Data manipulation

Luca De Sano, Daniele Ramazzotti, Giulio Caravagna, Alex Graudenxi and Marco Antoniotti

Package

TRONCO 2.39.0

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

0.1 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')

##        type       event     
## gene 4 "new type" "new name"
## gene 5 "new type" "EZH2"    
## gene 6 "new type" "CBL"     
## gene 7 "new type" "ASXL1"

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')

## *** Binding events for 2 datasets.

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')

## *** Aggregating events of type(s) { Nonsense point, Nonsense Ins/Del }
## in a unique event with label " new.type ".
## Dropping event types Nonsense point, Nonsense Ins/Del for 6 genes.
## ......
## *** Binding events for 2 datasets.

as.types(dataset)

## [1] "Ins/Del"        "Missense point" "banana"         "new.type"

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)

## Description: CAPRI - Bionformatics aCML data.
## -- TRONCO Dataset: n=62, m=8, |G|=7, patterns=0.
## Events (types): Ins/Del, Nonsense Ins/Del, Nonsense point.
## Colors (plot): #7FC97F, #FDC086, #fab3d8.
## Events (5 shown):
##   gene 5 : Ins/Del EZH2
##   gene 6 : Ins/Del CBL
##   gene 66 : Nonsense Ins/Del WT1
##   gene 69 : Nonsense Ins/Del RUNX1
##   gene 77 : Nonsense Ins/Del CEBPA
## Genotypes (5 shown):

0.2 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.

0.3 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)

## Description: CAPRI - Bionformatics aCML data.
## -- TRONCO Dataset: n=3, m=31, |G|=23, patterns=0.
## Events (types): Ins/Del, Missense point, Nonsense Ins/Del, Nonsense point.
## Colors (plot): #7FC97F, #4483B0, #FDC086, #fab3d8.
## 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):

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')

## *** Events selection: #events =  31 , #types =  4 Filters freq|in|out = { TRUE ,  TRUE ,  TRUE }
## Minimum event frequency:  0.05  ( 3  alterations out of  64  samples).
## ...............................
## Selected  9  events.
## 
## [filter.in] Genes hold:  EZH1, EZH2  ...  [ 1 / 2  found].
## [filter.out] Genes dropped:  SETBP1  ...  [ 1 / 1  found].
## Selected  10  events, returning.

as.events(dataset)

##         type             event  
## gene 4  "Ins/Del"        "TET2" 
## gene 5  "Ins/Del"        "EZH2" 
## gene 7  "Ins/Del"        "ASXL1"
## gene 30 "Missense point" "NRAS" 
## gene 32 "Missense point" "TET2" 
## gene 33 "Missense point" "EZH2" 
## gene 55 "Missense point" "CSF3R"
## gene 88 "Nonsense point" "TET2" 
## gene 89 "Nonsense point" "EZH2" 
## gene 91 "Nonsense point" "ASXL1"

An example visualization of the data before and after the selection process can be obtained by combining the gtable objects returned by . 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)

Figure 1: Multiple output from oncoprint can be captured as a gtable and composed via grid.arrange (package gridExtra)
In this case we show aCML data on top – displayed after the as.alterations transformation – versus a selected subdataset of events with a minimum frequency of 5%, force exclusion of SETBP1 (all events associated), and inclusion of EZH1 and EZH2.