vignettes/f2_loading_data.Rmd
f2_loading_data.RmdTRONCO transforms input data in a sort of database-alike format,
where three main fields are presente: genotypes which contains
the genomic signatures of the input samples, annotations which
provides an index to the events present in the data and types,
a field mapping type of events (e.g., mutations, CNAs, etc.) to colors
for display visualization. Other annotations are generated when a
dataset is augmented with some metadata. A TRONCO object shall be edited
by using TRONCO functions, to avoid to create inconsistencies in its
internal representation. Function is.compliant can be used
to test if a TRONCO object is consistent; the function is called by any
TRONCO function before returning a modified object, so to ensure that
consistency is preserved – is.compliant will raise an error
if this is not the case.
TRONCO supports the import of data from 3 formats. The Mutation Annotation Format (MAF) is a tab-delimited file containing somatic and/or germline mutation annotations; the GISTIC format for copy number alterations as defined by TCGA and a custom boolean matrix format where the user can directly specify the mutational profiles to be importend. Through some data included in the package we will show how to load your datasets in TRONCO.
Whatever is dataset created as explained in the next sections, it can
be annotated by adding a mnemonic description of the data, which will be
used as plot titles when possible. Function
annotate.description raises a warning if the dataset was
previously annotated.
aCML = annotate.description(aCML, 'aCML data (Bioinf.)')We use the function import.MAF to import a dataset in
MAF format, in this case the following TCGA dataset
head(crc_maf[, 1:10])## Hugo_Symbol Entrez_Gene_Id Center NCBI_Build Chromosome
## 27 TP53 7157 hgsc.bcm.edu 36 17
## 246 FBXW7 55294 hgsc.bcm.edu 36 4
## 623 APC 324 hgsc.bcm.edu 36 5
## 649 TP53 7157 hgsc.bcm.edu 36 17
## 928 FBXW7 55294 hgsc.bcm.edu 36 4
## 1390 TP53 7157 hgsc.bcm.edu 36 17
## Start_position End_position Strand Variant_Classification Variant_Type
## 27 7519131 7519131 + Missense_Mutation SNP
## 246 153466817 153466817 + Nonsense_Mutation SNP
## 623 112192485 112192485 + Nonsense_Mutation SNP
## 649 7518937 7518937 + Nonsense_Mutation SNP
## 928 153468834 153468834 + Missense_Mutation SNP
## 1390 7517864 7517864 + Missense_Mutation SNPA default importation is done without adding parameters to
import.MAF. In this case, all mutations per gene will be
considered equivalent, regardless of the type that is annotated in the
MAF. Also, all genes will be imported, and all samples.
dataset_maf = import.MAF(crc_maf)## *** Importing from dataframe
## Loading MAF dataframe ...DONE
## *** Mutations names: using Hugo_Symbol
## *** Using full MAF: #entries 17
## *** MAF report: TCGA=TRUE
## Type of annotated mutations:
## [1] "Missense_Mutation" "Nonsense_Mutation"
## *** [merge.mutation.types = T] Mutations will be merged and annotated as 'Mutation'
## Number of samples: 9
## [TCGA = TRUE] Number of TCGA patients: 9
## Number of annotated mutations: 17
## Mutations annotated with "Valid" flag (%): 71
## Number of genes (Hugo_Symbol): 6
## Starting conversion from MAF to 0/1 mutation profiles (1 = mutation) :9 x 6
## .................
## Starting conversion from MAF to TRONCO data type.In the above case – where we see that mutations are annotated as
Missense_Mutation or Nonsense_Mutation, if a
gene in a sample has both, these will be merged to a unique
Mutation type. In this case a pair gene name with
Mutation will be what we call an event in our
dataset – e.g., APC Mutation. If one would like to have two distinct
events in the dataset, i.e., APC Missense_Mutation and APC
Nonsense_Mutation, parameter
merge.mutation.types should be set to false in the call to
import.MAF.
dataset_maf = import.MAF(crc_maf, merge.mutation.types = FALSE)## *** Importing from dataframe
## Loading MAF dataframe ...DONE
## *** Mutations names: using Hugo_Symbol
## *** Using full MAF: #entries 17
## *** MAF report: TCGA=TRUE
## Type of annotated mutations:
## [1] "Missense_Mutation" "Nonsense_Mutation"
## *** [merge.mutation.types = F] Mutations will be distinguished by type
## Number of samples: 9
## [TCGA = TRUE] Number of TCGA patients: 9
## Number of annotated mutations: 17
## Mutations annotated with "Valid" flag (%): 71
## Number of genes (Hugo_Symbol): 6
## Starting conversion from MAF to 0/1 mutation profiles (1 = mutation) :
## .................Sometimes, we might want to filter out some of the entries in a MAF –
maybe restricting the type of genes, mutations or sample that we want to
process. If one defines filter.fun as a function that
returns TRUE only for those entries which shall be
considered, he gets a filter process which is applied to each row of the
MAF file prior to transforming that into a TRONCO dataset. In this
example we select only mutations annotated to APC – we access that
through the Hugo_Symbol flag of a MAF.
