Please use the devel version of the AnVIL
Bioconductor package.
library(cBioPortalData)
library(AnVIL)
This vignette is for users / developers who would like to learn more about
the available in cBioPortalData
and possibly hit other endpoints
in the cBioPortal API implementation. The functionality demonstrated
here is used internally by the package to create integrative representations
of study datasets.
Obtaining the cBioPortal API representation object
(cbio <- cBioPortal())
## service: cBioPortal
## tags(); use cbioportal$<tab completion>:
## # A tibble: 61 x 3
## tag operation summary
## <chr> <chr> <chr>
## 1 Cancer Types getAllCancerTypesUsingGET Get all cancer types
## 2 Cancer Types getCancerTypeUsingGET Get a cancer type
## 3 Clinical Att… fetchClinicalAttributesUs… Fetch clinical attributes
## 4 Clinical Att… getAllClinicalAttributesI… Get all clinical attributes in the …
## 5 Clinical Att… getAllClinicalAttributesU… Get all clinical attributes
## 6 Clinical Att… getClinicalAttributeInStu… Get specified clinical attribute
## 7 Clinical Data fetchAllClinicalDataInStu… Fetch clinical data by patient IDs …
## 8 Clinical Data fetchClinicalDataUsingPOST Fetch clinical data by patient IDs …
## 9 Clinical Data getAllClinicalDataInStudy… Get all clinical data in a study
## 10 Clinical Data getAllClinicalDataOfPatie… Get all clinical data of a patient …
## # … with 51 more rows
## tag values:
## Cancer Types, Clinical Attributes, Clinical Data, Copy Number
## Segments, Discrete Copy Number Alterations, Gene Panels, Generic
## Assays, Genes, Molecular Data, Molecular Profiles, Mutations,
## Patients, Sample Lists, Samples, Structural Variants, Studies,
## Treatments
## schemas():
## AlleleSpecificCopyNumber, AndedPatientTreatmentFilters,
## AndedSampleTreatmentFilters, CancerStudy, CancerStudyTags
## # ... with 55 more elements
Check available tags, operations, and descriptions as a tibble
:
tags(cbio)
## # A tibble: 61 x 3
## tag operation summary
## <chr> <chr> <chr>
## 1 Cancer Types getAllCancerTypesUsingGET Get all cancer types
## 2 Cancer Types getCancerTypeUsingGET Get a cancer type
## 3 Clinical Att… fetchClinicalAttributesUs… Fetch clinical attributes
## 4 Clinical Att… getAllClinicalAttributesI… Get all clinical attributes in the …
## 5 Clinical Att… getAllClinicalAttributesU… Get all clinical attributes
## 6 Clinical Att… getClinicalAttributeInStu… Get specified clinical attribute
## 7 Clinical Data fetchAllClinicalDataInStu… Fetch clinical data by patient IDs …
## 8 Clinical Data fetchClinicalDataUsingPOST Fetch clinical data by patient IDs …
## 9 Clinical Data getAllClinicalDataInStudy… Get all clinical data in a study
## 10 Clinical Data getAllClinicalDataOfPatie… Get all clinical data of a patient …
## # … with 51 more rows
head(tags(cbio)$operation)
