cBioPortalData: User Start Guide
January 30, 2022
Contents
Installation
library(cBioPortalData)
library(AnVIL)Introduction
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 use the MultiAssayExperiment (@Ramos2017-er) package to integrate,
represent, and coordinate multiple experiments for the studies availble in the
cBioPortal. This package in conjunction with curatedTCGAData give access to
a large trove of publicly available bioinformatic data. Please see our
publication here (@Ramos2020-ya).
We demonstrate common use cases of cBioPortalData and curatedTCGAData
during Bioconductor conference
workshops.
Overview
Data Structures
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.
Identifying available studies
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.
cbio <- cBioPortal()
studies <- getStudies(cbio, buildReport = TRUE)
head(studies)## # A tibble: 6 x 15
## name description publicStudy pmid citation groups status importDate
## <chr> <chr> <lgl> <chr> <chr> <chr> <int> <chr>
## 1 Prostate Aden~ "Genome-wi~ TRUE 2502~ Hierony~ "PUBL~ 0 2021-04-2~
## 2 China Pan-can~ "Landscape~ TRUE <NA> <NA> "" 0 2021-09-2~
## 3 Skin Cutaneou~ "Skin Cuta~ TRUE 2962~ TCGA, C~ "PUBL~ 0 2021-10-2~
## 4 Disparities i~ "Targeted ~ TRUE <NA> <NA> "" 0 2021-10-2~
## 5 Adrenocortica~ "TCGA Adre~ TRUE <NA> <NA> "PUBL~ 0 2021-10-2~
## 6 Adenoid Cysti~ "Whole exo~ TRUE 2377~ Stephen~ "ACYC~ 0 2021-10-2~
## # ... with 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).
Choosing download method
There are two main user-facing functions for downloading data from the cBioPortal API.
cBioDataPackmakes 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.cBioPortalDataallows 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.
Two main functions
cBioDataPack: Obtain Study Data as Zipped Tarballs
This function will access the packaged data from and return an integrative MultiAssayExperiment representation.
## Use ask=FALSE for non-interactive use
laml <- cBioDataPack("laml_tcga", ask = FALSE)
laml## A MultiAssayExperiment object of 13 listed
## experiments with user-defined names and respective classes.
## Containing an ExperimentList class object of length 13:
## [1] CNA: SummarizedExperiment with 24776 rows and 191 columns
## [2] RNA_Seq_expression_median: SummarizedExperiment with 19720 rows and 179 columns
## [3] RNA_Seq_mRNA_median_Zscores: SummarizedExperiment with 19719 rows and 179 columns
## [4] RNA_Seq_mRNA_median_all_sample_Zscores: SummarizedExperiment with 19720 rows and 179 columns
## [5] RNA_Seq_v2_expression_median: SummarizedExperiment with 20531 rows and 173 columns
## [6] RNA_Seq_v2_mRNA_median_Zscores: SummarizedExperiment with 20440 rows and 173 columns
## [7] RNA_Seq_v2_mRNA_median_all_sample_Zscores: SummarizedExperiment with 20531 rows and 173 columns
## [8] cna_hg19.seg: RaggedExperiment with 13571 rows and 191 columns
## [9] linear_CNA: SummarizedExperiment with 24776 rows and 191 columns
## [10] methylation_hm27: SummarizedExperiment with 10968 rows and 194 columns
## [11] methylation_hm450: SummarizedExperiment with 10968 rows and 194 columns
## [12] mutations_extended: RaggedExperiment with 2584 rows and 197 columns
## [13] mutations_mskcc: RaggedExperiment with 2584 rows and 197 columns
## Functionality:
## experiments() - obtain the ExperimentList instance
## colData() - the primary/phenotype DataFrame
## sampleMap() - the sample coordination DataFrame
## `$`, `[`, `[[` - extract colData columns, subset, or experiment
## *Format() - convert into a long or wide DataFrame
## assays() - convert ExperimentList to a SimpleList of matrices
## exportClass() - save data to flat filescBioPortalData: Obtain data from the cBioPortal API
This function provides a more flexible and granular way to request a MultiAssayExperiment object from a study ID, molecular profile, gene panel, sample list.
acc <- cBioPortalData(api = cbio, by = "hugoGeneSymbol", studyId = "acc_tcga",
genePanelId = "IMPACT341",
molecularProfileIds = c("acc_tcga_rppa", "acc_tcga_linear_CNA")
)## harmonizing input:
## removing 1 colData rownames not in sampleMap 'primary'acc## A MultiAssayExperiment object of 2 listed
## experiments with user-defined names and respective classes.
## Containing an ExperimentList class object of length 2:
## [1] acc_tcga_rppa: SummarizedExperiment with 57 rows and 46 columns
## [2] acc_tcga_linear_CNA: SummarizedExperiment with 339 rows and 90 columns
## Functionality:
## experiments() - obtain the ExperimentList instance
## colData() - the primary/phenotype DataFrame
## sampleMap() - the sample coordination DataFrame
## `$`, `[`, `[[` - 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 filesNote. 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.
