cgdsr to cBioPortalData: Migration Tutorial
Karim Mezhoud & Marcel Ramos
April 22, 2025
Source:vignettes/cgdsrMigration.Rmd
cgdsrMigration.RmdIntroduction
This vignette aims to help developers migrate from the now defunct
cgdsr CRAN package. Note that the cgdsr
package code is shown for comparison but it is not guaranteed to work.
If you have questions regarding the contents, please create an issue at
the GitHub repository: https://github.com/waldronlab/cBioPortalData/issues
Discovering studies
cBioPortalData setup
Here we show the default inputs to the cBioPortal function for clarity.
cbio <- cBioPortal(
hostname = "www.cbioportal.org",
protocol = "https",
api. = "/api/v2/api-docs"
)
getStudies(cbio)FALSE
[38;5;246m# A tibble: 485 × 13
[39m
FALSE name description publicStudy pmid citation groups status importDate
FALSE
[3m
[38;5;246m<chr>
[39m
[23m
[3m
[38;5;246m<chr>
[39m
[23m
[3m
[38;5;246m<lgl>
[39m
[23m
[3m
[38;5;246m<chr>
[39m
[23m
[3m
[38;5;246m<chr>
[39m
[23m
[3m
[38;5;246m<chr>
[39m
[23m
[3m
[38;5;246m<int>
[39m
[23m
[3m
[38;5;246m<chr>
[39m
[23m
FALSE
[38;5;250m 1
[39m Acute Lympho… Comprehens… TRUE 2573… Anderss…
[38;5;246m"
[39mPUBL… 0 2024-12-0…
FALSE
[38;5;250m 2
[39m Hypodiploid … Whole geno… TRUE 2333… Holmfel…
[38;5;246m"
[39m
[38;5;246m"
[39m 0 2024-12-0…
FALSE
[38;5;250m 3
[39m Adenoid Cyst… Targeted S… TRUE 2441… Ross et…
[38;5;246m"
[39mACYC… 0 2024-12-0…
FALSE
[38;5;250m 4
[39m Adenoid Cyst… Whole-geno… TRUE 2686… Rettig …
[38;5;246m"
[39mACYC… 0 2024-12-0…
FALSE
[38;5;250m 5
[39m Adenoid Cyst… WGS of 21 … TRUE 2663… Mitani …
[38;5;246m"
[39mACYC… 0 2024-12-0…
FALSE
[38;5;250m 6
[39m Adenoid Cyst… Whole-geno… TRUE 2682… Drier e…
[38;5;246m"
[39mACYC
[38;5;246m"
[39m 0 2024-12-0…
FALSE
[38;5;250m 7
[39m Adenoid Cyst… Whole exom… TRUE 2377… Stephen…
[38;5;246m"
[39mACYC… 0 2024-12-0…
FALSE
[38;5;250m 8
[39m Acute Lympho… Whole-geno… TRUE 2777… Zhang e…
[38;5;246m"
[39mPUBL… 0 2024-12-0…
FALSE
[38;5;250m 9
[39m Appendiceal … Targeted s… TRUE 3649… Michael…
[38;5;246m"
[39mPUBL… 0 2024-12-0…
FALSE
[38;5;250m10
[39m Bladder Canc… Whole exom… TRUE 2690… Al-Ahma…
[38;5;246m"
[39m
[38;5;246m"
[39m 0 2024-12-0…
FALSE
[38;5;246m# ℹ 475 more rows
[39m
FALSE
[38;5;246m# ℹ 5 more variables: allSampleCount <int>, readPermission <lgl>,
[39m
FALSE
[38;5;246m# studyId <chr>, cancerTypeId <chr>, referenceGenome <chr>
[39mNote that the studyId column is important for further
queries.
head(getStudies(cbio)[["studyId"]])## [1] "all_stjude_2015" "all_stjude_2013" "acyc_fmi_2014" "acyc_jhu_2016"
## [5] "acyc_mda_2015" "acyc_mgh_2016"
cgdsr setup
library(cgdsr)
cgds <- CGDS("http://www.cbioportal.org/")
getCancerStudies.CGDS(cgds)Obtaining Cases
cBioPortalData (Cases)
Notes
- Each patient is identified by a
patientId. -
sampleListIdidentifies groups ofpatientIdbased on profile type - The
sampleListsfunction usesstudyIdinput to returnsampleListId
sampleLists
For the sample list identifiers, you can use sampleLists
and inspect the sampleListId column.
