TRONCO 2.36.0
library(TRONCO)
data(aCML)
data(crc_maf)
data(crc_gistic)
data(crc_plain)TRONCO 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 {paste.to.Hugo_Symbol} 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')