Clearing the cache
cBioDataPack
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.
removeCache("laml_tcga")cBioPortalData
For users who wish to clear the entire cBioPortalData cache, it is
recommended that they use:
unlink("~/.cache/cBioPortalData/")Example Analysis: Kaplan-Meier Plot
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:
library(survival)
library(survminer)We can check the data to lookout for any issues.
table(colData(laml)$OS_STATUS)##
## 0:LIVING 1:DECEASED
## 67 133class(colData(laml)$OS_MONTHS)## [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) <- collamlWe 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)
Data update requests
If you are interested in a particular study dataset that is not currently building, please open an issue at our GitHub repository location and we will do our best to resolve the issues with either the data or the code.
We appreciate your feedback!
sessionInfo
sessionInfo()## R version 4.1.2 (2021-11-01)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows Server 2012 R2 x64 (build 9600)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=C
## [2] LC_CTYPE=English_United States.1252
## [3] LC_MONETARY=English_United States.1252
## [4] LC_NUMERIC=C
## [5] LC_TIME=English_United States.1252
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] survminer_0.4.9 ggpubr_0.4.0
## [3] ggplot2_3.3.5 survival_3.2-13
## [5] cBioPortalData_2.6.1 MultiAssayExperiment_1.20.0
## [7] SummarizedExperiment_1.24.0 Biobase_2.54.0
## [9] GenomicRanges_1.46.1 GenomeInfoDb_1.30.1
## [11] IRanges_2.28.0 S4Vectors_0.32.3
## [13] BiocGenerics_0.40.0 MatrixGenerics_1.6.0
## [15] matrixStats_0.61.0 AnVIL_1.6.2
## [17] dplyr_1.0.7 BiocStyle_2.22.0
##
## loaded via a namespace (and not attached):
## [1] backports_1.4.1 BiocFileCache_2.2.1
## [3] RCircos_1.2.2 splines_4.1.2
## [5] BiocParallel_1.28.3 TCGAutils_1.14.0
## [7] digest_0.6.29 htmltools_0.5.2
## [9] magick_2.7.3 fansi_1.0.2
## [11] magrittr_2.0.2 memoise_2.0.1
## [13] tzdb_0.2.0 limma_3.50.0
## [15] Biostrings_2.62.0 readr_2.1.1
## [17] vroom_1.5.7 prettyunits_1.1.1
## [19] colorspace_2.0-2 blob_1.2.2
## [21] rvest_1.0.2 rappdirs_0.3.3
## [23] xfun_0.29 crayon_1.4.2
## [25] RCurl_1.98-1.5 jsonlite_1.7.3
## [27] RaggedExperiment_1.18.0 zoo_1.8-9
## [29] glue_1.6.1 GenomicDataCommons_1.18.0
## [31] gtable_0.3.0 zlibbioc_1.40.0
## [33] XVector_0.34.0 DelayedArray_0.20.0
## [35] car_3.0-12 abind_1.4-5
## [37] scales_1.1.1 futile.options_1.0.1
## [39] DBI_1.1.2 rstatix_0.7.0
## [41] Rcpp_1.0.8 xtable_1.8-4
## [43] progress_1.2.2 gridtext_0.1.4
## [45] bit_4.0.4 km.ci_0.5-2
## [47] httr_1.4.2 ellipsis_0.3.2
## [49] pkgconfig_2.0.3 XML_3.99-0.8
## [51] rapiclient_0.1.3 farver_2.1.0
## [53] sass_0.4.0 dbplyr_2.1.1
## [55] utf8_1.2.2 RJSONIO_1.3-1.6
## [57] tidyselect_1.1.1 labeling_0.4.2
## [59] rlang_1.0.0 AnnotationDbi_1.56.2
## [61] munsell_0.5.0 tools_4.1.2
## [63] cachem_1.0.6 cli_3.1.1
## [65] generics_0.1.1 RSQLite_2.2.9
## [67] broom_0.7.12 evaluate_0.14
## [69] stringr_1.4.0 fastmap_1.1.0
## [71] yaml_2.2.2 knitr_1.37
## [73] bit64_4.0.5 survMisc_0.5.5
## [75] purrr_0.3.4 KEGGREST_1.34.0
## [77] formatR_1.11 xml2_1.3.3
## [79] biomaRt_2.50.2 compiler_4.1.2
## [81] filelock_1.0.2 curl_4.3.2
## [83] png_0.1-7 ggsignif_0.6.3
## [85] tibble_3.1.6 bslib_0.3.1
## [87] stringi_1.7.6 highr_0.9
## [89] futile.logger_1.4.3 GenomicFeatures_1.46.4
## [91] lattice_0.20-45 Matrix_1.4-0
## [93] markdown_1.1 KMsurv_0.1-5
## [95] RTCGAToolbox_2.24.0 vctrs_0.3.8
## [97] pillar_1.6.5 lifecycle_1.0.1
## [99] BiocManager_1.30.16 jquerylib_0.1.4
## [101] data.table_1.14.2 bitops_1.0-7
## [103] rtracklayer_1.54.0 R6_2.5.1
## [105] BiocIO_1.4.0 bookdown_0.24
## [107] gridExtra_2.3 codetools_0.2-18
## [109] lambda.r_1.2.4 assertthat_0.2.1
## [111] rjson_0.2.21 withr_2.4.3
## [113] GenomicAlignments_1.30.0 Rsamtools_2.10.0
## [115] GenomeInfoDbData_1.2.7 parallel_4.1.2
## [117] hms_1.1.1 ggtext_0.1.1
## [119] grid_4.1.2 tidyr_1.1.4
## [121] rmarkdown_2.11 carData_3.0-5
## [123] restfulr_0.0.13