Installation
IMPORTANT NOTE: geneOncoX requires that you have R version 4.1 or higher installed
if (!("remotes" %in% installed.packages())) {
install.packages("remotes")
}
remotes::install_github('sigven/geneOncoX')Get basic gene annotations
This shows how to retrieve basic gene and cancer relevant gene annotations, including how to retrieve tumor suppressor genes, proto-oncogenes, and predicted cancer driver genes.
library(geneOncoX)
## load the data
download_dir <- tempdir()
gene_basic <- get_basic(cache_dir = download_dir)
#> INFO [2025-05-22 14:00:17] Downloading remote dataset from Google Drive to cache_dir
#> INFO [2025-05-22 14:00:21] Reading from cache_dir = '/tmp/RtmpZglwrL', argument force_download = FALSE
#> INFO [2025-05-22 14:00:21] Object 'gene_basic' sucessfully loaded
#> INFO [2025-05-22 14:00:21] md5 checksum is valid: 3bd4f8163fe98ac5480397bff87dd23d
#> INFO [2025-05-22 14:00:21] Retrieved 65071 records
## Number of records
nrow(gene_basic$records)
#> [1] 65071
## Show metadata for underlying resources
gene_basic$metadata
#> source
#> 1 CancerMine
#> 2 Network of Cancer Genes
#> 3 IntoGen
#> 4 NCBI
#> 5 Bailey et al., Cell, 2018
#> 6 Sanchez-Vega et al., Cell, 2018
#> 7 F1CDx
#> 8 TSO500
#> 9 DNA repair gene database
#> 10 dbNSFP
#> 11 CPIC
#> source_description
#> 1 Predicted tumor suppressors/oncogenes/cancer drivers from text mining of literature
#> 2 Tumor suppressors/oncogenes
#> 3 Predicted cancer driver genes
#> 4 Basic gene identifiers (symbol, entrez ID, HGNC ID, name, synonyms, function summary)
#> 5 Predicted cancer driver genes / likely false positive driver genes
#> 6 Collection of curated signalling pathway annotations
#> 7 Collection of genes covered by Foundation One's F1CDx cancer gene panel
#> 8 Collection of genes covered by Illumina's TSO500 cancer gene panel
#> 9 Collection of genes involved in DNA repair
#> 10 Gene indispensability prediction, loss-of-function intolerance predictions etc.
#> 11 Clinical Pharmacogenomics Implementation Consortium - gene/oncology drug pairs
#> source_url
#> 1 http://bionlp.bcgsc.ca/cancermine/
#> 2 http://network-cancer-genes.org/
#> 3 https://www.intogen.org/download
#> 4 https://www.ncbi.nlm.nih.gov/gene/
#> 5 https://pubmed.ncbi.nlm.nih.gov/29625053/
#> 6 https://pubmed.ncbi.nlm.nih.gov/29625050/
#> 7 https://www.foundationmedicine.com/test/foundationone-cdx
#> 8 https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0260089
#> 9 https://panelapp.genomicsengland.co.uk/panels/256/
#> 10 https://sites.google.com/site/jpopgen/dbNSFP
#> 11 https://cpicpgx.org/genes-drugs/
#> source_citation source_version
#> 1 Lever et al., Nat Methods, 2019; 31110280 v50 (March 2023)
#> 2 Repana et al., Genome Biol, 2019; 30606230 v7.1
#> 3 Martínez-Jiménez et al., Nat Rev Cancer, 2020; 32778778 2024.09.20
#> 4 Brown et al., Nucleic Acids Res, 2015; 25355515 2025-05-21
#> 5 Bailey et al., Cell, 2018; 29625053 <NA>
#> 6 Sanchez-Vega et al., Cell, 2018; 29625050 <NA>
#> 7 <NA> <NA>
#> 8 <NA> <NA>
#> 9 Woods et al., Science, 2001; 11181991 v1.1
#> 10 Liu et al., Genome Med, 2020; 33261662 v5.0
#> 11 Caudle et al.,Curr Drug Metab, 2014; 24479687 <NA>
#> source_abbreviation source_license
#> 1 cancermine CC0 1.0
#> 2 ncg Free/open access
#> 3 intogen CC0 1.0
#> 4 ncbi NCBI data usage policies
#> 5 bailey2018 Free/open access
#> 6 sanchezvega2018 Free/open access
#> 7 foundation_one Free/open access
#> 8 illumina Free/open access
#> 9 woods_dnarepair Free/open access
#> 10 dbnsfp Free for non-commercial, academic use.
