Data visualization
Luca De Sano
Daniele Ramazzotti
Giulio Caravagna
Alex Graudenxi
Marco Antoniotti
October 31, 2024
Source:vignettes/f3_data_visualization.Rmd
f3_data_visualization.RmdAll examples in this section will be done with the the aCML dataset as reference.
Summary report for a dataset and boolean queries
We use the function view to get a short summary of a
dataset that we loaded in TRONCO; this function reports on the number of
samples and events, plus some meta information that could be displayed
graphically.
view(aCML)## Description: CAPRI - Bionformatics aCML data.
## -- TRONCO Dataset: n=64, 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):Creating views with the as functions
Several functions are available to create views over a dataset, with a set of parameter which can constraint the view – as in the SELECT/JOIN approaches in databases. In the following examples we show their execution with the default parameters, but shorten their output to make this document readable.
The main as functions are here documented.
as.genotypes, that we can use to get the matrix of
genotypes that we imported.
as.genotypes(aCML)[1:10,5:10]## gene 29 gene 30 gene 31 gene 32 gene 33 gene 34
## patient 1 1 0 0 0 0 0
## patient 2 1 0 0 0 0 1
## patient 3 1 1 0 0 0 0
## patient 4 1 0 0 0 0 1
## patient 5 1 0 0 0 0 0
## patient 6 1 0 0 0 0 0
## patient 7 1 0 0 0 0 0
## patient 8 1 0 0 0 0 0
## patient 9 1 0 0 0 0 0
## patient 10 1 0 0 0 0 0Differently, as.events and
as.events.in.samples, that show tables with the events that
we are processing in all dataset or in a specific sample that we want to
examine.
as.events(aCML)[1:5, ]## type event
## 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"
as.events.in.sample(aCML, sample = 'patient 2')## type event
## gene 29 "Missense point" "SETBP1"
## gene 34 "Missense point" "CBL"
## gene 91 "Nonsense point" "ASXL1"Concerning genes, as.genes shows the mnemonic names of
the genes (or chromosomes, cytobands, etc.) that we included in our
dataset.
as.genes(aCML)[1:8]## [1] "TET2" "EZH2" "CBL" "ASXL1" "SETBP1" "NRAS" "KRAS" "IDH2"And as.types shows the types of alterations (e.g.,
mutations, amplifications, etc.) that we have find in our dataset, and
function as.colors shows the list of the colors which are
associated to each type.
as.types(aCML)## [1] "Ins/Del" "Missense point" "Nonsense Ins/Del" "Nonsense point"
as.colors(aCML)## Ins/Del Missense point Nonsense Ins/Del Nonsense point
## "#7FC97F" "#4483B0" "#FDC086" "#fab3d8"A function as.gene can be used to display the
alterations of a specific gene across the samples
## Missense point SETBP1
## patient 1 1
## patient 2 1
## patient 3 1
## patient 4 1
## patient 5 1
## patient 6 1Views over samples can be created as well. as.samples
and which.samples list all the samples in the data, or
return a list of samples that harbour a certain alteration. The former
is
as.samples(aCML)[1:10]## [1] "patient 1" "patient 2" "patient 3" "patient 4" "patient 5"
## [6] "patient 6" "patient 7" "patient 8" "patient 9" "patient 10"and the latter is
which.samples(aCML, gene='TET2', type='Nonsense point')## [1] "patient 12" "patient 13" "patient 26" "patient 29" "patient 40"
## [6] "patient 57" "patient 62"A slightly different function, which manipulates the data, is
as.alterations, which transforms a dataset with events of
different type to events of a unique type, labeled
Alteration.
dataset = as.alterations(aCML)## *** Aggregating events of type(s) { Ins/Del, Missense point, Nonsense Ins/Del, Nonsense point }
## in a unique event with label " Alteration ".
## Dropping event types Ins/Del, Missense point, Nonsense Ins/Del, Nonsense point for 23 genes.
## .......................
## *** Binding events for 2 datasets.
view(dataset)## -- TRONCO Dataset: n=64, m=23, |G|=23, patterns=0.
## Events (types): Alteration.
## Colors (plot): khaki.
## Events (5 shown):
## G1 : Alteration TET2
## G2 : Alteration EZH2
## G3 : Alteration CBL
## G4 : Alteration ASXL1
## G5 : Alteration SETBP1
## Genotypes (5 shown):When samples are enriched with stage information function
as.stages can be used to create a view over such table.
Views over patterns can be created as well – see Model Inference with
CAPRI.
Dataset size
A set of functions allow to get the number of genes, events, samples, types and patterns in a dataset.
ngenes(aCML)## [1] 23
nevents(aCML)## [1] 31
nsamples(aCML)## [1] 64
ntypes(aCML)## [1] 4
npatterns(aCML)## [1] 0Oncoprints
Oncoprints are the most effective data-visualization functions in TRONCO. These are heatmaps where rows represent variants, and columns samples (the reverse of the input format required by TRONCO), and are annotated and displayed/sorted to enhance which samples have which mutations etc.