samps <- sampleLists(cbio, "gbm_tcga_pub")
samps[, c("category", "name", "sampleListId")]## # A tibble: 15 × 3
## category name sampleListId
## <chr> <chr> <chr>
## 1 all_cases_in_study All samples gbm_tcga_pu…
## 2 other Expression Cluster Classic… gbm_tcga_pu…
## 3 all_cases_with_cna_data Samples with CNA data gbm_tcga_pu…
## 4 all_cases_with_mutation_and_cna_data Samples with mutation and … gbm_tcga_pu…
## 5 all_cases_with_mrna_array_data Samples with mRNA data (Ag… gbm_tcga_pu…
## 6 other Expression Cluster Mesench… gbm_tcga_pu…
## 7 all_cases_with_methylation_data Samples with methylation d… gbm_tcga_pu…
## 8 all_cases_with_methylation_data Samples with methylation d… gbm_tcga_pu…
## 9 all_cases_with_microrna_data Samples with microRNA data… gbm_tcga_pu…
## 10 other Expression Cluster Neural gbm_tcga_pu…
## 11 other Expression Cluster Proneur… gbm_tcga_pu…
## 12 other Sequenced, No Hypermutators gbm_tcga_pu…
## 13 other Sequenced, Not Treated gbm_tcga_pu…
## 14 other Sequenced, Treated gbm_tcga_pu…
## 15 all_cases_with_mutation_data Samples with mutation data gbm_tcga_pu…samples from sampleLists
It is possible to get case_ids directly when using the
samplesInSampleLists function. The function handles
multiple sampleList identifiers.
samplesInSampleLists(
api = cbio,
sampleListIds = c(
"gbm_tcga_pub_expr_classical", "gbm_tcga_pub_expr_mesenchymal"
)
)## CharacterList of length 2
## [["gbm_tcga_pub_expr_classical"]] TCGA-02-0001-01 ... TCGA-12-0615-01
## [["gbm_tcga_pub_expr_mesenchymal"]] TCGA-02-0004-01 ... TCGA-12-0620-01getSampleInfo
To get more information about patients, we can query with
getSampleInfo function.
getSampleInfo(api = cbio, studyId = "gbm_tcga_pub", projection = "SUMMARY")## # A tibble: 206 × 6
## uniqueSampleKey uniquePatientKey sampleType sampleId patientId studyId
## <chr> <chr> <chr> <chr> <chr> <chr>
## 1 VENHQS0wMi0wMDAxLTAxO… VENHQS0wMi0wMDA… Primary S… TCGA-02… TCGA-02-… gbm_tc…
## 2 VENHQS0wMi0wMDAzLTAxO… VENHQS0wMi0wMDA… Primary S… TCGA-02… TCGA-02-… gbm_tc…
## 3 VENHQS0wMi0wMDA0LTAxO… VENHQS0wMi0wMDA… Primary S… TCGA-02… TCGA-02-… gbm_tc…
## 4 VENHQS0wMi0wMDA2LTAxO… VENHQS0wMi0wMDA… Primary S… TCGA-02… TCGA-02-… gbm_tc…
## 5 VENHQS0wMi0wMDA3LTAxO… VENHQS0wMi0wMDA… Primary S… TCGA-02… TCGA-02-… gbm_tc…
## 6 VENHQS0wMi0wMDA5LTAxO… VENHQS0wMi0wMDA… Primary S… TCGA-02… TCGA-02-… gbm_tc…
## 7 VENHQS0wMi0wMDEwLTAxO… VENHQS0wMi0wMDE… Primary S… TCGA-02… TCGA-02-… gbm_tc…
## 8 VENHQS0wMi0wMDExLTAxO… VENHQS0wMi0wMDE… Primary S… TCGA-02… TCGA-02-… gbm_tc…
## 9 VENHQS0wMi0wMDE0LTAxO… VENHQS0wMi0wMDE… Primary S… TCGA-02… TCGA-02-… gbm_tc…
## 10 VENHQS0wMi0wMDE1LTAxO… VENHQS0wMi0wMDE… Primary S… TCGA-02… TCGA-02-… gbm_tc…
## # ℹ 196 more rows
cgdsr (Cases)
Notes
- ‘Cases’ and ‘Patients’ are synonymous.
- Each patient is identified by a
case_id. - Queries only handle a single
cancerStudyidentifier - The
case_list_descriptiondescribes the assays
getCaseLists and getClinicalData
We obtain the first case_list_id in the
cgds object from above and the corresponding clinical data
for that case list (gbm_tcga_pub_all as the case list in
this example).
clist1 <-
getCaseLists.CGDS(cgds, cancerStudy = "gbm_tcga_pub")[1, "case_list_id"]
getClinicalData.CGDS(cgds, clist1)Obtaining Clinical Data
cBioPortalData (Clinical)
All clinical data
Note that a sampleListId is not required when using the
fetchAllClinicalDataInStudyUsingPOST internal endpoint.