#> 11 cpic CC0 1.0
#> source_license_url
#> 1 https://creativecommons.org/publicdomain/zero/1.0/
#> 2 <NA>
#> 3 https://creativecommons.org/publicdomain/zero/1.0/
#> 4 https://www.ncbi.nlm.nih.gov/home/about/policies/#data
#> 5 <NA>
#> 6 <NA>
#> 7 <NA>
#> 8 <NA>
#> 9 <NA>
#> 10 <NA>
#> 11 <NA>Get classified tumor suppressor genes
## Get tumor suppressor genes - as indicated from either
## Network of Cancer Genes (NCG)
## - show literature support from CancerMine
tsg <- gene_basic$records |>
dplyr::filter(ncg_tsg == TRUE) |>
dplyr::select(symbol, entrezgene, name, gene_biotype,
ncg_tsg, cancermine_cit_links_tsg,
ncbi_function_summary) |>
dplyr::rename(support_cancermine = cancermine_cit_links_tsg)
## Make as datatable
tsg_table <- DT::datatable(
tsg,
escape = FALSE,
extensions = c("Buttons", "Responsive"),
width = "100%",
options = list(
buttons = c("csv", "excel"), dom = "Bfrtip"))Get classified proto-oncogenes
## Get proto-oncogenes - as indicated from either
## Cancer Gene Census (CGC) or Network of Cancer Genes (NCG),
## - show literature support from CancerMine
oncogene <- gene_basic$records |>
dplyr::filter(ncg_oncogene == TRUE) |>
dplyr::select(symbol, entrezgene,
name, gene_biotype,
ncg_oncogene,
cancermine_cit_links_oncogene,
ncbi_function_summary) |>
dplyr::rename(support_cancermine =
cancermine_cit_links_oncogene)
## Make as datatable
oncogene_table <- DT::datatable(
oncogene,
escape = FALSE,
extensions = c("Buttons", "Responsive"),
width = "100%",
options = list(
buttons = c("csv", "excel"),
dom = "Bfrtip"))Get predicted cancer driver genes
## Get predicted cancer driver genes - as indicated from either
## - Network of Cancer Genes (NCG) - canonical drivers
## - IntOGen mutational driver catalogue
## - TCGA's PanCancer driver prediction (Bailey et al., Cell, 2018)
##
## Rank hits by how many sources that contribute to classification
##
cancer_driver <- gene_basic$records |>
dplyr::filter(intogen_driver == TRUE |
ncg_driver == TRUE |
tcga_driver == TRUE) |>
dplyr::mutate(driver_score = 0) |>
# dplyr::mutate(driver_score = dplyr::if_else(
# cgc_driver_tier1 == T,
# driver_score + 2,
# as.numeric(driver_score))) |>
# dplyr::mutate(driver_score = dplyr::if_else(
# cgc_driver_tier2 == T,
# driver_score + 1,
# as.numeric(driver_score))) |>
dplyr::mutate(driver_score = dplyr::if_else(
intogen_driver == T,
driver_score + 1,
as.numeric(driver_score))) |>
dplyr::mutate(driver_score = dplyr::if_else(
ncg_driver == T,
driver_score + 1,
as.numeric(driver_score))) |>
dplyr::mutate(driver_score = dplyr::if_else(
tcga_driver == T,
driver_score + 1,
as.numeric(driver_score))) |>
dplyr::select(symbol, entrezgene,
name, gene_biotype,
intogen_driver,
intogen_role,
ncg_driver,
ncg_phenotype,
tcga_driver,
driver_score,
cancermine_cit_links_driver,
ncbi_function_summary) |>
dplyr::rename(support_cancermine =
cancermine_cit_links_driver) |>
dplyr::arrange(dplyr::desc(driver_score), symbol)
## Make as datatable
driver_table <- DT::datatable(
cancer_driver,
escape = FALSE,
extensions = c("Buttons", "Responsive"),
width = "100%",
options = list(
buttons = c("csv", "excel"),
dom = "Bfrtip"))Get cancer predisposition genes
This show how to retrieve known cancer predisposition genes, utilizing multiple sources, including Cancer Gene Census, Genomics England PanelApp, TCGA’s pan-cancer study of germline variants, and other/user-curated entries.