## [1] "getAllCancerTypesUsingGET"
## [2] "getCancerTypeUsingGET"
## [3] "fetchClinicalAttributesUsingPOST"
## [4] "getAllClinicalAttributesInStudyUsingGET"
## [5] "getAllClinicalAttributesUsingGET"
## [6] "getClinicalAttributeInStudyUsingGET"
searchOps(cbio, "clinical")
## [1] "getAllClinicalAttributesUsingGET"
## [2] "fetchClinicalAttributesUsingPOST"
## [3] "fetchClinicalDataUsingPOST"
## [4] "getAllClinicalAttributesInStudyUsingGET"
## [5] "getClinicalAttributeInStudyUsingGET"
## [6] "getAllClinicalDataInStudyUsingGET"
## [7] "fetchAllClinicalDataInStudyUsingPOST"
## [8] "getAllClinicalDataOfPatientInStudyUsingGET"
## [9] "getAllClinicalDataOfSampleInStudyUsingGET"
Get the list of studies available:
getStudies(cbio)
## # A tibble: 309 x 13
## name shortName description publicStudy pmid citation groups status
## <chr> <chr> <chr> <lgl> <chr> <chr> <chr> <int>
## 1 Oral Sq… Head & ne… Comprehensive … TRUE 2361… Pickerin… "" 0
## 2 Hepatoc… HCC (Inse… Whole-exome se… TRUE 2582… Schulze … "PUBL… 0
## 3 Uveal M… UM (QIMR) Whole-genome o… TRUE 2668… Johansso… "PUBL… 0
## 4 Neurobl… NBL (AMC) Whole genome s… TRUE 2236… Molenaar… "PUBL… 0
## 5 Nasopha… NPC (Sing… Whole exome se… TRUE 2495… Lin et a… "PUBL… 0
## 6 Neurobl… NBL (Colo… Whole-genome s… TRUE 2646… Peifer e… "" 0
## 7 Myelody… MDS (Toky… Whole exome se… TRUE 2190… Yoshida … "" 0
## 8 Insulin… Panet (Sh… Whole exome se… TRUE 2432… Cao et a… "" 0
## 9 Pleural… PLMESO (N… Whole-exome se… TRUE 2548… Guo et a… "" 0
## 10 Pilocyt… PAST (Nat… Whole-genome s… TRUE 2381… Jones et… "PUBL… 0
## # … with 299 more rows, and 5 more variables: importDate <chr>,
## # allSampleCount <int>, studyId <chr>, cancerTypeId <chr>,
## # referenceGenome <chr>
Obtain the clinical data for a particular study:
clinicalData(cbio, "acc_tcga")
## # A tibble: 92 x 21
## uniqueSampleKey uniquePatientKey sampleId patientId studyId CANCER_TYPE
## <chr> <chr> <chr> <chr> <chr> <chr>
## 1 VENHQS1PUi1BNUox… VENHQS1PUi1BNUoxO… TCGA-OR-… TCGA-OR-… acc_tc… Adrenocorti…
## 2 VENHQS1PUi1BNUoy… VENHQS1PUi1BNUoyO… TCGA-OR-… TCGA-OR-… acc_tc… Adrenocorti…
## 3 VENHQS1PUi1BNUoz… VENHQS1PUi1BNUozO… TCGA-OR-… TCGA-OR-… acc_tc… Adrenocorti…
## 4 VENHQS1PUi1BNUo0… VENHQS1PUi1BNUo0O… TCGA-OR-… TCGA-OR-… acc_tc… Adrenocorti…
## 5 VENHQS1PUi1BNUo1… VENHQS1PUi1BNUo1O… TCGA-OR-… TCGA-OR-… acc_tc… Adrenocorti…
## 6 VENHQS1PUi1BNUo2… VENHQS1PUi1BNUo2O… TCGA-OR-… TCGA-OR-… acc_tc… Adrenocorti…
## 7 VENHQS1PUi1BNUo3… VENHQS1PUi1BNUo3O… TCGA-OR-… TCGA-OR-… acc_tc… Adrenocorti…
## 8 VENHQS1PUi1BNUo4… VENHQS1PUi1BNUo4O… TCGA-OR-… TCGA-OR-… acc_tc… Adrenocorti…
## 9 VENHQS1PUi1BNUo5… VENHQS1PUi1BNUo5O… TCGA-OR-… TCGA-OR-… acc_tc… Adrenocorti…
## 10 