Data for all patients can be obtained using the
clinicalData function.
clinicalData(cbio, "gbm_tcga_pub")## # A tibble: 206 × 24
## patientId DFS_MONTHS DFS_STATUS KARNOFSKY_PERFORMANC…¹ OS_MONTHS OS_STATUS
## <chr> <chr> <chr> <chr> <chr> <chr>
## 1 TCGA-02-0001 4.5041095… 1:Recurred 80.0 11.60547… 1:DECEAS…
## 2 TCGA-02-0003 1.3150684… 1:Recurred 100.0 4.734246… 1:DECEAS…
## 3 TCGA-02-0004 10.323287… 1:Recurred 80.0 11.34246… 1:DECEAS…
## 4 TCGA-02-0006 9.9287671… 1:Recurred 80.0 18.34520… 1:DECEAS…
## 5 TCGA-02-0007 17.030136… 1:Recurred 80.0 23.17808… 1:DECEAS…
## 6 TCGA-02-0009 8.6794520… 1:Recurred 80.0 10.58630… 1:DECEAS…
## 7 TCGA-02-0010 11.539726… 1:Recurred 80.0 35.40821… 1:DECEAS…
## 8 TCGA-02-0011 4.7342465… 1:Recurred 80.0 20.71232… 1:DECEAS…
## 9 TCGA-02-0014 NA NA 100.0 82.55342… 1:DECEAS…
## 10 TCGA-02-0015 14.991780… 1:Recurred 80.0 20.61369… 1:DECEAS…
## # ℹ 196 more rows
## # ℹ abbreviated name: ¹KARNOFSKY_PERFORMANCE_SCORE
## # ℹ 18 more variables: PRETREATMENT_HISTORY <chr>, PRIOR_GLIOMA <chr>,
## # SAMPLE_COUNT <chr>, SEX <chr>, sampleId <chr>, ACGH_DATA <chr>,
## # CANCER_TYPE <chr>, CANCER_TYPE_DETAILED <chr>, COMPLETE_DATA <chr>,
## # FRACTION_GENOME_ALTERED <chr>, MRNA_DATA <chr>, MUTATION_COUNT <chr>,
## # ONCOTREE_CODE <chr>, SAMPLE_TYPE <chr>, SEQUENCED <chr>, …By sample data
You can use a different endpoint to obtain data for a single sample.
First, obtain a single sampleId with the
samplesInSampleLists function.
clist1 <- "gbm_tcga_pub_all"
samplist <- samplesInSampleLists(cbio, clist1)
onesample <- samplist[["gbm_tcga_pub_all"]][1]
onesample## [1] "TCGA-02-0001-01"Then we use the API endpoint to retrieve the data. Note that you
would run httr::content on the output to extract the
data.
cbio$getAllClinicalDataOfSampleInStudyUsingGET(
sampleId = onesample, studyId = "gbm_tcga_pub"
)## Response [https://www.cbioportal.org/api/studies/gbm_tcga_pub/samples/TCGA-02-0001-01/clinical-data]
## Date: 2025-04-22 18:57
## Status: 200
## Content-Type: application/json
## Size: 3.31 kBClinical Data Summary
cgdsr allows you to obtain clinical data for a case list
subset (54 cases with gbm_tcga_pub_expr_classical) and
cBioPortalData provides clinical data for all 206 samples
in gbm_tcga_pub using the clinicalData
function.
-
cgdsrreturns adata.framewithsampleId(TCGA.02.0009.01) but notpatientId(TCGA.02.0009) -
cBioPortalDatareturnssampleId(TCGA-02-0009-01) andpatientId(TCGA-02-0009). - Note the differences in identifier delimiters between the two
packages,
cgdsrprovidescase_ids with.andcBioPortalDatareturnspatientIds with-.
You may be interested in other clinical data endpoints. For a list,
use the searchOps function.
searchOps(cbio, "clinical")## [1] "getAllClinicalAttributesUsingGET"
## [2] "fetchClinicalAttributesUsingPOST"
## [3] "fetchClinicalDataUsingPOST"
## [4] "getAllClinicalAttributesInStudyUsingGET"
## [5] "getClinicalAttributeInStudyUsingGET"
## [6] "getAllClinicalDataInStudyUsingGET"
## [7] "fetchAllClinicalDataInStudyUsingPOST"
## [8] "getAllClinicalDataOfPatientInStudyUsingGET"
## [9] "getAllClinicalDataOfSampleInStudyUsingGET"Molecular or Genetic Profiles
cBioPortalData (molecularProfiles)
molecularProfiles(api = cbio, studyId = "gbm_tcga_pub")## # A tibble: 10 × 8
## molecularAlterationType datatype name description showProfileInAnalysi…¹
## <chr> <chr> <chr> <chr> <lgl>
## 1 COPY_NUMBER_ALTERATION DISCRETE Putati… Putative c… TRUE
## 2 COPY_NUMBER_ALTERATION DISCRETE Putati… Putative c… TRUE
## 3 MUTATION_EXTENDED MAF Mutati… Mutation d… TRUE
## 4 METHYLATION CONTINUOUS Methyl… Methylatio… FALSE
## 5 MRNA_EXPRESSION CONTINUOUS mRNA e… mRNA expre… FALSE
## 6 MRNA_EXPRESSION Z-SCORE mRNA e… 18,698 gen… TRUE
## 7 MRNA_EXPRESSION Z-SCORE mRNA e… Log-transf… TRUE
## 8 MRNA_EXPRESSION CONTINUOUS microR… expression… FALSE
## 9 MRNA_EXPRESSION Z-SCORE microR… microRNA e… FALSE
## 10 MRNA_EXPRESSION Z-SCORE mRNA/m… mRNA and m… TRUE
## # ℹ abbreviated name: ¹showProfileInAnalysisTab
## # ℹ 3 more variables: patientLevel <lgl>, molecularProfileId <chr>,
## # studyId <chr>Note that we want to pull the molecularProfileId column
to use in other queries.
