cgdsr to cBioPortalData: Migration Tutorial

Introduction

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

Loading the package

library(cBioPortalData)

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: 399 × 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 Adenoid Cyst… Whole exom… TRUE        2609… Martelo… ACYC;…      0 2023-12-0…
FALSE  [38;5;250m 2 [39m Adenoid Cyst… Whole-exom… TRUE        2368… Ho et a… ACYC;…      0 2023-12-0…
FALSE  [38;5;250m 3 [39m Adenoid Cyst… Targeted S… TRUE        2441… Ross et… ACYC;…      0 2023-12-0…
FALSE  [38;5;250m 4 [39m Adenoid Cyst… Whole-geno… TRUE        2686… Rettig … ACYC;…      0 2023-12-0…
FALSE  [38;5;250m 5 [39m Adenoid Cyst… WGS of 21 … TRUE        2663… Mitani … ACYC;…      0 2023-12-0…
FALSE  [38;5;250m 6 [39m Adenoid Cyst… Whole-geno… TRUE        2682… Drier e… ACYC        0 2023-12-0…
FALSE  [38;5;250m 7 [39m Adenoid Cyst… Whole exom… TRUE        2377… Stephen… ACYC;…      0 2023-12-0…
FALSE  [38;5;250m 8 [39m Adenoid Cyst… Multi-Inst… TRUE        3148… Allen e… ACYC;…      0 2023-12-0…
FALSE  [38;5;250m 9 [39m Basal Cell C… Whole-exom… TRUE        2695… Bonilla… PUBLIC      0 2023-12-0…
FALSE  [38;5;250m10 [39m Acute Lympho… Comprehens… TRUE        2573… Anderss… PUBLIC      0 2023-12-0…
FALSE  [38;5;246m# ℹ 389 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> [39m

Note that the studyId column is important for further queries.

head(getStudies(cbio)[["studyId"]])

## [1] "acbc_mskcc_2015" "acyc_mskcc_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.
sampleListId identifies groups of patientId based on profile type
The sampleLists function uses studyId input to return sampleListId

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-12-0620-01

getSampleInfo

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 cancerStudy identifier
The case_list_description describes 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: 2024-02-02 20:32
##   Status: 200
##   Content-Type: application/json
##   Size: 3.31 kB

`cgdsr` (Clinical)

Notes

getClinicalData uses case_list_id as input without specifying the study_id as case list identifiers are unique to each study.

getClinicalData

We query clinical data for the gbm_tcga_pub_expr_classical case list identifier which is part of the gbm_tcga_pub study.

getClinicalData.CGDS(x = cgds,
    caseList = "gbm_tcga_pub_expr_classical"
)

Clinical 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.

cgdsr returns a data.frame with sampleId (TCGA.02.0009.01) but not patientId (TCGA.02.0009)
cBioPortalData returns sampleId (TCGA-02-0009-01) and patientId (TCGA-02-0009).
Note the differences in identifier delimiters between the two packages, cgdsr provides case_ids with . and cBioPortalData returns patientIds 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.

`cgdsr` (getGeneticProfiles)

getGeneticProfiles.CGDS(cgds, cancerStudy = "gbm_tcga_pub")