VENHQS1PUi1BNUpB… VENHQS1PUi1BNUpBO… TCGA-OR-… TCGA-OR-… acc_tc… Adrenocorti…
## # … with 82 more rows, and 15 more variables: CANCER_TYPE_DETAILED <chr>,
## # DAYS_TO_COLLECTION <chr>, FRACTION_GENOME_ALTERED <chr>, IS_FFPE <chr>,
## # MUTATION_COUNT <chr>, OCT_EMBEDDED <chr>, ONCOTREE_CODE <chr>,
## # OTHER_SAMPLE_ID <chr>, PATHOLOGY_REPORT_FILE_NAME <chr>,
## # PATHOLOGY_REPORT_UUID <chr>, SAMPLE_INITIAL_WEIGHT <chr>,
## # SAMPLE_TYPE <chr>, SAMPLE_TYPE_ID <chr>, SOMATIC_STATUS <chr>,
## # VIAL_NUMBER <chr>
A table of molecular profiles for a particular study can be obtained by running the following:
mols <- molecularProfiles(cbio, "acc_tcga")
mols[["molecularProfileId"]]
## [1] "acc_tcga_rppa"
## [2] "acc_tcga_rppa_Zscores"
## [3] "acc_tcga_gistic"
## [4] "acc_tcga_rna_seq_v2_mrna"
## [5] "acc_tcga_rna_seq_v2_mrna_median_Zscores"
## [6] "acc_tcga_linear_CNA"
## [7] "acc_tcga_methylation_hm450"
## [8] "acc_tcga_mutations"
## [9] "acc_tcga_rna_seq_v2_mrna_median_all_sample_Zscores"
The data for a molecular profile can be obtained with prior knowledge of
available entrezGeneIds
:
molecularData(cbio, molecularProfileId = "acc_tcga_rna_seq_v2_mrna",
entrezGeneIds = c(1, 2),
sampleIds = c("TCGA-OR-A5J1-01", "TCGA-OR-A5J2-01")
)
## $acc_tcga_rna_seq_v2_mrna
## # A tibble: 4 x 8
## uniqueSampleKey uniquePatientKey entrezGeneId molecularProfile… sampleId
## <chr> <chr> <int> <chr> <chr>
## 1 VENHQS1PUi1BNUoxL… VENHQS1PUi1BNUoxO… 1 acc_tcga_rna_seq… TCGA-OR-…
## 2 VENHQS1PUi1BNUoxL… VENHQS1PUi1BNUoxO… 2 acc_tcga_rna_seq… TCGA-OR-…
## 3 VENHQS1PUi1BNUoyL… VENHQS1PUi1BNUoyO… 1 acc_tcga_rna_seq… TCGA-OR-…
## 4 VENHQS1PUi1BNUoyL… VENHQS1PUi1BNUoyO… 2 acc_tcga_rna_seq… TCGA-OR-…
## # … with 3 more variables: patientId <chr>, studyId <chr>, value <dbl>
A list of all the genes provided by the API service including hugo symbols,
and entrez gene IDs can be obtained by using the geneTable
function:
geneTable(cbio)
## # A tibble: 1,000 x 3
## entrezGeneId hugoGeneSymbol type
## <int> <chr> <chr>
## 1 -95835 IVNS1ABP_PT330 phosphoprotein
## 2 -95834 IVNS1ABP_PT328 phosphoprotein
## 3 -95833 IVNS1ABP_PS329 phosphoprotein
## 4 -95832 IVNS1ABP_PS277 phosphoprotein
## 5 -95831 MORC2_PS785 phosphoprotein
## 6 -95830 MORC2_PS779 phosphoprotein
## 7 -95829 MORC2_PS777 phosphoprotein
## 8 -95828 MORC2_PS743 phosphoprotein
## 9 -95827 MORC2_PS739 phosphoprotein
## 10 -95826 MORC2_PS725 phosphoprotein
## # … with 990 more rows
genePanels(cbio)
