gene 4',
    'gene 88',
    new.event='test',
    new.type='banana',
    event.color='yellow')

where in this case we also created a new event type, with its own color.

In a similar way we can decide to join all the events of two distinct types, in this case if a gene x has signatures for both type of events, he will get a unique signature with an alteration present if it is either of the second or the second type

dataset = join.types(dataset, 'Nonsense point', 'Nonsense Ins/Del')
as.types(dataset)

TRONCO also provides functions for deleting specific events, samples or types.

dataset = delete.gene(aCML, gene = 'TET2')
dataset = delete.event(dataset, gene = 'ASXL1', type = 'Ins/Del')
dataset = delete.samples(dataset, samples = c('patient 5', 'patient 6'))
dataset = delete.type(dataset, type = 'Missense point')
view(dataset)

Modifying patterns

TRONCO provides functions to edit patterns, pretty much as for any other type of events. Patterns however have a special denotation and are supported only by CAPRI algorithm -- see Model Reconstruction with CAPRI to see a practical application of that.

Subsetting a dataset

It is very often the case that we want to subset a dataset by either selecting only some of its samples, or some of its events. Function samples.selection returns a dataset with only some selected samples.

dataset = samples.selection(aCML, samples = as.samples(aCML)[1:3])
view(dataset)

Function events.selection, instead, performs selection according to a filter of events. With this function, we can subset data according to a frequency, and we can force inclusion/exclusion of certain events by specifying their name. For instance, here we pick all events with a minimum frequency of 5%, force exclusion of SETBP1 (all events associated), and inclusion of EZH1 and EZH2.

dataset = events.selection(aCML,  filter.freq = .05, 
    filter.in.names = c('EZH1','EZH2'), 
    filter.out.names = 'SETBP1')

as.events(dataset)

An example visualization of the data before and after the selection process can be obtained by combining the gtable objects returned by \Rfunction{oncoprint}. We here use gtable = T to get access to have a GROB table returned, and silent = T to avoid that the calls to the function display on the device; the call to grid.arrange displays the captured gtable objects.

library(gridExtra)
grid.arrange(
    oncoprint(as.alterations(aCML, new.color = 'brown3'), 
        cellheight = 6, cellwidth = 4, gtable = TRUE,
        silent = TRUE, font.row = 6)$gtable,
    oncoprint(dataset, cellheight = 6, cellwidth = 4,
        gtable = TRUE, silent = TRUE, font.row = 6)$gtable, 
    ncol = 1)