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       0.3967758

Mutation 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: 609 × 10
##    uniqueSampleKey     uniquePatientKey entrezGeneId molecularProfileId sampleId
##    <chr>               <chr>                   <int> <chr>              <chr>   
##  1 VENHQS0wMi0wMDAxLT… VENHQS0wMi0wMDA…           25 gbm_tcga_pub_cna_… TCGA-02…
##  2 VENHQS0wMi0wMDAxLT… VENHQS0wMi0wMDA…         4763 gbm_tcga_pub_cna_… TCGA-02…
##  3 VENHQS0wMi0wMDAxLT… VENHQS0wMi0wMDA…         7157 gbm_tcga_pub_cna_… TCGA-02…
##  4 VENHQS0wMi0wMDAzLT… VENHQS0wMi0wMDA…           25 gbm_tcga_pub_cna_… TCGA-02…
##  5 VENHQS0wMi0wMDAzLT… VENHQS0wMi0wMDA…         4763 gbm_tcga_pub_cna_… TCGA-02…
##  6 VENHQS0wMi0wMDAzLT… VENHQS0wMi0wMDA…         7157 gbm_tcga_pub_cna_… TCGA-02…
##  7 VENHQS0wMi0wMDA2LT… VENHQS0wMi0wMDA…           25 gbm_tcga_pub_cna_… TCGA-02…
##  8 VENHQS0wMi0wMDA2LT… VENHQS0wMi0wMDA…         4763 gbm_tcga_pub_cna_… TCGA-02…
##  9 VENHQS0wMi0wMDA2LT… VENHQS0wMi0wMDA…         7157 gbm_tcga_pub_cna_… TCGA-02…
## 10 VENHQS0wMi0wMDA3LT… VENHQS0wMi0wMDA…           25 gbm_tcga_pub_cna_… TCGA-02…
## # ℹ 599 more rows
## # ℹ 5 more variables: patientId <chr>, studyId <chr>, value <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 3 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 3 rows and 203 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

sessionInfo()

## R version 4.3.2 (2023-10-31)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 22.04.3 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-r0.3.20.so;  LAPACK version 3.10.0
## 
## 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_2.14.2       MultiAssayExperiment_1.28.0
##  [3] SummarizedExperiment_1.32.0 Biobase_2.62.0             
##  [5] GenomicRanges_1.54.1        GenomeInfoDb_1.38.5        
##  [7] IRanges_2.36.0              S4Vectors_0.40.2           
##  [9] BiocGenerics_0.48.1         MatrixGenerics_1.14.0      
## [11] matrixStats_1.2.0           AnVIL_1.14.1               
## [13] dplyr_1.1.4                 BiocStyle_2.30.0           
## 
## loaded via a namespace (and not attached):
##   [1] jsonlite_1.8.8            magrittr_2.0.3           
##   [3] GenomicFeatures_1.54.3    rmarkdown_2.25           
##   [5] fs_1.6.3                  BiocIO_1.12.0            
##   [7] zlibbioc_1.48.0           ragg_1.2.7               
##   [9] vctrs_0.6.5               memoise_2.0.1            
##  [11] Rsamtools_2.18.0          RCurl_1.98-1.14          
##  [13] htmltools_0.5.7           S4Arrays_1.2.0           
##  [15] progress_1.2.3            lambda.r_1.2.4           
##  [17] curl_5.2.0                SparseArray_1.2.3        
##  [19] sass_0.4.8                bslib_0.6.1              
##  [21] htmlwidgets_1.6.4         desc_1.4.3               
##  [23] futile.options_1.0.1      cachem_1.0.8             
##  [25] GenomicAlignments_1.38.2  mime_0.12                
##  [27] lifecycle_1.0.4           pkgconfig_2.0.3          
##  [29] Matrix_1.6-5              R6_2.5.1                 
##  [31] fastmap_1.1.1             GenomeInfoDbData_1.2.11  
##  [33] shiny_1.8.0               digest_0.6.34            
##  [35] RaggedExperiment_1.26.0   AnnotationDbi_1.64.1     
##  [37] textshaping_0.3.7         RSQLite_2.3.5            
##  [39] filelock_1.0.3            RTCGAToolbox_2.32.1      
##  [41] fansi_1.0.6               RJSONIO_1.3-1.9          
##  [43] httr_1.4.7                abind_1.4-5              
##  [45] compiler_4.3.2            bit64_4.0.5              
##  [47] withr_3.0.0               BiocParallel_1.36.0      
##  [49] DBI_1.2.1                 biomaRt_2.58.2           
##  [51] rappdirs_0.3.3            DelayedArray_0.28.0      
##  [53] rjson_0.2.21              tools_4.3.2              
##  [55] httpuv_1.6.14             glue_1.7.0               
##  [57] restfulr_0.0.15           promises_1.2.1           
##  [59] grid_4.3.2                generics_0.1.3           
##  [61] tzdb_0.4.0                tidyr_1.3.1              
##  [63] data.table_1.15.0         hms_1.1.3                
##  [65] xml2_1.3.6                utf8_1.2.4               
##  [67] XVector_0.42.0            pillar_1.9.0             
##  [69] stringr_1.5.1             later_1.3.2              
##  [71] BiocFileCache_2.10.1      lattice_0.22-5           
##  [73] rtracklayer_1.62.0        bit_4.0.5                
##  [75] tidyselect_1.2.0          Biostrings_2.70.2        
##  [77] miniUI_0.1.1.1            knitr_1.45               
##  [79] bookdown_0.37             futile.logger_1.4.3      
##  [81] xfun_0.41                 DT_0.31                  
##  [83] stringi_1.8.3             yaml_2.3.8               
##  [85] evaluate_0.23             codetools_0.2-19         
##  [87] tibble_3.2.1              BiocManager_1.30.22.4    
##  [89] cli_3.6.2                 xtable_1.8-4             
##  [91] systemfonts_1.0.5         jquerylib_0.1.4          
##  [93] Rcpp_1.0.12               GenomicDataCommons_1.26.0
##  [95] dbplyr_2.4.0              png_0.1-8                
##  [97] XML_3.99-0.16.1           rapiclient_0.1.3         
##  [99] parallel_4.3.2            TCGAutils_1.22.2         
## [101] ellipsis_0.3.2            pkgdown_2.0.7            
## [103] readr_2.1.5               blob_1.2.4               
## [105] prettyunits_1.2.0         bitops_1.0-7             
## [107] purrr_1.0.2               crayon_1.5.2             
## [109] rlang_1.1.3               KEGGREST_1.42.0          
## [111] rvest_1.0.3               formatR_1.14