## # A tibble: 50 x 2
## description genePanelId
## <chr> <chr>
## 1 Targeted (341 cancer genes) sequencing of various tumor types … IMPACT341
## 2 Targeted (410 cancer genes) sequencing of various tumor types … IMPACT410
## 3 Targeted (468 cancer genes) sequencing of various tumor types … IMPACT468
## 4 Targeted sequencing of urcc tumor via MSK-IMPACT. IMPACT
## 5 Targeted (300 cancer genes) sequencing of bladder urothelial c… IMPACT300
## 6 Targeted sequencing of urcc tumor via MSK-IMPACT. IMPACT230
## 7 Targeted (27 cancer genes) sequencing of adenoid cystic carcin… ACYC_FMI_27
## 8 Targeted (173 cancer genes) sequencing of breast cancers on Il… METABRIC_173
## 9 Targeted sequencing of 504 cancer-associated genes on Illumina… DFCI_504
## 10 DNA sequencing of 623 genes with known or potential relationsh… WUSTL-DFCI_6…
## # … with 40 more rows
getGenePanel(cbio, "IMPACT341")
## # A tibble: 341 x 2
## entrezGeneId hugoGeneSymbol
## <int> <chr>
## 1 25 ABL1
## 2 84142 ABRAXAS1
## 3 207 AKT1
## 4 208 AKT2
## 5 10000 AKT3
## 6 238 ALK
## 7 242 ALOX12B
## 8 139285 AMER1
## 9 324 APC
## 10 367 AR
## # … with 331 more rows
gprppa <- genePanelMolecular(cbio,
molecularProfileId = "acc_tcga_rppa",
sampleListId = "acc_tcga_all")
gprppa
## # A tibble: 92 x 7
## uniqueSampleKey uniquePatientKey molecularProfil… sampleId patientId studyId
## <chr> <chr> <chr> <chr> <chr> <chr>
## 1 VENHQS1PUi1BNUo… VENHQS1PUi1BNUo… acc_tcga_rppa TCGA-OR… TCGA-OR-… acc_tc…
## 2 VENHQS1PUi1BNUo… VENHQS1PUi1BNUo… acc_tcga_rppa TCGA-OR… TCGA-OR-… acc_tc…
## 3 VENHQS1PUi1BNUo… VENHQS1PUi1BNUo… acc_tcga_rppa TCGA-OR… TCGA-OR-… acc_tc…
## 4 VENHQS1PUi1BNUo… VENHQS1PUi1BNUo… acc_tcga_rppa TCGA-OR… TCGA-OR-… acc_tc…
## 5 VENHQS1PUi1BNUo… VENHQS1PUi1BNUo… acc_tcga_rppa TCGA-OR… TCGA-OR-… acc_tc…
## 6 VENHQS1PUi1BNUo… VENHQS1PUi1BNUo… acc_tcga_rppa TCGA-OR… TCGA-OR-… acc_tc…
## 7 VENHQS1PUi1BNUo… VENHQS1PUi1BNUo… acc_tcga_rppa TCGA-OR… TCGA-OR-… acc_tc…
## 8 VENHQS1PUi1BNUo… VENHQS1PUi1BNUo… acc_tcga_rppa TCGA-OR… TCGA-OR-… acc_tc…
## 9 VENHQS1PUi1BNUo… VENHQS1PUi1BNUo… acc_tcga_rppa TCGA-OR… TCGA-OR-… acc_tc…
## 10 VENHQS1PUi1BNUp… VENHQS1PUi1BNUp… acc_tcga_rppa TCGA-OR… TCGA-OR-… acc_tc…
## # … with 82 more rows, and 1 more variable: profiled <lgl>
getGenePanelMolecular(cbio,
molecularProfileIds = c("acc_tcga_rppa", "acc_tcga_gistic"),
sampleIds = allSamples(cbio, "acc_tcga")$sampleId
)
## # A tibble: 184 x 7
## uniqueSampleKey uniquePatientKey molecularProfil… sampleId patientId studyId
## <chr> <chr> <chr> <chr> <chr> <chr>
## 1 VENHQS1PUi1BNUo… VENHQS1PUi1BNUo… acc_tcga_gistic TCGA-OR… TCGA-OR-… acc_tc…
## 2 VENHQS1PUi1BNUo… VENHQS1PUi1BNUo… acc_tcga_gistic TCGA-OR… TCGA-OR-… acc_tc…
## 3 VENHQS1PUi1BNUo… VENHQS1PUi1BNUo… acc_tcga_gistic TCGA-OR… TCGA-OR-… acc_tc…
## 4 VENHQS1PUi1BNUo… VENHQS1PUi1BNUo… acc_tcga_gistic TCGA-OR… TCGA-OR-… acc_tc…
## 5 VENHQS1PUi1BNUo… VENHQS1PUi1BNUo… acc_tcga_gistic TCGA-OR… TCGA-OR-… acc_tc…
## 6 VENHQS1PUi1BNUo… VENHQS1PUi1BNUo… acc_tcga_gistic TCGA-OR… TCGA-OR-… acc_tc…
## 7 VENHQS1PUi1BNUo… VENHQS1PUi1BNUo… acc_tcga_gistic