Contents

Installation

library(cBioPortalData)
library(AnVIL)

Introduction

This vignette lays out the two main user-facing functions for downloading and representing data from the cBioPortal API. cBioDataPack makes use of the legacy distribution data method in cBioPortal (via tarballs). cBioPortalData allows for a more flexibile approach to obtaining data based on several available parameters including available molecular profiles.

Two main interfaces

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
cBioDataPack("laml_tcga", ask = FALSE)
## 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] RNA_Seq_expression_median: SummarizedExperiment with 19720 rows and 179 columns
##  [3] RNA_Seq_mRNA_median_all_sample_Zscores: SummarizedExperiment with 19720 rows and 179 columns
##  [4] RNA_Seq_v2_expression_median: SummarizedExperiment with 20531 rows and 173 columns
##  [5] RNA_Seq_v2_mRNA_median_Zscores: SummarizedExperiment with 20440 rows and 173 columns
##  [6] RNA_Seq_v2_mRNA_median_all_sample_Zscores: SummarizedExperiment with 20531 rows and 173 columns
##  [7] cna_hg19.seg: RaggedExperiment with 13571 rows and 191 columns
##  [8] linear_CNA: SummarizedExperiment with 24776 rows and 191 columns
##  [9] methylation_hm27: SummarizedExperiment with 10919 rows and 194 columns
##  [10] methylation_hm450: SummarizedExperiment with 10919 rows and 194 columns
##  [11] mutations_extended: RaggedExperiment with 2584 rows and 197 columns
##  [12] 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 all data to files

cBioPortalData: 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.

cbio <- cBioPortal()
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 all data to files

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/")

sessionInfo

sessionInfo()
## R version 4.0.3 (2020-10-10)
## 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_2.2.8        MultiAssayExperiment_1.16.0
##  [3] SummarizedExperiment_1.20.0 Biobase_2.50.0             
##  [5] GenomicRanges_1.42.0        GenomeInfoDb_1.26.2        
##  [7] IRanges_2.24.1              S4Vectors_0.28.1           
##  [9] BiocGenerics_0.36.0         MatrixGenerics_1.2.1       
## [11] matrixStats_0.58.0          AnVIL_1.2.0                
## [13] dplyr_1.0.4                 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_4.0.3              
##  [7] R6_2.5.0                  DBI_1.1.1                
##  [9] withr_2.4.1               tidyselect_1.1.0         
## [11] prettyunits_1.1.1         TCGAutils_1.10.0         
## [13] bit_4.0.4                 curl_4.3                 
## [15] compiler_4.0.3            cli_2.3.0                
## [17] rvest_0.3.6               formatR_1.7              
## [19] xml2_1.3.2                DelayedArray_0.16.1      
## [21] rtracklayer_1.50.0        bookdown_0.21            
## [23] readr_1.4.0               askpass_1.1              
## [25] rappdirs_0.3.3            rapiclient_0.1.3         
## [27] RCircos_1.2.1             stringr_1.4.0            
## [29] digest_0.6.27             Rsamtools_2.6.0          
## [31] rmarkdown_2.6             XVector_0.30.0           
## [33] pkgconfig_2.0.3           htmltools_0.5.1.1        
## [35] dbplyr_2.1.0              fastmap_1.1.0            
## [37] limma_3.46.0              rlang_0.4.10             
## [39] rstudioapi_0.13           RSQLite_2.2.3            
## [41] generics_0.1.0            jsonlite_1.7.2           
## [43] BiocParallel_1.24.1       RCurl_1.98-1.2           
## [45] magrittr_2.0.1            GenomeInfoDbData_1.2.4   
## [47] futile.logger_1.4.3       Matrix_1.3-2             
## [49] Rcpp_1.0.6                lifecycle_0.2.0          
## [51] stringi_1.5.3             yaml_2.2.1               
## [53] RaggedExperiment_1.14.1   RJSONIO_1.3-1.4          
## [55] zlibbioc_1.36.0           BiocFileCache_1.14.0     
## [57] grid_4.0.3                blob_1.2.1               
## [59] crayon_1.4.0              lattice_0.20-41          
## [61] Biostrings_2.58.0         splines_4.0.3            
## [63] GenomicFeatures_1.42.1    hms_1.0.0                
## [65] ps_1.5.0                  knitr_1.31               
## [67] pillar_1.4.7              codetools_0.2-18         
## [69] biomaRt_2.46.2            futile.options_1.0.1     
## [71] XML_3.99-0.5              glue_1.4.2               
## [73] evaluate_0.14             lambda.r_1.2.4           
## [75] data.table_1.13.6         BiocManager_1.30.10      
## [77] vctrs_0.3.6               tidyr_1.1.2              
## [79] openssl_1.4.3             purrr_0.3.4              
## [81] assertthat_0.2.1          cachem_1.0.3             
## [83] xfun_0.20                 survival_3.2-7           
## [85] tibble_3.0.6              RTCGAToolbox_2.20.0      
## [87] GenomicAlignments_1.26.0  AnnotationDbi_1.52.0     
## [89] memoise_2.0.0             ellipsis_0.3.1