Genomic Profile Data for a set of genes
cBioPortalData (Indentify samples and genes)
Currently, some conversion is needed to directly use the
molecularData function, if you only have Hugo symbols.
First, convert to Entrez gene IDs and then obtain all the samples in the
sample list of interest.
Convert hugoGeneSymbol to
entrezGeneId
genetab <- queryGeneTable(cbio,
by = "hugoGeneSymbol",
genes = c("NF1", "TP53", "ABL1")
)
genetab## # A tibble: 3 × 3
## entrezGeneId hugoGeneSymbol type
## <int> <chr> <chr>
## 1 4763 NF1 protein-coding
## 2 25 ABL1 protein-coding
## 3 7157 TP53 protein-coding
entrez <- genetab[["entrezGeneId"]]Obtain all samples in study
allsamps <- samplesInSampleLists(cbio, "gbm_tcga_pub_all")In the next section, we will show how to use the genes and sample identifiers to obtain the molecular profile data.
cgdsr (Profile Data)
The getProfileData function allows for straightforward
retrieval of the molecular profile data with only a case list and
genetic profile identifiers.
getProfileData.CGDS(x = cgds,
genes = c("NF1", "TP53", "ABL1"),
geneticProfiles = "gbm_tcga_pub_mrna",
caseList = "gbm_tcga_pub_all"
)Molecular Data with cBioPortalData
cBioPortalData provides a number of options for
retrieving molecular profile data depending on the use case. Note that
molecularData is mostly used internally and that the
cBioPortalData function is the user-friendly method for
downloading such data.
molecularData
We use the translated entrez identifiers from above.
molecularData(cbio, "gbm_tcga_pub_mrna",
entrezGeneIds = entrez, sampleIds = unlist(allsamps))## $gbm_tcga_pub_mrna
## # A tibble: 618 × 8
## uniqueSampleKey uniquePatientKey entrezGeneId molecularProfileId sampleId
## <chr> <chr> <int> <chr> <chr>
## 1 VENHQS0wMi0wMDAxLT… VENHQS0wMi0wMDA… 25 gbm_tcga_pub_mrna TCGA-02…
## 2 VENHQS0wMi0wMDAxLT… VENHQS0wMi0wMDA… 4763 gbm_tcga_pub_mrna TCGA-02…
## 3 VENHQS0wMi0wMDAxLT… VENHQS0wMi0wMDA… 7157 gbm_tcga_pub_mrna TCGA-02…
## 4 VENHQS0wMi0wMDAzLT… VENHQS0wMi0wMDA… 25 gbm_tcga_pub_mrna TCGA-02…
## 5 VENHQS0wMi0wMDAzLT… VENHQS0wMi0wMDA… 4763 gbm_tcga_pub_mrna TCGA-02…
## 6 VENHQS0wMi0wMDAzLT… VENHQS0wMi0wMDA… 7157 gbm_tcga_pub_mrna TCGA-02…
## 7 VENHQS0wMi0wMDA0LT… VENHQS0wMi0wMDA… 25 gbm_tcga_pub_mrna TCGA-02…
## 8 VENHQS0wMi0wMDA0LT… VENHQS0wMi0wMDA… 4763 gbm_tcga_pub_mrna TCGA-02…
## 9 VENHQS0wMi0wMDA0LT… VENHQS0wMi0wMDA… 7157 gbm_tcga_pub_mrna TCGA-02…
## 10 VENHQS0wMi0wMDA2LT… VENHQS0wMi0wMDA… 25 gbm_tcga_pub_mrna TCGA-02…
## # ℹ 608 more rows
## # ℹ 3 more variables: patientId <chr>, studyId <chr>, value <dbl>
getDataByGenes
The getDataByGenes function automatically figures out
all the sample identifiers in the study and it allows Hugo and Entrez
identifiers, as well as genePanelId inputs.