TCGA-OR… TCGA-OR-… acc_tc…
## 8 VENHQS1PUi1BNUo… VENHQS1PUi1BNUo… acc_tcga_gistic TCGA-OR… TCGA-OR-… acc_tc…
## 9 VENHQS1PUi1BNUo… VENHQS1PUi1BNUo… acc_tcga_gistic TCGA-OR… TCGA-OR-… acc_tc…
## 10 VENHQS1PUi1BNUp… VENHQS1PUi1BNUp… acc_tcga_gistic TCGA-OR… TCGA-OR-… acc_tc…
## # … with 174 more rows, and 1 more variable: profiled <lgl>
getDataByGenePanel(cbio, "acc_tcga", genePanelId = "IMPACT341",
molecularProfileId = "acc_tcga_rppa", sampleListId = "acc_tcga_rppa")
## $acc_tcga_rppa
## # A tibble: 2,622 x 9
## uniqueSampleKey uniquePatientKey entrezGeneId molecularProfil… sampleId
## <chr> <chr> <int> <chr> <chr>
## 1 VENHQS1PUi1BNUoyL… VENHQS1PUi1BNUoyO… 207 acc_tcga_rppa TCGA-OR-…
## 2 VENHQS1PUi1BNUoyL… VENHQS1PUi1BNUoyO… 208 acc_tcga_rppa TCGA-OR-…
## 3 VENHQS1PUi1BNUoyL… VENHQS1PUi1BNUoyO… 10000 acc_tcga_rppa TCGA-OR-…
## 4 VENHQS1PUi1BNUoyL… VENHQS1PUi1BNUoyO… 367 acc_tcga_rppa TCGA-OR-…
## 5 VENHQS1PUi1BNUoyL… VENHQS1PUi1BNUoyO… 472 acc_tcga_rppa TCGA-OR-…
## 6 VENHQS1PUi1BNUoyL… VENHQS1PUi1BNUoyO… 8314 acc_tcga_rppa TCGA-OR-…
## 7 VENHQS1PUi1BNUoyL… VENHQS1PUi1BNUoyO… 596 acc_tcga_rppa TCGA-OR-…
## 8 VENHQS1PUi1BNUoyL… VENHQS1PUi1BNUoyO… 598 acc_tcga_rppa TCGA-OR-…
## 9 VENHQS1PUi1BNUoyL… VENHQS1PUi1BNUoyO… 10018 acc_tcga_rppa TCGA-OR-…
## 10 VENHQS1PUi1BNUoyL… VENHQS1PUi1BNUoyO… 673 acc_tcga_rppa TCGA-OR-…
## # … with 2,612 more rows, and 4 more variables: patientId <chr>, studyId <chr>,
## # value <dbl>, hugoGeneSymbol <chr>
It uses the getAllGenesUsingGET
function from the API.
To display all available sample list identifiers for a particular study ID,
one can use the sampleLists
function:
sampleLists(cbio, "acc_tcga")
## # A tibble: 9 x 5
## category name description sampleListId studyId
## <chr> <chr> <chr> <chr> <chr>
## 1 all_cases_with_r… Samples with… Samples protein data … acc_tcga_rppa acc_tc…
## 2 all_cases_with_m… Samples with… Samples with mutation… acc_tcga_cnaseq acc_tc…
## 3 all_cases_in_stu… All samples All samples (92 sampl… acc_tcga_all acc_tc…
## 4 all_cases_with_c… Samples with… Samples with CNA data… acc_tcga_cna acc_tc…
## 5 all_cases_with_m… Samples with… Samples with mutation… acc_tcga_seque… acc_tc…
## 6 all_cases_with_m… Samples with… Samples with methylat… acc_tcga_methy… acc_tc…
## 7 all_cases_with_m… Samples with… Samples with mRNA exp… acc_tcga_rna_s… acc_tc…
## 8 all_cases_with_m… Complete sam… Samples with mutation… acc_tcga_3way_… acc_tc…
## 9 all_cases_with_m… Samples with… Samples with methylat… acc_tcga_methy… acc_tc…
One can obtain the barcodes / identifiers for each sample using a specific sample list identifier, in this case we want all the copy number alteration samples:
samplesInSampleLists(cbio, "acc_tcga_cna")
