The cBioPortal for Cancer Genomics website is a great resource for interactive exploration of study datasets. However, it does not easily allow the analyst to obtain and further analyze the data.
We’ve developed the cBioPortalData
package to fill this
need to programmatically access the data resources available on the
cBioPortal.
The cBioPortalData
package provides an R interface for
accessing the cBioPortal study data within the Bioconductor
ecosystem.
It downloads study data from the cBioPortal API (the full API specification can be found here https://cbioportal.org/api) and uses Bioconductor infrastructure to cache and represent the data.
We demonstrate common use cases of cBioPortalData
and
curatedTCGAData
during Bioconductor conference workshops.
We use the MultiAssayExperiment
(Ramos et al. (2017)) package to
integrate, represent, and coordinate multiple experiments for the
studies available in the cBioPortal. This package in conjunction with
curatedTCGAData
give access to a large trove of publicly
available bioinformatic data. Please see our JCO Clinical Cancer
Informatics publication here (Ramos et al. (2020)).
Our free and open source project depends on citations for funding.
When using cBioPortalData
, please cite the following
publications:
Data are provided as a single MultiAssayExperiment
per
study. The MultiAssayExperiment
representation usually
contains SummarizedExperiment
objects for expression data
and RaggedExperiment
objects for mutation and CNV-type
data. RaggedExperiment
is a data class for representing
‘ragged’ genomic location data, meaning that the measurements per sample
vary.
For more information, please see the RaggedExperiment
and SummarizedExperiment
vignettes.
As we work through the data, there are some datasest that cannot be
represented as MultiAssayExperiment
objects. This can be
due to a number of reasons such as the way the data is handled, presence
of mis-matched identifiers, invalid data types, etc. To see what
datasets are currently not building, we can look refer to
getStudies()
with the buildReport = TRUE
argument.
## Warning in readLines(url, encoding = "UTF-8"): incomplete final line found on
## '/tmp/Rtmpg5OLL2/Rinst35687e775849/cBioPortalData/service/cBioPortal/api.json'
## # A tibble: 6 × 15
## name description publicStudy pmid citation groups status importDate
## <chr> <chr> <lgl> <chr> <chr> <chr> <int> <chr>
## 1 Adenoid Cysti… Whole-exom… TRUE 2368… Ho et a… ACYC;… 0 2023-12-0…
## 2 Adenoid Cysti… Targeted S… TRUE 2441… Ross et… ACYC;… 0 2023-12-0…
## 3 Adenoid Cysti… Whole-geno… TRUE 2686… Rettig … ACYC;… 0 2023-12-0…
## 4 Adenoid Cysti… WGS of 21 … TRUE 2663… Mitani … ACYC;… 0 2023-12-0…
## 5 Adenoid Cysti… Whole-geno… TRUE 2682… Drier e… ACYC 0 2023-12-0…
## 6 Adenoid Cysti… Whole exom… TRUE 2377… Stephen… ACYC;… 0 2023-12-0…
## # ℹ 7 more variables: allSampleCount <int>, readPermission <lgl>,
## # studyId <chr>, cancerTypeId <chr>, referenceGenome <chr>, api_build <lgl>,
## # pack_build <lgl>
The last two columns will show the availability of each
studyId
for either download method (pack_build
for cBioDataPack
and api_build
for
cBioPortalData
).
There are two main user-facing functions for downloading data from the cBioPortal API.
cBioDataPack
makes use of the tarball distribution
of study data. This is useful when the user wants to download and
analyze the entirety of the data as available from the cBioPortal.org
website.
cBioPortalData
allows a more flexibile approach to
obtaining study data based on the available parameters such as molecular
profile identifiers. This option is useful for users who have a set of
gene symbols or identifiers and would like to get a smaller subset of
the data that correspond to a particular molecular profile.
This function will access the packaged data from and return an integrative MultiAssayExperiment representation.