## load the data
gene_predisposition <- get_predisposition(cache_dir = download_dir)
#> INFO [2025-05-22 14:00:22] Downloading remote dataset from Google Drive to cache_dir
#> INFO [2025-05-22 14:00:23] Reading from cache_dir = '/tmp/RtmpZglwrL', argument force_download = FALSE
#> INFO [2025-05-22 14:00:23] Object 'gene_predisposition' sucessfully loaded
#> INFO [2025-05-22 14:00:23] md5 checksum is valid: 0f7ff4bad851216cee9e1d233609f2c7
#> INFO [2025-05-22 14:00:23] Retrieved 607 records
## Number of cancer predisposition genes
nrow(gene_predisposition$records |> dplyr::filter(
!stringr::str_detect(cpg_source, "^(ACMG_SF|CPIX_PGX_ONCOLOGY)$")
))
#> [1] 558
## Get statistics regarding how reference sources on
## cancer predisposition genes contribute
##
## CGC - Cancer Gene Census (germline)
## PANEL_APP - N = 43 gene panels for inherited cancer conditions/
## cancer syndromes (Genomics England PanelApp)
## CURATED_OTHER - curated/user-contributed genes
## TCGA_PANCAN_2018 - TCGA's pancancer analysis of
## germline variants in cancer
## (Huang et al., Cell, 2019)
plyr::count(gene_predisposition$records$cpg_source) |>
dplyr::arrange(dplyr::desc(freq)) |>
dplyr::filter(x != "ACMG_SF") |>
dplyr::filter(x != "CPIC_PGX_ONCOLOGY")
#> x freq
#> 1 PANEL_APP 275
#> 2 CURATED_OTHER 115
#> 3 CANVAR_UK&PANEL_APP&TCGA_PANCAN_2018 68
#> 4 PANEL_APP&TCGA_PANCAN_2018 34
#> 5 ACMG_SF&CANVAR_UK&PANEL_APP&TCGA_PANCAN_2018 24
#> 6 CANVAR_UK&PANEL_APP 15
#> 7 TCGA_PANCAN_2018 12
#> 8 ACMG_SF&PANEL_APP&TCGA_PANCAN_2018 4
#> 9 ACMG_SF&PANEL_APP 2
#> 10 CANVAR_UK 2
#> 11 CANVAR_UK&TCGA_PANCAN_2018 2
#> 12 ACMG_SF&CANVAR_UK&TCGA_PANCAN_2018 1
#> 13 ACMG_SF&TCGA_PANCAN_2018 1
## Cancer predisposition metadata
gene_predisposition$metadata
#> source
#> 1 Genomics England PanelApp
#> 2 NCBI
#> 3 Huang et al., Cell, 2018
#> 4 Maxwell et al., Am J Hum Genet, 2016
#> 5 Cancer predisposition genes - curated/other
#> 6 ACMG - secondary findings
#> 7 CanVar-UK
#> source_description
#> 1 Collection of > 40 dedicated gene panels for various inherited cancer conditions and syndromes
#> 2 Basic gene identifiers (symbol, entrez ID, HGNC ID, name, synonyms, function summary)
#> 3 Collection of cancer predisposition genes screened in TCGA's pancancer study
#> 4 Mechanisms of inheritance for cancer predisposition genes
#> 5 Candidate cancer predisposition genes - contributed e.g. by CPSR users
#> 6 Genes recommended for reporting of incidental findings in clinical exome sequencing
#> 7 Cancer predisposition gene variant database
#> source_url
#> 1 https://panelapp.genomicsengland.co.uk/
#> 2 https://www.ncbi.nlm.nih.gov/gene/
#> 3 https://pubmed.ncbi.nlm.nih.gov/29625052/
#> 4 https://pubmed.ncbi.nlm.nih.gov/27153395/