getDataByGenes(
api = cbio,
studyId = "gbm_tcga_pub",
genes = c("NF1", "TP53", "ABL1"),
by = "hugoGeneSymbol",
molecularProfileIds = "gbm_tcga_pub_mrna"
)## $gbm_tcga_pub_mrna
## # A tibble: 618 × 10
## uniqueSampleKey uniquePatientKey entrezGeneId molecularProfileId sampleId
## <chr> <chr> <int> <chr> <chr>
## 1 VENHQS0wMi0wMDAxLT… VENHQS0wMi0wMDA… 25 gbm_tcga_pub_mrna TCGA-02…
## 2 VENHQS0wMi0wMDAxLT… VENHQS0wMi0wMDA… 4763 gbm_tcga_pub_mrna TCGA-02…
## 3 VENHQS0wMi0wMDAxLT… VENHQS0wMi0wMDA… 7157 gbm_tcga_pub_mrna TCGA-02…
## 4 VENHQS0wMi0wMDAzLT… VENHQS0wMi0wMDA… 25 gbm_tcga_pub_mrna TCGA-02…
## 5 VENHQS0wMi0wMDAzLT… VENHQS0wMi0wMDA… 4763 gbm_tcga_pub_mrna TCGA-02…
## 6 VENHQS0wMi0wMDAzLT… VENHQS0wMi0wMDA… 7157 gbm_tcga_pub_mrna TCGA-02…
## 7 VENHQS0wMi0wMDA0LT… VENHQS0wMi0wMDA… 25 gbm_tcga_pub_mrna TCGA-02…
## 8 VENHQS0wMi0wMDA0LT… VENHQS0wMi0wMDA… 4763 gbm_tcga_pub_mrna TCGA-02…
## 9 VENHQS0wMi0wMDA0LT… VENHQS0wMi0wMDA… 7157 gbm_tcga_pub_mrna TCGA-02…
## 10 VENHQS0wMi0wMDA2LT… VENHQS0wMi0wMDA… 25 gbm_tcga_pub_mrna TCGA-02…
## # ℹ 608 more rows
## # ℹ 5 more variables: patientId <chr>, studyId <chr>, value <dbl>,
## # hugoGeneSymbol <chr>, type <chr>
cBioPortalData: the main end-user function
It is important to note that end users who wish to obtain the data as
easily as possible should use the main cBioPortalData
function:
gbm_pub <- cBioPortalData(
api = cbio,
studyId = "gbm_tcga_pub",
genes = c("NF1", "TP53", "ABL1"), by = "hugoGeneSymbol",
molecularProfileIds = "gbm_tcga_pub_mrna"
)
assay(gbm_pub[["gbm_tcga_pub_mrna"]])[, 1:4]## TCGA-02-0001-01 TCGA-02-0003-01 TCGA-02-0004-01 TCGA-02-0006-01
## ABL1 -0.1744878 -0.177096729 -0.08782114 -0.1733767
## NF1 -0.2966920 -0.001066810 -0.23626512 -0.1691507
## TP53 0.6213171 0.006435625 -0.30507285 0.3967758Mutation Data
cBioPortalData (mutationData)
Similar to molecularData, mutation data can be obtained
with the mutationData function or the
getDataByGenes function.
mutationData(
api = cbio,
molecularProfileIds = "gbm_tcga_pub_mutations",
entrezGeneIds = entrez,
sampleIds = unlist(allsamps)
)## $gbm_tcga_pub_mutations
## # A tibble: 57 × 23
## uniqueSampleKey uniquePatientKey molecularProfileId sampleId patientId
## <chr> <chr> <chr> <chr> <chr>
## 1 VENHQS0wMi0wMDAxLTAxO… VENHQS0wMi0wMDA… gbm_tcga_pub_muta… TCGA-02… TCGA-02-…
## 2 VENHQS0wMi0wMDAxLTAxO… VENHQS0wMi0wMDA… gbm_tcga_pub_muta… TCGA-02… TCGA-02-…
## 3 VENHQS0wMi0wMDAzLTAxO… VENHQS0wMi0wMDA… gbm_tcga_pub_muta… TCGA-02… TCGA-02-…
## 4 VENHQS0wMi0wMDAzLTAxO… VENHQS0wMi0wMDA… gbm_tcga_pub_muta… TCGA-02… TCGA-02-…
## 5 VENHQS0wMi0wMDEwLTAxO… VENHQS0wMi0wMDE… gbm_tcga_pub_muta… TCGA-02… TCGA-02-…
## 6 VENHQS0wMi0wMDEwLTAxO… VENHQS0wMi0wMDE… gbm_tcga_pub_muta… TCGA-02… TCGA-02-…