## A MultiAssayExperiment object of 12 listed
## experiments with user-defined names and respective classes.
## Containing an ExperimentList class object of length 12:
## [1] cna: SummarizedExperiment with 24776 rows and 191 columns
## [2] cna_hg19.seg: RaggedExperiment with 13571 rows and 191 columns
## [3] linear_cna: SummarizedExperiment with 24776 rows and 191 columns
## [4] methylation_hm27: SummarizedExperiment with 10968 rows and 194 columns
## [5] methylation_hm450: SummarizedExperiment with 10968 rows and 194 columns
## [6] mrna_seq_rpkm: SummarizedExperiment with 19720 rows and 179 columns
## [7] mrna_seq_rpkm_zscores_ref_all_samples: SummarizedExperiment with 19720 rows and 179 columns
## [8] mrna_seq_rpkm_zscores_ref_diploid_samples: SummarizedExperiment with 19719 rows and 179 columns
## [9] mrna_seq_v2_rsem: SummarizedExperiment with 20531 rows and 173 columns
## [10] mrna_seq_v2_rsem_zscores_ref_all_samples: SummarizedExperiment with 20531 rows and 173 columns
## [11] mrna_seq_v2_rsem_zscores_ref_diploid_samples: SummarizedExperiment with 20440 rows and 173 columns
## [12] mutations: RaggedExperiment with 2584 rows and 197 columns
## Functionality:
## experiments() - obtain the ExperimentList instance
## colData() - the primary/phenotype DataFrame
## sampleMap() - the sample coordination DataFrame
## `$`, `[`, `[[` - extract colData columns, subset, or experiment
## *Format() - convert into a long or wide DataFrame
## assays() - convert ExperimentList to a SimpleList of matrices
## exportClass() - save data to flat files
This function provides a more flexible and granular way to request a
MultiAssayExperiment
object from a study ID, molecular
profile, gene panel, sample list.
acc <- cBioPortalData(api = cbio, by = "hugoGeneSymbol", studyId = "acc_tcga",
genePanelId = "IMPACT341",
molecularProfileIds = c("acc_tcga_rppa", "acc_tcga_linear_CNA")
)
## harmonizing input:
## removing 1 colData rownames not in sampleMap 'primary'
## A MultiAssayExperiment object of 2 listed
## experiments with user-defined names and respective classes.
## Containing an ExperimentList class object of length 2:
## [1] acc_tcga_linear_CNA: SummarizedExperiment with 339 rows and 90 columns
## [2] acc_tcga_rppa: SummarizedExperiment with 57 rows and 46 columns
## Functionality:
## experiments() - obtain the ExperimentList instance
## colData() - the primary/phenotype DataFrame
## sampleMap() - the sample coordination DataFrame
## `$`, `[`, `[[` - 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
Note. To avoid overloading the API service, the API was designed to only query a part of the study data. Therefore, the user is required to enter either a set of genes of interest or a gene panel identifier.
Note that cBioPortalData
and cBioDataPack
obtain data diligently curated by the cBio Portal data team. The
original data and curation lies in the https://github.com/cBioPortal/cBioPortal GitHub
repository. However, despite the curation efforts there may be some
inconsistencies in identifiers in the data. This causes our software to
not work as intended though we have made efforts to represent all the
data from both API and tarball formats.
You may notice that the metadata()
may have some
additional data that was not able to be integrated in the
MultiAssayExperiment
.