#> 5 <NA>
#> 6 https://pubmed.ncbi.nlm.nih.gov/35802134/
#> 7 https://canvaruk.org/
#> source_citation source_version
#> 1 Martin et al., Nat Genet, 2019; 31676867 v1 (API)
#> 2 Brown et al., Nucleic Acids Res, 2015; 25355515 2025-05-21
#> 3 Huang et al., Cell, 2018; 29625052 <NA>
#> 4 Maxwell et al., Am J Hum Genet, 2016; 27153395 <NA>
#> 5 <NA> 20250305
#> 6 Miller et al., Genet Med, 2023; 37347242 v3.2
#> 7 <NA> v2.2
#> source_abbreviation source_license
#> 1 gepa Commercial use requires separate agreement
#> 2 ncbi NCBI data usage policies
#> 3 tcga_pancan_2018 Free/open access
#> 4 maxwell2016 Free/open access
#> 5 cpg_other Free/open access
#> 6 acmg_sf Free/open access
#> 7 canvar_uk Free/open access
#> source_license_url
#> 1 https://panelapp.genomicsengland.co.uk/media/files/GEL_-_PanelApp_Terms_of_Use_December_2019.pdf
#> 2 https://www.ncbi.nlm.nih.gov/home/about/policies/#data
#> 3 <NA>
#> 4 <NA>
#> 5 <NA>
#> 6 https://www.ncbi.nlm.nih.gov/clinvar/docs/acmg/
#> 7 <NA>Get cancer gene panels
This shows how to retrieve genes from cancer gene panels defined in Genomics England PanelApp.
## load the data
gene_panels <- get_panels(cache_dir = download_dir)
#> INFO [2025-05-22 14:00:23] Downloading remote dataset from Google Drive to cache_dir
#> INFO [2025-05-22 14:00:25] Reading from cache_dir = '/tmp/RtmpZglwrL', argument force_download = FALSE
#> INFO [2025-05-22 14:00:25] Object 'gene_panels' sucessfully loaded
#> INFO [2025-05-22 14:00:25] md5 checksum is valid: bb73853e9924a4a21a39eb186a9a77fe
#> INFO [2025-05-22 14:00:25] Retrieved 2614 records
## panel data for genome build grch38
panel_data <- gene_panels$records |>
dplyr::filter(genome_build == "grch38")
## show number of genes in each panel
gene_freq <- as.data.frame(panel_data |>
dplyr::group_by(gepa_panel_name) |>
dplyr::summarise(n = dplyr::n()) |>
dplyr::arrange(desc(n)) |>
dplyr::rename(panel_name = gepa_panel_name))
gene_freq
#> panel_name n
#> 1 DNA Repair Genes pertinent cancer susceptibility 178
#> 2 Childhood solid tumours 121
#> 3 Haematological malignancies cancer susceptibility 108
#> 4 Adult solid tumours cancer susceptibility 105
#> 5 Haematological malignancies for rare disease 89
#> 6 Childhood solid tumours cancer susceptibility 85
#> 7 Adult solid tumours for rare disease 58
#> 8 Multiple monogenic benign skin tumours 46
#> 9 Sarcoma susceptibility 44
#> 10 Sarcoma cancer susceptibility 33
#> 11 GI tract tumours 31
#> 12 Neurofibromatosis Type 1 30
#> 13 Inherited non-medullary thyroid cancer 29
#> 14 Familial breast cancer 27
#> 15 Inherited ovarian cancer (without breast cancer) 27
#> 16 Familial Tumours Syndromes of the central & peripheral Nervous system 21