dataset_maf = import.MAF(crc_maf, filter.fun = function(x){ x['Hugo_Symbol'] == 'APC'} )## *** Importing from dataframe
## Loading MAF dataframe ...DONE
## *** Mutations names: using Hugo_Symbol
## *** Filtering full MAF: #entries 17
## *** Using reduced MAF: #entries 3
## *** MAF report: TCGA=TRUE
## Type of annotated mutations:
## [1] "Nonsense_Mutation"
## *** [merge.mutation.types = T] Mutations will be merged and annotated as 'Mutation'
## Number of samples: 3
## [TCGA = TRUE] Number of TCGA patients: 3
## Number of annotated mutations: 3
## Mutations annotated with "Valid" flag (%): 33
## Number of genes (Hugo_Symbol): 1
## Starting conversion from MAF to 0/1 mutation profiles (1 = mutation) :3 x 1
## ...
## Starting conversion from MAF to TRONCO data type.It is also sometimes convenient – especially when working with data
collected from a single individual patient – to distinguish the type of
mutations and their position in a gene, or if they are somehow annotated
to COSMIC or other databases. For instance, we might want to want to use
the MA.protein.change annotation in the MAF file to get
composite names such as TP53.R175H, TP53.R213, TP53.R267W etc. This can
be done by setting {} to have the relevant name of the MAF
annotation
dataset_maf = import.MAF(crc_maf,
merge.mutation.types = FALSE,
paste.to.Hugo_Symbol = c('MA.protein.change'))## *** Importing from dataframe
## Loading MAF dataframe ...DONE
## *** Mutations names: augmenting Hugo_Symbol with values: MA.protein.change
## *** Using full MAF: #entries 17
## *** MAF report: TCGA=TRUE
## Type of annotated mutations:
## [1] "Missense_Mutation" "Nonsense_Mutation"
## *** [merge.mutation.types = F] Mutations will be distinguished by type
## Number of samples: 9
## [TCGA = TRUE] Number of TCGA patients: 9
## Number of annotated mutations: 17
## Mutations annotated with "Valid" flag (%): 71
## Number of genes (Hugo_Symbol): 16
## Starting conversion from MAF to 0/1 mutation profiles (1 = mutation) :
## .................TRONCO supports custom MAF files, where possibly not all the standard
annotations are present, via irregular = TRUE.
We use the function import.GISTIC to import a dataset in
GISTIC format, in this case from
crc_gistic## NRAS CTNNB1 FBXW7 APC KRAS TP53
## TCGA-A6-2670 -1 0 0 -1 1 -1
## TCGA-A6-2672 0 0 0 0 0 0
## TCGA-A6-2674 0 0 0 0 0 0
## TCGA-A6-2676 0 0 0 0 0 0
## TCGA-A6-2677 0 0 0 0 0 -1
## TCGA-A6-2678 0 0 0 0 0 -1
## TCGA-A6-2683 -1 -1 -1 -1 0 -1
## TCGA-A6-3807 0 -1 0 0 -1 -1
## TCGA-AA-3516 0 0 0 0 0 0In its default execution all the data annotated in the file is
imported. But in principle it is possible to avoid to import some genes
or samples; in this case it is sufficient to use parameters
filter.genes and filter.samples for this
function.
dataset_gistic = import.GISTIC(crc_gistic)## *** Using full GISTIC: #dim 9 x 6
## *** GISTIC input format conversion started.
## Converting input data to character for import speedup.
## Creating 24 events for 6 genes
## Extracting "Homozygous Loss" events (GISTIC = -2)
## Extracting "Heterozygous Loss" events (GISTIC = -1)
## Extracting "Low-level Gain" events (GISTIC = +1)
## Extracting "High-level Gain" events (GISTIC = +2)
## Transforming events in TRONCO data types .....
## *** Binding events for 4 datasets.
## *** Data extracted, returning only events observed in at least one sample
## Number of events: n = 7
## Number of genes: |G| = 6
## Number of samples: m = 9One can annotate its custom type of alterations in a boolean matrix
such as crc_plain
crc_plain## TP53 FBXW7 APC CTNNB1 NRAS KRAS
## TCGA-AA-3517-01 1 0 0 0 0 0
## TCGA-AA-3518-01 0 1 0 0 0 0
## TCGA-AA-3519-01 1 0 1 0 0 0
## TCGA-AA-3520-01 1 0 0 0 0 1
## TCGA-AA-3521-01 0 0 1 0 0 1In this case, function import.genotypes will convert the
matrix to a TRONCO object where events’ names and samples codes will be
set from column and row names of the matrix. If this is not possible,
these will be generated from templates. By default, the
event.type is set to variant but one can
specify a custom name for the alteration that is reported in the
matrix
dataset_plain = import.genotypes(crc_plain, event.type='myVariant')TRONCO uses the R interface to cBio to query data from the portal. All type of data can be downloaded from the portal, which includes MAF/GISTIC data for a lot of different cancer studies. An example of interaction with the portal is archived at the tool’s webpage.
Here, we show how to download lung cancer data somatic mutations for
genes TP53, KRAS and PIK3CA, from the lung cancer project run by TCGA,
which is archived as luad_tcga_pub at cBio. If some of the
parameters to cbio.query are missing the function will
become interactive by showing a list of possible data available at the
portal.
data = cbio.query(
genes=c('TP53', 'KRAS', 'PIK3CA'),
cbio.study = 'luad_tcga_pub',
cbio.dataset = 'luad_tcga_pub_cnaseq',
cbio.profile = 'luad_tcga_pub_mutations')