Karim Mezhoud & Marcel Ramos

February 02, 2024

Introduction

Loading the package

Discovering studies

cBioPortalData setup

cgdsr setup

Obtaining Cases

cBioPortalData (Cases)

Notes

sampleLists

samples from sampleLists

getSampleInfo

cgdsr (Cases)

Notes

getCaseLists and getClinicalData

Obtaining Clinical Data

cBioPortalData (Clinical)

All clinical data

By sample data

cgdsr (Clinical)

Notes

getClinicalData

Clinical Data Summary

Molecular or Genetic Profiles

cBioPortalData (molecularProfiles)

cgdsr (getGeneticProfiles)

Genomic Profile Data for a set of genes

cBioPortalData (Indentify samples and genes)

Convert hugoGeneSymbol to entrezGeneId

Obtain all samples in study

cgdsr (Profile Data)

Molecular Data with cBioPortalData

molecularData

getDataByGenes

cBioPortalData: the main end-user function

Mutation Data

cBioPortalData (mutationData)

cgdsr (getMutationData)

Copy Number Alteration (CNA)

cBioPortalData (CNA)

cgdsr (CNA)

Methylation Data

cBioPortalData (Methylation)

cgdsr (Methylation)

sessionInfo

`cBioPortalData` setup

`cgdsr` setup

`cBioPortalData` (Cases)

`cgdsr` (Cases)

`getCaseLists` and `getClinicalData`

`cBioPortalData` (Clinical)

`cgdsr` (Clinical)

`cBioPortalData` (molecularProfiles)

`cgdsr` (getGeneticProfiles)

`cBioPortalData` (Indentify samples and genes)

Convert `hugoGeneSymbol` to `entrezGeneId`

`cgdsr` (Profile Data)

Molecular Data with `cBioPortalData`

`molecularData`

`getDataByGenes`

`cBioPortalData`: the main end-user function

`cBioPortalData` (mutationData)

`cgdsr` (getMutationData)

`cBioPortalData` (CNA)

`cgdsr` (CNA)

`cBioPortalData` (Methylation)

`cgdsr` (Methylation)