#> 17 Inherited phaeochromocytoma and paraganglioma 20
#> 18 Inherited polyposis and early onset colorectal cancer - germline testing 19
#> 19 Familial rhabdomyosarcoma 18
#> 20 Inherited renal cancer 18
#> 21 Inherited predisposition to acute myeloid leukaemia (AML) 16
#> 22 Multiple endocrine tumours 16
#> 23 Familial prostate cancer 14
#> 24 Colorectal cancer pertinent cancer susceptibility 13
#> 25 Genodermatoses with malignancies 13
#> 26 Inherited pancreatic cancer 13
#> 27 Head and neck cancer pertinent cancer susceptibility 12
#> 28 Neuroendocrine cancer pertinent cancer susceptibility 12
#> 29 Renal cancer pertinent cancer susceptibility 10
#> 30 Ovarian cancer pertinent cancer susceptibility 9
#> 31 Familial melanoma 8
#> 32 Brain cancer pertinent cancer susceptibility 7
#> 33 Breast cancer pertinent cancer susceptibility 7
#> 34 Inherited predisposition to GIST 7
#> 35 Parathyroid Cancer 7
#> 36 Endometrial cancer pertinent cancer susceptibility 6
#> 37 Inherited MMR deficiency (Lynch syndrome) 5
#> 38 Prostate cancer pertinent cancer susceptibility 5
#> 39 Thyroid cancer pertinent cancer susceptibility 5
#> 40 Bladder cancer pertinent cancer susceptibility 4
#> 41 Upper gastrointestinal cancer pertinent cancer susceptibility 4
#> 42 Melanoma pertinent cancer susceptibility 3
#> 43 Familial rhabdoid tumours 2
#> 44 Inherited susceptibility to acute lymphoblastoid leukaemia (ALL) 2Get gene aliases
This shows how to retrieve ambiguous and unambiguous gene aliases (i.e. with respect to primary gene symbols).
## load the data
gene_alias <- get_alias(cache_dir = download_dir)
#> INFO [2025-05-22 14:00:25] Downloading remote dataset from Google Drive to cache_dir
#> INFO [2025-05-22 14:00:30] Reading from cache_dir = '/tmp/RtmpZglwrL', argument force_download = FALSE
#> INFO [2025-05-22 14:00:30] Object 'gene_alias' sucessfully loaded
#> INFO [2025-05-22 14:00:30] md5 checksum is valid: 175d18b3c6c746e3a178d683b69958c6
#> INFO [2025-05-22 14:00:30] Retrieved 138488 records
## number of gene synonyms that are ambiguous
nrow(dplyr::filter(gene_alias$records, ambiguous == TRUE))
#> [1] 7126
## show structure of alias records
head(gene_alias$records)
#> alias symbol entrezgene n_primary_map ambiguous source
#> 1 'C-K-RAS KRAS 3845 1 FALSE NCBI
#> 2 (FM-3) NMUR1 10316 1 FALSE NCBI
#> 3 (IV)-44 IGHVIV-44-1 28337 1 FALSE NCBI
#> 4 (P)RR ATP6AP2 10159 1 FALSE NCBI
#> 5 (ppGpp)ase HDDC3 374659 1 FALSE NCBI
#> 6 A-116A10.1 NAE1 8883 1 FALSE NCBI
#> is_primary_symbol other_index
#> 1 FALSE <NA>
#> 2 FALSE <NA>
#> 3 FALSE <NA>
#> 4 FALSE <NA>
#> 5 FALSE <NA>
#> 6 FALSE <NA>Get GENCODE transcripts
This shows how to retrieve GENCODE transcripts for
grch37 and grch38.