## [[1]]
## # A tibble: 30,510 × 6
## uniqueSampleKey uniquePatientKey entrezGeneId molecularProfileId patientId
## <chr> <chr> <int> <chr> <chr>
## 1 VENHQS1PUi1BNUoxL… VENHQS1PUi1BNUo… 25 acc_tcga_linear_C… TCGA-OR-…
## 2 VENHQS1PUi1BNUoxL… VENHQS1PUi1BNUo… 142 acc_tcga_linear_C… TCGA-OR-…
## 3 VENHQS1PUi1BNUoxL… VENHQS1PUi1BNUo… 207 acc_tcga_linear_C… TCGA-OR-…
## 4 VENHQS1PUi1BNUoxL… VENHQS1PUi1BNUo… 208 acc_tcga_linear_C… TCGA-OR-…
## 5 VENHQS1PUi1BNUoxL… VENHQS1PUi1BNUo… 238 acc_tcga_linear_C… TCGA-OR-…
## 6 VENHQS1PUi1BNUoxL… VENHQS1PUi1BNUo… 242 acc_tcga_linear_C… TCGA-OR-…
## 7 VENHQS1PUi1BNUoxL… VENHQS1PUi1BNUo… 324 acc_tcga_linear_C… TCGA-OR-…
## 8 VENHQS1PUi1BNUoxL… VENHQS1PUi1BNUo… 331 acc_tcga_linear_C… TCGA-OR-…
## 9 VENHQS1PUi1BNUoxL… VENHQS1PUi1BNUo… 367 acc_tcga_linear_C… TCGA-OR-…
## 10 VENHQS1PUi1BNUoxL… VENHQS1PUi1BNUo… 369 acc_tcga_linear_C… TCGA-OR-…
## # ℹ 30,500 more rows
## # ℹ 1 more variable: studyId <chr>
##
## [[2]]
## # A tibble: 2,622 × 6
## uniqueSampleKey uniquePatientKey entrezGeneId molecularProfileId patientId
## <chr> <chr> <int> <chr> <chr>
## 1 VENHQS1PUi1BNUoyL… VENHQS1PUi1BNUo… 207 acc_tcga_rppa TCGA-OR-…
## 2 VENHQS1PUi1BNUoyL… VENHQS1PUi1BNUo… 208 acc_tcga_rppa TCGA-OR-…
## 3 VENHQS1PUi1BNUoyL… VENHQS1PUi1BNUo… 367 acc_tcga_rppa TCGA-OR-…
## 4 VENHQS1PUi1BNUoyL… VENHQS1PUi1BNUo… 472 acc_tcga_rppa TCGA-OR-…
## 5 VENHQS1PUi1BNUoyL… VENHQS1PUi1BNUo… 595 acc_tcga_rppa TCGA-OR-…
## 6 VENHQS1PUi1BNUoyL… VENHQS1PUi1BNUo… 596 acc_tcga_rppa TCGA-OR-…
## 7 VENHQS1PUi1BNUoyL… VENHQS1PUi1BNUo… 598 acc_tcga_rppa TCGA-OR-…
## 8 VENHQS1PUi1BNUoyL… VENHQS1PUi1BNUo… 673 acc_tcga_rppa TCGA-OR-…
## 9 VENHQS1PUi1BNUoyL… VENHQS1PUi1BNUo… 675 acc_tcga_rppa TCGA-OR-…
## 10 VENHQS1PUi1BNUoyL… VENHQS1PUi1BNUo… 898 acc_tcga_rppa TCGA-OR-…
## # ℹ 2,612 more rows
## # ℹ 1 more variable: studyId <chr>
You will also get a message for studyId
s whose data has
not been fully integrated into a MultiAssayExperiment
.
## Our testing shows that '%s' is not currently building.
## Use 'downloadStudy()' to manually obtain the data.
## Proceed anyway? [y/n]: y
For this reason, we have also provided the
downloadStudy
, untarStudy
, and
loadStudy
functions to allow researchers to simply download
the data and potentially, manually curate it. Generally, we advise
researchers to report inconsistencies in the data in the cBioPortal data
repository.
In cases where a download is interrupted, the user may experience a
corrupt cache. The user can clear the cache for a particular study by
using the removeCache
function. Note that this function
only works for data downloaded through the cBioDataPack
function.
We can use information in the colData
to draw a K-M plot
with a few variables from the colData
slot of the
MultiAssayExperiment
. First, we load the necessary
packages:
We can check the data to lookout for any issues.
##
## 0:LIVING 1:DECEASED
## 67 133
## [1] "character"
Now, we clean the data a bit to ensure that our variables are of the right type for the subsequent survival model fit.
collaml <- colData(laml)
collaml[collaml$OS_MONTHS == "[Not Available]", "OS_MONTHS"] <- NA
collaml$OS_MONTHS <- as.numeric(collaml$OS_MONTHS)
colData(laml) <- collaml
We specify a simple survival model using SEX
as a
covariate and we draw the K-M plot.
fit <- survfit(
Surv(OS_MONTHS, as.numeric(substr(OS_STATUS, 1, 1))) ~ SEX,
data = colData(laml)
)
ggsurvplot(fit, data = colData(laml), risk.table = TRUE)
If you are interested in a particular study dataset that is not currently building, please open an issue at our GitHub repository and we will do our best to resolve the issues with the code base. Data issues can be opened at the cBioPortal data repository.
