library(cBioPortalData)
library(AnVIL)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.
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
## `$`, `[`, `[[` - extract colData columns, subset, or experiment
## *Format() - convert into a long or wide DataFrame
## assays() - convert ExperimentList to a SimpleList of matrices
## exportClass() - save all data to filesThis function provides a more flexible and granular way to request a MultiAssayExperiment object from a study ID, molecular profile, gene panel, sample list.
cbio <- cBioPortal()
acc <- cBioPortalData(api = cbio, by = "hugoGeneSymbol", studyId = "acc_tcga",
genePanelId = "IMPACT341",
molecularProfileIds = c("acc_tcga_rppa", "acc_tcga_linear_CNA")
)## harmonizing input:
## removing 1 colData rownames not in sampleMap 'primary'acc## A MultiAssayExperiment object of 2 listed
## experiments with user-defined names and respective classes.
## Containing an ExperimentList class object of length 2:
## [1] acc_tcga_rppa: SummarizedExperiment with 57 rows and 46 columns
## [2] acc_tcga_linear_CNA: SummarizedExperiment with 339 rows and 90 columns
## Functionality:
## experiments() - obtain the ExperimentList instance
## colData() - the primary/phenotype DataFrame
## sampleMap() - the sample coordination DataFrame
## `$`, `[`, `[[` - 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 filesIn 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")For users who wish to clear the entire cBioPortalData cache, it is
recommended that they use:
unlink("~/.cache/cBioPortalData/")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_2.2.0 MultiAssayExperiment_1.16.0
## [3] SummarizedExperiment_1.20.0 Biobase_2.50.0
## [5] GenomicRanges_1.42.0 GenomeInfoDb_1.26.0
## [7] IRanges_2.24.0 S4Vectors_0.28.0
## [9] BiocGenerics_0.36.0 MatrixGenerics_1.2.0
## [11] matrixStats_0.57.0 AnVIL_1.2.0
## [13] dplyr_1.0.2 BiocStyle_2.18.0
##
## 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_2.4.1 DBI_1.1.0
## [9] tidyselect_1.1.0 prettyunits_1.1.1
## [11] TCGAutils_1.10.0 bit_4.0.4
## [13] curl_4.3 compiler_4.0.3
## [15] cli_2.1.0 rvest_0.3.6
## [17] formatR_1.7 xml2_1.3.2
## [19] DelayedArray_0.16.0 rtracklayer_1.50.0
## [21] bookdown_0.21 readr_1.4.0
## [23] askpass_1.1 rappdirs_0.3.1
## [25] rapiclient_0.1.3 RCircos_1.2.1
## [27] stringr_1.4.0 digest_0.6.27
## [29] Rsamtools_2.6.0 rmarkdown_2.5
## [31] XVector_0.30.0 pkgconfig_2.0.3
## [33] htmltools_0.5.0 dbplyr_1.4.4
## [35] limma_3.46.0 rlang_0.4.8
## [37] rstudioapi_0.11 RSQLite_2.2.1
## [39] generics_0.0.2 jsonlite_1.7.1
## [41] BiocParallel_1.24.0 RCurl_1.98-1.2
## [43] magrittr_1.5 GenomeInfoDbData_1.2.4
## [45] futile.logger_1.4.3 Matrix_1.2-18
## [47] fansi_0.4.1 Rcpp_1.0.5
## [49] lifecycle_0.2.0 stringi_1.5.3
## [51] yaml_2.2.1 RaggedExperiment_1.14.0
## [53] RJSONIO_1.3-1.4 zlibbioc_1.36.0
## [55] BiocFileCache_1.14.0 grid_4.0.3
## [57] blob_1.2.1 crayon_1.3.4
## [59] lattice_0.20-41 Biostrings_2.58.0
## [61] splines_4.0.3 GenomicFeatures_1.42.0
## [63] hms_0.5.3 ps_1.4.0
## [65] knitr_1.30 pillar_1.4.6
## [67] codetools_0.2-16 biomaRt_2.46.0
## [69] futile.options_1.0.1 XML_3.99-0.5
## [71] glue_1.4.2 evaluate_0.14
## [73] lambda.r_1.2.4 data.table_1.13.2
## [75] BiocManager_1.30.10 vctrs_0.3.4
## [77] tidyr_1.1.2 openssl_1.4.3
## [79] purrr_0.3.4 assertthat_0.2.1
## [81] xfun_0.18 survival_3.2-7
## [83] tibble_3.0.4 RTCGAToolbox_2.20.0
## [85] GenomicAlignments_1.26.0 AnnotationDbi_1.52.0
## [87] memoise_1.1.0 ellipsis_0.3.1