## load the data
gene_gencode <- get_gencode(cache_dir = download_dir)
#> INFO [2025-05-22 14:00:30] Downloading remote dataset from Google Drive to cache_dir
#> INFO [2025-05-22 14:00:42] Reading from cache_dir = '/tmp/RtmpZglwrL', argument force_download = FALSE
#> INFO [2025-05-22 14:00:42] Object 'gene_gencode_48_114' sucessfully loaded
#> INFO [2025-05-22 14:00:42] md5 checksum is valid: 61fea84d6a02be49dc7d8a1cc07801e3
#> INFO [2025-05-22 14:00:42] Retrieved records
## number of transcript records - grch37
nrow(gene_gencode$records$grch37)
#> [1] 196520
## number of transcript records - grch38
nrow(gene_gencode$records$grch38)
#> [1] 385669
## show colnames for transcript records
colnames(gene_gencode$records$grch38)
#> [1] "chrom" "start"
#> [3] "end" "transcript_start"
#> [5] "transcript_end" "cds_start"
#> [7] "strand" "ensembl_gene_id"
#> [9] "ensembl_gene_id_full" "ensembl_transcript_id"
#> [11] "ensembl_transcript_id_full" "ensembl_protein_id"
#> [13] "symbol" "symbol_gencode"
#> [15] "hgnc_id" "entrezgene"
#> [17] "name" "gene_biotype"
#> [19] "transcript_biotype" "tag"
#> [21] "refseq_protein_id" "refseq_transcript_id"
#> [23] "mane_select" "mane_plus_clinical"
#> [25] "refseq_select" "principal_isoform_flag"
#> [27] "uniprot_acc" "uniprot_id"
#> [29] "ensembl_version" "gencode_version"
#> [31] "uniprot_version"
## show metadata for underlying resources
gene_gencode$metadata
#> source
#> 1 GENCODE
#> 2 Ensembl Biomart
#> 3 UniprotKB
#> 4 APPRIS
#> source_description
#> 1 Human gene transcripts
#> 2 API for retrieval of gene and transcript cross-references (MANE, RefSeq)
#> 3 UniProt identifiers and accessions with cross-references to Ensembl
#> 4 Prinicipal transcript isoform annotation
#> source_url
#> 1 https://www.gencodegenes.org/
#> 2 https://www.ensembl.org/biomart/martview
#> 3 https://www.uniprot.org
#> 4 https://apprisws.bioinfo.cnio.es/landing_page/
#> source_citation source_version
#> 1 Frankish et al., Nucleic Acids Res, 2021; 33270111 48
#> 2 Cunningham et al., Nucleic Acids Res, 2022; 34791404 114
#> 3 UniProt Consortium, Nucleic Acids Res, 2021; 33237286 2025_02
#> 4 Rodriguez et al, Nucleic Acids Res, 2022; 34755885 2025-05-21
#> source_abbreviation source_license
#> 1 gencode Free/open access
#> 2 ensembl EMBL-EBI terms of use
#> 3 uniprot CC BY 4.0
#> 4 appris Free/open access
#> source_license_url
#> 1 <NA>
#> 2 https://www.ebi.ac.uk/about/terms-of-use
#> 3 https://creativecommons.org/licenses/by/4.0/
#> 4 <NA>Get DNA repair genes
## load the data
gene_dna_repair <- gene_basic$records |>
dplyr::filter(!is.na(woods_dnarepair_class)) |>
dplyr::select(symbol, woods_dnarepair_class,
woods_dnarepair_activity)
## count number of genes in each class
dna_repair_class_freq <- as.data.frame(
gene_dna_repair |>
dplyr::group_by(woods_dnarepair_class) |>
dplyr::summarise(n = dplyr::n()) |>
dplyr::arrange(desc(n)) |>
dplyr::rename(dna_repair_class = woods_dnarepair_class))