## CharacterList of length 1
## [["acc_tcga_cna"]] TCGA-OR-A5J1-01 TCGA-OR-A5J2-01 ... TCGA-PK-A5HC-01
This returns a CharacterList
of all identifiers for each sample list
identifier input:
samplesInSampleLists(cbio, c("acc_tcga_cna", "acc_tcga_cnaseq"))
## CharacterList of length 2
## [["acc_tcga_cna"]] TCGA-OR-A5J1-01 TCGA-OR-A5J2-01 ... TCGA-PK-A5HC-01
## [["acc_tcga_cnaseq"]] TCGA-OR-A5J1-01 TCGA-OR-A5J2-01 ... TCGA-PK-A5HC-01
allSamples(cbio, "acc_tcga")
## # A tibble: 92 x 6
## uniqueSampleKey uniquePatientKey sampleType sampleId patientId studyId
## <chr> <chr> <chr> <chr> <chr> <chr>
## 1 VENHQS1PUi1BNUoxL… VENHQS1PUi1BNUoxO… Primary So… TCGA-OR-… TCGA-OR-… acc_tc…
## 2 VENHQS1PUi1BNUoyL… VENHQS1PUi1BNUoyO… Primary So… TCGA-OR-… TCGA-OR-… acc_tc…
## 3 VENHQS1PUi1BNUozL… VENHQS1PUi1BNUozO… Primary So… TCGA-OR-… TCGA-OR-… acc_tc…
## 4 VENHQS1PUi1BNUo0L… VENHQS1PUi1BNUo0O… Primary So… TCGA-OR-… TCGA-OR-… acc_tc…
## 5 VENHQS1PUi1BNUo1L… VENHQS1PUi1BNUo1O… Primary So… TCGA-OR-… TCGA-OR-… acc_tc…
## 6 VENHQS1PUi1BNUo2L… VENHQS1PUi1BNUo2O… Primary So… TCGA-OR-… TCGA-OR-… acc_tc…
## 7 VENHQS1PUi1BNUo3L… VENHQS1PUi1BNUo3O… Primary So… TCGA-OR-… TCGA-OR-… acc_tc…
## 8 VENHQS1PUi1BNUo4L… VENHQS1PUi1BNUo4O… Primary So… TCGA-OR-… TCGA-OR-… acc_tc…
## 9 VENHQS1PUi1BNUo5L… VENHQS1PUi1BNUo5O… Primary So… TCGA-OR-… TCGA-OR-… acc_tc…
## 10 VENHQS1PUi1BNUpBL… VENHQS1PUi1BNUpBO… Primary So… TCGA-OR-… TCGA-OR-… acc_tc…
## # … with 82 more rows
getSampleInfo(cbio, studyId = "acc_tcga",
sampleListIds = c("acc_tcga_rppa", "acc_tcga_gistic"))
## # A tibble: 46 x 6
## uniqueSampleKey uniquePatientKey sampleType sampleId patientId studyId
## <chr> <chr> <chr> <chr> <chr> <chr>
## 1 VENHQS1PUi1BNUoyL… VENHQS1PUi1BNUoyO… Primary So… TCGA-OR-… TCGA-OR-… acc_tc…
## 2 VENHQS1PUi1BNUozL… VENHQS1PUi1BNUozO… Primary So… TCGA-OR-… TCGA-OR-… acc_tc…
## 3 VENHQS1PUi1BNUo2L… VENHQS1PUi1BNUo2O… Primary So… TCGA-OR-… TCGA-OR-… acc_tc…
## 4 VENHQS1PUi1BNUo3L… VENHQS1PUi1BNUo3O… Primary So… TCGA-OR-… TCGA-OR-… acc_tc…
## 5 VENHQS1PUi1BNUo4L… VENHQS1PUi1BNUo4O… Primary So… TCGA-OR-… TCGA-OR-… acc_tc…
## 6 VENHQS1PUi1BNUo5L… VENHQS1PUi1BNUo5O… Primary So… TCGA-OR-… TCGA-OR-… acc_tc…
## 7 VENHQS1PUi1BNUpBL… VENHQS1PUi1BNUpBO… Primary So… TCGA-OR-… TCGA-OR-… acc_tc…
## 8 VENHQS1PUi1BNUpQL… VENHQS1PUi1BNUpQO… Primary So… TCGA-OR-… TCGA-OR-… acc_tc…
## 9 VENHQS1PUi1BNUpSL… VENHQS1PUi1BNUpSO… Primary So… TCGA-OR-… TCGA-OR-… acc_tc…
## 10 VENHQS1PUi1BNUpTL… VENHQS1PUi1BNUpTO… Primary So… TCGA-OR-… TCGA-OR-… acc_tc…
## # … with 36 more rows
The cBioPortal
API representation is not limited to the functions
provided in the package. Users who wish to make use of any of the endpoints
provided by the API specification should use the dollar sign $
function
to access the endpoints.