By default oncoprint will try to sort samples and events
to enhance exclusivity patterns among the events.
oncoprint(aCML)## *** Oncoprint for "CAPRI - Bionformatics aCML data"
## with attributes: stage = FALSE, hits = TRUE
## Sorting samples ordering to enhance exclusivity patterns.
## Setting automatic row font (exponential scaling): 8.1
This plot gives a graphical visualization of the events that are in the dataset – with a color per event type. It sorts samples to enhance exclusivity patterns among the events
But the sorting mechanism is bypassed if one wants to cluster samples
or events, or if one wants to split samples by cluster (not shown). In
the clustering case, the ordering is given by the dendrograms. In this
case we also show the annotation of some groups of events via parameter
gene.annot.
oncoprint(aCML,
legend = FALSE,
samples.cluster = TRUE,
gene.annot = list(one = list('NRAS', 'SETBP1'), two = list('EZH2', 'TET2')),
gene.annot.color = 'Set2',
genes.cluster = TRUE)## *** Oncoprint for "CAPRI - Bionformatics aCML data"
## with attributes: stage = FALSE, hits = TRUE
## Sorting samples ordering to enhance exclusivity patterns.
## Annotating genes with RColorBrewer color palette Set2 .
## Setting automatic row font (exponential scaling): 8.1
## Clustering samples and showing dendogram.
## Clustering alterations and showing dendogram.
This plot gives a graphical visualization of the events that are in the dataset – with a color per event type. It it clusters samples/events
Oncoprints can be annotated; a special type of annotation is given by stage data. As this is not available for the aCML dataset, we create it randomly, just for the sake of showing how the oncoprint is enriched with this information. This is the random stage map that we create – if some samples had no stage a NA would be added automatically.
stages = c(rep('stage 1', 32), rep('stage 2', 32))
stages = as.matrix(stages)
rownames(stages) = as.samples(aCML)
dataset = annotate.stages(aCML, stages = stages)
has.stages(aCML)## [1] FALSE## stage
## patient 1 stage 1
## patient 2 stage 1
## patient 3 stage 1
## patient 4 stage 1
## patient 5 stage 1
## patient 6 stage 1The as.stages function can now be used to create a view
over stages.
## stage
## patient 1 stage 1
## patient 2 stage 1
## patient 3 stage 1
## patient 4 stage 1
## patient 5 stage 1
## patient 6 stage 1After that the data is annotated via annotate.stages
function, we can again plot an oncoprint – which this time will detect
that the dataset has also stages associated, and will diplay those
oncoprint(dataset, legend = FALSE)## *** Oncoprint for "CAPRI - Bionformatics aCML data"
## with attributes: stage = TRUE, hits = TRUE
## Sorting samples ordering to enhance exclusivity patterns.
## Annotating stages with RColorBrewer color palette YlOrRd
## Setting automatic row font (exponential scaling): 8.1
If one is willing to display samples grouped according to some
variable, for instance after a sample clustering task, he can use
group.samples parameter of oncoprint and that
will override the mutual exclusivity ordering. Here, we make the trick
of using the stages as if they were such clustering result.
## *** Oncoprint for "CAPRI - Bionformatics aCML data"
## with attributes: stage = TRUE, hits = TRUE
## Sorting samples ordering to enhance exclusivity patterns.
## Grouping samples according to input groups (group.samples).
## Annotating stages with RColorBrewer color palette YlOrRd
## Grouping labels: stage 1, stage 2
## Setting automatic row font (exponential scaling): 8.1
Example exttt{oncoprint} output for aCML data with randomly annotated stages, in left, and samples clustered by group assignment in right – for simplicity the group variable is again the stage annotation.
Groups visualization (e.g., pathways)
TRONCO provides functions to visualize groups of events, which in
this case are called pathways – though this could be any group that one
would like to define. Aggregation happens with the same rational as the
as.alterations function, namely by merging the events in
the group.
We make an example of a pathway called MyPATHWAY involving genes SETBP1, EZH2 and WT1; we want it to be colored in red, and we want to have the genotype of each event to be maintened in the dataset. We proceed as follows (R’s output is omitted).
pathway = as.pathway(aCML,
pathway.genes = c('SETBP1', 'EZH2', 'WT1'),
pathway.name = 'MyPATHWAY',
pathway.color = 'red',
aggregate.pathway = FALSE)## *** Extracting events for pathway: MyPATHWAY .