We appreciate your feedback!
## R version 4.4.1 (2024-06-14)
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## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
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##
## other attached packages:
## [1] survminer_0.5.0 ggpubr_0.6.0
## [3] ggplot2_3.5.1 survival_3.7-0
## [5] cBioPortalData_2.19.0 MultiAssayExperiment_1.33.0
## [7] SummarizedExperiment_1.35.5 Biobase_2.67.0
## [9] GenomicRanges_1.59.0 GenomeInfoDb_1.43.0
## [11] IRanges_2.41.0 S4Vectors_0.43.2
## [13] BiocGenerics_0.53.0 MatrixGenerics_1.19.0
## [15] matrixStats_1.4.1 AnVIL_1.19.0
## [17] AnVILBase_1.1.0 dplyr_1.1.4
## [19] BiocStyle_2.35.0
##
## loaded via a namespace (and not attached):
## [1] sys_3.4.3 jsonlite_1.8.9
## [3] magrittr_2.0.3 GenomicFeatures_1.59.0
## [5] farver_2.1.2 rmarkdown_2.28
## [7] BiocIO_1.17.0 zlibbioc_1.51.2
## [9] vctrs_0.6.5 memoise_2.0.1
## [11] Rsamtools_2.21.2 RCurl_1.98-1.16
## [13] rstatix_0.7.2 htmltools_0.5.8.1
## [15] S4Arrays_1.5.11 BiocBaseUtils_1.9.0
## [17] lambda.r_1.2.4 curl_5.2.3
## [19] broom_1.0.7 Formula_1.2-5
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## [23] bslib_0.8.0 htmlwidgets_1.6.4
## [25] httr2_1.0.5 zoo_1.8-12
## [27] futile.options_1.0.1 cachem_1.1.0
## [29] commonmark_1.9.2 buildtools_1.0.0
## [31] GenomicAlignments_1.43.0 mime_0.12
## [33] lifecycle_1.0.4 pkgconfig_2.0.3
## [35] Matrix_1.7-1 R6_2.5.1
## [37] fastmap_1.2.0 GenomeInfoDbData_1.2.13
## [39] shiny_1.9.1 digest_0.6.37
## [41] colorspace_2.1-1 RaggedExperiment_1.31.0
## [43] AnnotationDbi_1.69.0 RSQLite_2.3.7
## [45] labeling_0.4.3 filelock_1.0.3
## [47] RTCGAToolbox_2.35.1 km.ci_0.5-6
## [49] fansi_1.0.6 RJSONIO_1.3-1.9
## [51] httr_1.4.7 abind_1.4-8
## [53] compiler_4.4.1 bit64_4.5.2
## [55] withr_3.0.2 backports_1.5.0
## [57] BiocParallel_1.41.0 carData_3.0-5
## [59] DBI_1.2.3 highr_0.11
## [61] ggsignif_0.6.4 rappdirs_0.3.3
## [63] DelayedArray_0.33.1 rjson_0.2.23
## [65] tools_4.4.1 httpuv_1.6.15
## [67] glue_1.8.0 restfulr_0.0.15
## [69] promises_1.3.0 gridtext_0.1.5
## [71] grid_4.4.1 generics_0.1.3
## [73] gtable_0.3.6 KMsurv_0.1-5
## [75] tzdb_0.4.0 tidyr_1.3.1
## [77] data.table_1.16.2 hms_1.1.3
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## [83] markdown_1.13 pillar_1.9.0
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## [93] tidyselect_1.2.1 maketools_1.3.1
## [95] Biostrings_2.75.0 miniUI_0.1.1.1
## [97] knitr_1.48 gridExtra_2.3
## [99] futile.logger_1.4.3 xfun_0.48
## [101] DT_0.33 stringi_1.8.4
## [103] UCSC.utils_1.1.0 yaml_2.3.10
## [105] evaluate_1.0.1 codetools_0.2-20
## [107] tibble_3.2.1 BiocManager_1.30.25
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