CELLector.Tumours_buildBEM: Building a Genomic Binary Event Matrix (BEM) for primary...

In najha/CELLector: Genomics guided selection of cancer cell lines

Building a Genomic Binary Event Matrix (BEM) for primary tumours

Description

This function takes in input a catalogue of somatic genomic variants observed in primary tumours (or it uses a built in catalogue from TCGA, presented in [1]) and it converts it into a presence/absence (binary) matrix, which can be processed by the CELLector package and CELLector shiny app for identifying patient subtypes, and map in vitro models onto these.

Usage

CELLector.Tumours_buildBEM(varCat = NULL,
                           Cancer_Type,
                           GenesToConsider = NULL,
                           VariantsToConsider = NULL)

Arguments

varCat	A data frame containing a catalogue of somatic genomic variants observed in primary tumours, with one row per variant. The format should be the same of the CELLector.PrimTumVarCatalog data object (which is used when this parameter is set to its default NULL value) or at least contain the following column headers SAMPLE, Cancer.Type, Gene, cDNA, AA, Recurrence.Filter
Cancer_Type	A string specifying the cancer type for which individual variants should be extracted from the catalogue and assembled into the final matrix. It must be a value included in the Cancer.Type column of the varCat data object
GenesToConsider	A list of strings with HGNC symbols [2] for genes hosting the variants to be extracted from the catalogue and assembled into the final matrix. When set to its default NULL value, all genes hosting at least one variants are considered.
VariantsToConsider	A list of individual somatic variants to be extracted from the catalogue and assembled into the final matrix. The format should be the same of the CELLector.RecfiltVariants (which is used when this parameter is set to its default NULL value)

Value

A presence/absence (binary) matrix with gene symbols on the rows and patient sample ids on the columns, specifying in the i,j-entry the status of the ith gene in the jth patient sample, i.e. 0 = wild-type, 1 = mutated.

Author(s)

Francesco Iorio ([e-mail])

References

[1] Iorio, F. et al. A Landscape of Pharmacogenomic Interactions in Cancer. Cell 166, 740–754 (2016).

[2] Braschi, B. et al. Genenames.org: the HGNC and VGNC resources in 2019. Nucleic Acids Res. Epub 2018 Oct 10. PMID: 30304474 DOI: 10.1093/nar/gky930

[3] Tate JG, Bamford S, Jubb HC, et al. COSMIC: the Catalogue Of Somatic Mutations In Cancer. Nucleic Acids Res. 2019;47(D1):D941–D947. doi:10.1093/nar/gky1015

See Also

CELLector.PrimTumVarCatalog, CELLector.CELLline_buildBEM

Examples

## loading high-confidence cancer driver genes from [1]
data(CELLector.HCCancerDrivers)

## loading COSMIC [3] variants observed it at least two patients from [1]
data(CELLector.RecfiltVariants)

## Assembling a BRCA primary tumour binary event matrix (BEM)
BRCA_tum_BEM<-
  CELLector.Tumours_buildBEM(Cancer_Type = 'BRCA',
                             VariantsToConsider =
                             CELLector.RecfiltVariants)

## showing first 100 entries of the BEM
BRCA_tum_BEM[1:10,1:10]

## showing a bar diagram with mutation frequency of 30 top frequently altered genes
barplot(100*sort(rowSums(BRCA_tum_BEM),
                 decreasing=TRUE)[1:30]/ncol(BRCA_tum_BEM),
                 las=2,ylab='% patients')

najha/CELLector documentation built on Feb. 8, 2023, 5:35 a.m.