dna_repair_class_freq
#> dna_repair_class
#> 1 Nucleotide excision repair (NER)
#> 2 Homologous recombination
#> 3 Fanconi anemia
#> 4 DNA polymerases (catalytic subunits)
#> 5 Other conserved DNA damage response genes
#> 6 Base excision repair (BER)
#> 7 Ubiquitination and modification
#> 8 Mismatch excision repair (MMR)
#> 9 Other identified genes with known or suspected DNA repair function
#> 10 Editing and processing nucleases
#> 11 Non-homologous end-joining
#> 12 Other BER and strand break joining factors
#> 13 Genes defective in diseases associated with sensitivity to DNA damaging agents
#> 14 Chromatin Structure and Modification
#> 15 Direct reversal of damage
#> 16 Modulation of nucleotide pools
#> 17 Poly(ADP-ribose) polymerase (PARP) enzymes that bind to DNA
#> 18 Repair of DNA-topoisomerase crosslinks
#> 19 Unknown
#> n
#> 1 28
#> 2 21
#> 3 17
#> 4 15
#> 5 15
#> 6 11
#> 7 11
#> 8 10
#> 9 9
#> 10 8
#> 11 7
#> 12 6
#> 13 5
#> 14 3
#> 15 3
#> 16 3
#> 17 3
#> 18 2
#> 19 1Get TSO500 genes
## load the data
gene_tso500 <- gene_basic$records |>
dplyr::filter(!is.na(illumina_tso500))
## number of genes covered by TSO500 panel
nrow(gene_tso500)
#> [1] 523
## types of variant types covered
illumina_tso500_variant_freq <- as.data.frame(
gene_tso500 |>
dplyr::group_by(illumina_tso500) |>
dplyr::summarise(n = dplyr::n()) |>
dplyr::arrange(desc(n)))
illumina_tso500_variant_freq
#> illumina_tso500 n
#> 1 SNV_INDEL 433
#> 2 CNA_GAIN,SNV_INDEL 33
#> 3 RNA_FUSION,SNV_INDEL 31
#> 4 CNA_GAIN,RNA_FUSION,SNV_INDEL 22
#> 5 CNA_LOSS,RNA_FUSION,SNV_INDEL 2
#> 6 CNA_LOSS,SNV_INDEL 2Session Info
# set eval = FALSE if you don't want this info (useful for reproducibility)
# to appear
sessionInfo()
#> R version 4.5.0 (2025-04-11)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 24.04.2 LTS
#>
#> Matrix products: default
#> BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
#> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so; LAPACK version 3.12.0
#>
#> locale:
#> [1] LC_CTYPE=C.UTF-8 LC_NUMERIC=C LC_TIME=C.UTF-8
#> [4] LC_COLLATE=C.UTF-8 LC_MONETARY=C.UTF-8 LC_MESSAGES=C.UTF-8
#> [7] LC_PAPER=C.UTF-8 LC_NAME=C LC_ADDRESS=C
#> [10] LC_TELEPHONE=C LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C
#>
#> time zone: UTC
#> tzcode source: system (glibc)
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] geneOncoX_1.1.3
#>
#> loaded via a namespace (and not attached):
#> [1] jsonlite_2.0.0 dplyr_1.1.4 compiler_4.5.0 crayon_1.5.3
#> [5] Rcpp_1.0.14 tidyselect_1.2.1 stringr_1.5.1 jquerylib_0.1.4
#> [9] systemfonts_1.2.3 textshaping_1.0.1 yaml_2.3.10 fastmap_1.2.0
#> [13] plyr_1.8.9 R6_2.6.1 generics_0.1.4 curl_6.2.2
#> [17] knitr_1.50 htmlwidgets_1.6.4 tibble_3.2.1 desc_1.4.3
#> [21] bslib_0.9.0 pillar_1.10.2 rlang_1.1.6 DT_0.33
#> [25] stringi_1.8.7 cachem_1.1.0 lgr_0.4.4 xfun_0.52
#> [29] fs_1.6.6 sass_0.4.10 cli_3.6.5 pkgdown_2.1.2
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