First the user should see the input for a particular endpoint as detailed in the API:
cbio$getGeneUsingGET
## getGeneUsingGET
## Get a gene
##
## Parameters:
## geneId (string)
## Entrez Gene ID or Hugo Gene Symbol e.g. 1 or A1BG
Then the user can provide such input:
(resp <- cbio$getGeneUsingGET("BRCA1"))
## Response [https://www.cbioportal.org/api/genes/BRCA1]
## Date: 2021-04-22 22:15
## Status: 200
## Content-Type: application/json
## Size: 69 B
which will require the user to ‘translate’ the response using httr::content
:
httr::content(resp)
## $entrezGeneId
## [1] 672
##
## $hugoGeneSymbol
## [1] "BRCA1"
##
## $type
## [1] "protein-coding"
For users who wish to clear the entire cBioPortalData
cache, it is
recommended that they use:
unlink("~/.cache/cBioPortalData/")
sessionInfo()
## R version 4.0.5 (2021-03-31)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 18.04.5 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.12-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.12-bioc/R/lib/libRlapack.so
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=C
## [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
##
## attached base packages:
## [1] parallel stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] cBioPortalData_2.2.11 MultiAssayExperiment_1.16.0
## [3] SummarizedExperiment_1.20.0 Biobase_2.50.0
## [5] GenomicRanges_1.42.0 GenomeInfoDb_1.26.7
## [7] IRanges_2.24.1 S4Vectors_0.28.1
## [9] BiocGenerics_0.36.1 MatrixGenerics_1.2.1
## [11] matrixStats_0.58.0 AnVIL_1.2.0
## [13] dplyr_1.0.5 BiocStyle_2.18.1
##
## loaded via a namespace (and not attached):
## [1] bitops_1.0-6 bit64_4.0.5
## [3] progress_1.2.2 httr_1.4.2
## [5] GenomicDataCommons_1.14.0 tools_4.0.5
## [7] bslib_0.2.4 utf8_1.2.1
## [9] R6_2.5.0 DBI_1.1.1
## [11] withr_2.4.2 tidyselect_1.1.0
## [13] prettyunits_1.1.1 TCGAutils_1.10.1
## [15] bit_4.0.4 curl_4.3
## [17] compiler_4.0.5 cli_2.4.0
## [19] rvest_1.0.0 formatR_1.9
## [21] xml2_1.3.2 DelayedArray_0.16.3
## [23] rtracklayer_1.50.0 bookdown_0.22
## [25] sass_0.3.1 readr_1.4.0
## [27] askpass_1.1 rappdirs_0.3.3
## [29] rapiclient_0.1.3 RCircos_1.2.1
## [31] Rsamtools_2.6.0 stringr_1.4.0
## [33] digest_0.6.27 rmarkdown_2.7
## [35] XVector_0.30.0 pkgconfig_2.0.3
## [37] htmltools_0.5.1.1 dbplyr_2.1.1
## [39] fastmap_1.1.0 limma_3.46.0
## [41] rlang_0.4.10 rstudioapi_0.13
## [43] RSQLite_2.2.7 jquerylib_0.1.3
## [45] generics_0.1.0 jsonlite_1.7.2
## [47] BiocParallel_1.24.1 RCurl_1.98-1.3
## [49] magrittr_2.0.1 GenomeInfoDbData_1.2.4
## [51] futile.logger_1.4.3 Matrix_1.3-2
## [53] Rcpp_1.0.6 fansi_0.4.2
## [55] lifecycle_1.0.0 stringi_1.5.3
## [57] yaml_2.2.1 RaggedExperiment_1.14.2
## [59] RJSONIO_1.3-1.4 zlibbioc_1.36.0
## [61] BiocFileCache_1.14.0 grid_4.0.5
## [63] blob_1.2.1 crayon_1.4.1
## [65] lattice_0.20-41 Biostrings_2.58.0
## [67] splines_4.0.5 GenomicFeatures_1.42.3
## [69] hms_1.0.0 ps_1.6.0
## [71] knitr_1.32.9 pillar_1.6.0
## [73] codetools_0.2-18 biomaRt_2.46.3
## [75] futile.options_1.0.1 XML_3.99-0.6
## [77] glue_1.4.2 evaluate_0.14
## [79] lambda.r_1.2.4 data.table_1.14.0
## [81] BiocManager_1.30.12 vctrs_0.3.7
## [83] tidyr_1.1.3 openssl_1.4.3
## [85] purrr_0.3.4 assertthat_0.2.1
## [87] cachem_1.0.4 xfun_0.22
## [89] survival_3.2-10 tibble_3.1.1
## [91] RTCGAToolbox_2.20.0 GenomicAlignments_1.26.0
## [93] AnnotationDbi_1.52.0 memoise_2.0.0
## [95] ellipsis_0.3.1