## 7 VENHQS0wMi0wMDEwLTAxO… VENHQS0wMi0wMDE… gbm_tcga_pub_muta… TCGA-02… TCGA-02-…
## 8 VENHQS0wMi0wMDExLTAxO… VENHQS0wMi0wMDE… gbm_tcga_pub_muta… TCGA-02… TCGA-02-…
## 9 VENHQS0wMi0wMDE0LTAxO… VENHQS0wMi0wMDE… gbm_tcga_pub_muta… TCGA-02… TCGA-02-…
## 10 VENHQS0wMi0wMDI0LTAxO… VENHQS0wMi0wMDI… gbm_tcga_pub_muta… TCGA-02… TCGA-02-…
## # ℹ 47 more rows
## # ℹ 18 more variables: entrezGeneId <int>, studyId <chr>, center <chr>,
## # mutationStatus <chr>, validationStatus <chr>, startPosition <int>,
## # endPosition <int>, referenceAllele <chr>, proteinChange <chr>,
## # mutationType <chr>, ncbiBuild <chr>, variantType <chr>, keyword <chr>,
## # chr <chr>, variantAllele <chr>, refseqMrnaId <chr>, proteinPosStart <int>,
## # proteinPosEnd <int>
getDataByGenes(
api = cbio,
studyId = "gbm_tcga_pub",
genes = c("NF1", "TP53", "ABL1"),
by = "hugoGeneSymbol",
molecularProfileIds = "gbm_tcga_pub_mutations"
)## $gbm_tcga_pub_mutations
## # A tibble: 57 × 25
## uniqueSampleKey uniquePatientKey molecularProfileId sampleId patientId
## <chr> <chr> <chr> <chr> <chr>
## 1 VENHQS0wMi0wMDAxLTAxO… VENHQS0wMi0wMDA… gbm_tcga_pub_muta… TCGA-02… TCGA-02-…
## 2 VENHQS0wMi0wMDAxLTAxO… VENHQS0wMi0wMDA… gbm_tcga_pub_muta… TCGA-02… TCGA-02-…
## 3 VENHQS0wMi0wMDAzLTAxO… VENHQS0wMi0wMDA… gbm_tcga_pub_muta… TCGA-02… TCGA-02-…
## 4 VENHQS0wMi0wMDAzLTAxO… VENHQS0wMi0wMDA… gbm_tcga_pub_muta… TCGA-02… TCGA-02-…
## 5 VENHQS0wMi0wMDEwLTAxO… VENHQS0wMi0wMDE… gbm_tcga_pub_muta… TCGA-02… TCGA-02-…
## 6 VENHQS0wMi0wMDEwLTAxO… VENHQS0wMi0wMDE… gbm_tcga_pub_muta… TCGA-02… TCGA-02-…
## 7 VENHQS0wMi0wMDEwLTAxO… VENHQS0wMi0wMDE… gbm_tcga_pub_muta… TCGA-02… TCGA-02-…
## 8 VENHQS0wMi0wMDExLTAxO… VENHQS0wMi0wMDE… gbm_tcga_pub_muta… TCGA-02… TCGA-02-…
## 9 VENHQS0wMi0wMDE0LTAxO… VENHQS0wMi0wMDE… gbm_tcga_pub_muta… TCGA-02… TCGA-02-…
## 10 VENHQS0wMi0wMDI0LTAxO… VENHQS0wMi0wMDI… gbm_tcga_pub_muta… TCGA-02… TCGA-02-…
## # ℹ 47 more rows
## # ℹ 20 more variables: entrezGeneId <int>, studyId <chr>, center <chr>,
## # mutationStatus <chr>, validationStatus <chr>, startPosition <int>,
## # endPosition <int>, referenceAllele <chr>, proteinChange <chr>,
## # mutationType <chr>, ncbiBuild <chr>, variantType <chr>, keyword <chr>,
## # chr <chr>, variantAllele <chr>, refseqMrnaId <chr>, proteinPosStart <int>,
## # proteinPosEnd <int>, hugoGeneSymbol <chr>, type <chr>
cgdsr (getMutationData)
getMutationData.CGDS(
x = cgds,
caseList = "getMutationData",
geneticProfile = "gbm_tcga_pub_mutations",
genes = c("NF1", "TP53", "ABL1")
)Copy Number Alteration (CNA)
cBioPortalData (CNA)
Copy Number Alteration data can be obtained with the
getDataByGenes function or by the main
cBioPortal function.