## *** Events selection: #events = 31 , #types = 4 Filters freq|in|out = { FALSE , TRUE , FALSE }
## [filter.in] Genes hold: SETBP1, EZH2, WT1 ... [ 3 / 3 found].
## Selected 5 events, returning.
## Pathway extracted succesfully.
## *** Binding events for 2 datasets.Which we then visualize with an oncoprint
oncoprint(pathway, title = 'Custom pathway', font.row = 8, cellheight = 15, cellwidth = 4)## *** Oncoprint for "Custom pathway"
## with attributes: stage = FALSE, hits = TRUE
## Sorting samples ordering to enhance exclusivity patterns.
Oncoprint output of a custom pathway called MyPATHWAY involving genes SETBP1, EZH2 and WT1; the genotype of each event is shown.
In TRONCO there is also a function which creates the pathway view and the corresponding oncoprint to multiple pathways, when these are given as a list. We make here a simple example of two custom pathways.
pathway.visualization(aCML,
pathways=list(P1 = c('TET2', 'IRAK4'), P2=c('SETBP1', 'KIT')),
aggregate.pathways=FALSE,
font.row = 8)## Annotating pathways with RColorBrewer color palette Set2 .
## *** Processing pathways: P1, P2
##
## [PATHWAY "P1"] TET2, IRAK4
## *** Extracting events for pathway: P1 .
## *** Events selection: #events = 31 , #types = 4 Filters freq|in|out = { FALSE , TRUE , FALSE }
## [filter.in] Genes hold: TET2, IRAK4 ... [ 2 / 2 found].
## Selected 4 events, returning.
## Pathway extracted succesfully.
## *** Binding events for 2 datasets.
##
##
## [PATHWAY "P2"] SETBP1, KIT
## *** Extracting events for pathway: P2 .
## *** Events selection: #events = 31 , #types = 4 Filters freq|in|out = { FALSE , TRUE , FALSE }
## [filter.in] Genes hold: SETBP1, KIT ... [ 2 / 2 found].
## Selected 2 events, returning.
## Pathway extracted succesfully.
## *** Binding events for 2 datasets.
## *** Binding events for 2 datasets.
## *** Oncoprint for "Pathways: P1, P2"
## with attributes: stage = FALSE, hits = TRUE
## Sorting samples ordering to enhance exclusivity patterns.
Oncoprint output of a custom pair of pathways, with events shown
## NULLIf we had to visualize just the signature of the pathway, we could
set aggregate.pathways=T.
pathway.visualization(aCML,
pathways=list(P1 = c('TET2', 'IRAK4'), P2=c('SETBP1', 'KIT')),
aggregate.pathways = TRUE,
font.row = 8)## Annotating pathways with RColorBrewer color palette Set2 .
## *** Processing pathways: P1, P2
##
## [PATHWAY "P1"] TET2, IRAK4
## *** Extracting events for pathway: P1 .
## *** Events selection: #events = 31 , #types = 4 Filters freq|in|out = { FALSE , TRUE , FALSE }
## [filter.in] Genes hold: TET2, IRAK4 ... [ 2 / 2 found].
## Selected 4 events, returning.
## Pathway extracted succesfully.
##
##
## [PATHWAY "P2"] SETBP1, KIT
## *** Extracting events for pathway: P2 .
## *** Events selection: #events = 31 , #types = 4 Filters freq|in|out = { FALSE , TRUE , FALSE }
## [filter.in] Genes hold: SETBP1, KIT ... [ 2 / 2 found].
## Selected 2 events, returning.
## Pathway extracted succesfully.
## *** Binding events for 2 datasets.
## *** Oncoprint for "Pathways: P1, P2"
## with attributes: stage = FALSE, hits = TRUE
## Sorting samples ordering to enhance exclusivity patterns.
Oncoprint output of a custom pair of pathways, with events hidden
## NULLThe same operation could have been done using WikiPathways. We can query WikiPathways and collect HGNC gene symbols and titles for pathways of interest as follows. (R’s output is omitted).
library(rWikiPathways)
# quotes inside query to require both terms
my.pathways <- findPathwaysByText('SETBP1 EZH2 TET2 IRAK4 SETBP1 KIT')
human.filter <- lapply(my.pathways, function(x) x$species == "Homo sapiens")
my.hs.pathways <- my.pathways[unlist(human.filter)]
# collect pathways idenifiers
my.wpids <- sapply(my.hs.pathways, function(x) x$id)
pw.title<-my.hs.pathways[[1]]$name
pw.genes<-getXrefList(my.wpids[1],"H") Now pw.genes and pw.title can be used as
input for the function as.pathway. It is also possible to
view and edit these pathways at WikiPathways using the following
commands to open tabs in your default browser.
browseURL(getPathwayInfo(my.wpids[1])[2])
browseURL(getPathwayInfo(my.wpids[2])[2])
browseURL(getPathwayInfo(my.wpids[3])[2])