getDataByGenes(
api = cbio,
studyId = "gbm_tcga_pub",
genes = c("NF1", "TP53", "ABL1"),
by = "hugoGeneSymbol",
molecularProfileIds = "gbm_tcga_pub_cna_rae"
)## $gbm_tcga_pub_cna_rae
## # A tibble: 6 × 10
## uniqueSampleKey uniquePatientKey molecularProfileId sampleId patientId
## <chr> <chr> <chr> <chr> <chr>
## 1 VENHQS0wMi0wMDU1LTAxOm… VENHQS0wMi0wMDU… gbm_tcga_pub_cna_… TCGA-02… TCGA-02-…
## 2 VENHQS0wMi0wMzI0LTAxOm… VENHQS0wMi0wMzI… gbm_tcga_pub_cna_… TCGA-02… TCGA-02-…
## 3 VENHQS0wNi0wMTY2LTAxOm… VENHQS0wNi0wMTY… gbm_tcga_pub_cna_… TCGA-06… TCGA-06-…
## 4 VENHQS0wNi0wMjA2LTAxOm… VENHQS0wNi0wMjA… gbm_tcga_pub_cna_… TCGA-06… TCGA-06-…
## 5 VENHQS0wNi0wMjEzLTAxOm… VENHQS0wNi0wMjE… gbm_tcga_pub_cna_… TCGA-06… TCGA-06-…
## 6 VENHQS0wNi0wNjQ2LTAxOm… VENHQS0wNi0wNjQ… gbm_tcga_pub_cna_… TCGA-06… TCGA-06-…
## # ℹ 5 more variables: entrezGeneId <int>, studyId <chr>, alteration <int>,
## # hugoGeneSymbol <chr>, type <chr>
cBioPortalData(
api = cbio,
studyId = "gbm_tcga_pub",
genes = c("NF1", "TP53", "ABL1"),
by = "hugoGeneSymbol",
molecularProfileIds = "gbm_tcga_pub_cna_rae"
)## harmonizing input:
## removing 200 colData rownames not in sampleMap 'primary'## A MultiAssayExperiment object of 1 listed
## experiment with a user-defined name and respective class.
## Containing an ExperimentList class object of length 1:
## [1] gbm_tcga_pub_cna_rae: SummarizedExperiment with 2 rows and 6 columns
## Functionality:
## experiments() - obtain the ExperimentList instance
## colData() - the primary/phenotype DataFrame
## sampleMap() - the sample coordination DataFrame
## `$`, `[`, `[[` - extract colData columns, subset, or experiment
## *Format() - convert into a long or wide DataFrame
## assays() - convert ExperimentList to a SimpleList of matrices
## exportClass() - save data to flat files
cgdsr (CNA)
getProfileData.CGDS(
x = cgds,
genes = c("NF1", "TP53", "ABL1"),
geneticProfiles = "gbm_tcga_pub_cna_rae",
caseList = "gbm_tcga_pub_cna"
)Methylation Data
cBioPortalData (Methylation)
Similar to Copy Number Alteration, Methylation can be obtained by
getDataByGenes function or by ‘cBioPortalData’
function.
getDataByGenes(
api = cbio,
studyId = "gbm_tcga_pub",
genes = c("NF1", "TP53", "ABL1"),
by = "hugoGeneSymbol",
molecularProfileIds = "gbm_tcga_pub_methylation_hm27"
)## $gbm_tcga_pub_methylation_hm27
## # A tibble: 174 × 10
## uniqueSampleKey uniquePatientKey entrezGeneId molecularProfileId sampleId
## <chr> <chr> <int> <chr> <chr>
## 1 VENHQS0wMi0wMDAxLT… VENHQS0wMi0wMDA… 25 gbm_tcga_pub_meth… TCGA-02…
## 2 VENHQS0wMi0wMDAxLT… VENHQS0wMi0wMDA… 4763 gbm_tcga_pub_meth… TCGA-02…
## 3 VENHQS0wMi0wMDAxLT… VENHQS0wMi0wMDA… 7157 gbm_tcga_pub_meth… TCGA-02…
## 4 VENHQS0wMi0wMDAzLT… VENHQS0wMi0wMDA… 25 gbm_tcga_pub_meth… TCGA-02…
## 5 VENHQS0wMi0wMDAzLT… VENHQS0wMi0wMDA… 4763 gbm_tcga_pub_meth… TCGA-02…
## 6 VENHQS0wMi0wMDAzLT… VENHQS0wMi0wMDA… 7157 gbm_tcga_pub_meth… TCGA-02…
## 7 VENHQS0wMi0wMDA2LT… VENHQS0wMi0wMDA… 25 gbm_tcga_pub_meth… TCGA-02…
## 8 VENHQS0wMi0wMDA2LT… VENHQS0wMi0wMDA… 4763 gbm_tcga_pub_meth… TCGA-02…
## 9 VENHQS0wMi0wMDA2LT… VENHQS0wMi0wMDA… 7157 gbm_tcga_pub_meth… TCGA-02…
## 10 VENHQS0wMi0wMDA3LT… VENHQS0wMi0wMDA… 25 gbm_tcga_pub_meth… TCGA-02…
## # ℹ 164 more rows
## # ℹ 5 more variables: patientId <chr>, studyId <chr>, value <dbl>,
## # hugoGeneSymbol <chr>, type <chr>
cBioPortalData(
api = cbio,
studyId = "gbm_tcga_pub",
genes = c("NF1", "TP53", "ABL1"),
by = "hugoGeneSymbol",
molecularProfileIds = "gbm_tcga_pub_methylation_hm27"
)## harmonizing input:
## removing 148 colData rownames not in sampleMap 'primary'## A MultiAssayExperiment object of 1 listed
## experiment with a user-defined name and respective class.
## Containing an ExperimentList class object of length 1:
## [1] gbm_tcga_pub_methylation_hm27: SummarizedExperiment with 3 rows and 58 columns
## Functionality:
## experiments() - obtain the ExperimentList instance
## colData() - the primary/phenotype DataFrame
## sampleMap() - the sample coordination DataFrame
## `$`, `[`, `[[` - extract colData columns, subset, or experiment
## *Format() - convert into a long or wide DataFrame
## assays() - convert ExperimentList to a SimpleList of matrices
## exportClass() - save data to flat files
cgdsr (Methylation)
getProfileData.CGDS(
x = cgds,
genes = c("NF1", "TP53", "ABL1"),
geneticProfiles = "gbm_tcga_pub_methylation_hm27",
caseList = "gbm_tcga_pub_methylation_hm27"
)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=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## time zone: Etc/UTC
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] cBioPortalData_2.20.0 MultiAssayExperiment_1.34.0
## [3] SummarizedExperiment_1.38.0 Biobase_2.68.0
## [5] GenomicRanges_1.60.0 GenomeInfoDb_1.44.0
## [7] IRanges_2.42.0 S4Vectors_0.46.0
## [9] BiocGenerics_0.54.0 generics_0.1.3
## [11] MatrixGenerics_1.20.0 matrixStats_1.5.0
## [13] AnVIL_1.20.0 AnVILBase_1.2.0
## [15] dplyr_1.1.4 BiocStyle_2.36.0
##
## loaded via a namespace (and not attached):
## [1] DBI_1.2.3 bitops_1.0-9
## [3] httr2_1.1.2 formatR_1.14
## [5] rlang_1.1.6 magrittr_2.0.3
## [7] compiler_4.5.0 RSQLite_2.3.9
## [9] GenomicFeatures_1.60.0 png_0.1-8
## [11] systemfonts_1.2.2 vctrs_0.6.5
## [13] rvest_1.0.4 stringr_1.5.1
## [15] pkgconfig_2.0.3 crayon_1.5.3
## [17] fastmap_1.2.0 dbplyr_2.5.0
## [19] XVector_0.48.0 utf8_1.2.4
## [21] Rsamtools_2.24.0 promises_1.3.2
## [23] rmarkdown_2.29 tzdb_0.5.0
## [25] UCSC.utils_1.4.0 ragg_1.4.0
## [27] purrr_1.0.4 bit_4.6.0
## [29] xfun_0.52 cachem_1.1.0
## [31] jsonlite_2.0.0 blob_1.2.4
## [33] later_1.4.2 DelayedArray_0.34.1
## [35] BiocParallel_1.42.0 parallel_4.5.0
## [37] R6_2.6.1 bslib_0.9.0
## [39] stringi_1.8.7 rtracklayer_1.68.0
## [41] jquerylib_0.1.4 Rcpp_1.0.14
## [43] bookdown_0.43 knitr_1.50
## [45] readr_2.1.5 BiocBaseUtils_1.10.0
## [47] httpuv_1.6.16 Matrix_1.7-3
## [49] tidyselect_1.2.1 abind_1.4-8
## [51] yaml_2.3.10 codetools_0.2-20
## [53] miniUI_0.1.2 curl_6.2.2
## [55] lattice_0.22-7 tibble_3.2.1
## [57] withr_3.0.2 KEGGREST_1.48.0
## [59] shiny_1.10.0 evaluate_1.0.3
## [61] desc_1.4.3 lambda.r_1.2.4
## [63] futile.logger_1.4.3 BiocFileCache_2.16.0
## [65] xml2_1.3.8 Biostrings_2.76.0
## [67] pillar_1.10.2 BiocManager_1.30.25
## [69] filelock_1.0.3 DT_0.33
## [71] TCGAutils_1.28.0 RCurl_1.98-1.17
## [73] hms_1.1.3 xtable_1.8-4
## [75] RTCGAToolbox_2.38.0 glue_1.8.0
## [77] tools_4.5.0 BiocIO_1.18.0
## [79] data.table_1.17.0 GenomicAlignments_1.44.0
## [81] rapiclient_0.1.8 XML_3.99-0.18
## [83] fs_1.6.6 grid_4.5.0
## [85] tidyr_1.3.1 AnnotationDbi_1.70.0
## [87] GenomeInfoDbData_1.2.14 RaggedExperiment_1.32.0
## [89] RJSONIO_2.0.0 restfulr_0.0.15
## [91] cli_3.6.4 rappdirs_0.3.3
## [93] textshaping_1.0.0 futile.options_1.0.1
## [95] GenomicDataCommons_1.32.0 S4Arrays_1.8.0
## [97] sass_0.4.10 digest_0.6.37
## [99] SparseArray_1.8.0 rjson_0.2.23
## [101] htmlwidgets_1.6.4 memoise_2.0.1
## [103] htmltools_0.5.8.1 pkgdown_2.1.1
## [105] lifecycle_1.0.4 httr_1.4.7
## [107] mime_0.13 bit64_4.6.0-1