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 4.0.5 (2021-03-31)
## 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] parallel stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] cBioPortalData_2.2.11 MultiAssayExperiment_1.16.0
## [3] SummarizedExperiment_1.20.0 Biobase_2.50.0
## [5] GenomicRanges_1.42.0 GenomeInfoDb_1.26.7
## [7] IRanges_2.24.1 S4Vectors_0.28.1
## [9] BiocGenerics_0.36.1 MatrixGenerics_1.2.1
## [11] matrixStats_0.58.0 AnVIL_1.2.0
## [13] dplyr_1.0.5 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_4.0.5
## [7] bslib_0.2.4 utf8_1.2.1
## [9] R6_2.5.0 DBI_1.1.1
## [11] withr_2.4.2 tidyselect_1.1.0
## [13] prettyunits_1.1.1 TCGAutils_1.10.1
## [15] bit_4.0.4 curl_4.3
## [17] compiler_4.0.5 cli_2.4.0
## [19] rvest_1.0.0 formatR_1.9
## [21] xml2_1.3.2 DelayedArray_0.16.3
## [23] rtracklayer_1.50.0 bookdown_0.22
## [25] sass_0.3.1 readr_1.4.0
## [27] askpass_1.1 rappdirs_0.3.3
## [29] rapiclient_0.1.3 RCircos_1.2.1
## [31] Rsamtools_2.6.0 stringr_1.4.0
## [33] digest_0.6.27 rmarkdown_2.7
## [35] XVector_0.30.0 pkgconfig_2.0.3
## [37] htmltools_0.5.1.1 dbplyr_2.1.1
## [39] fastmap_1.1.0 limma_3.46.0
## [41] rlang_0.4.10 rstudioapi_0.13
## [43] RSQLite_2.2.7 jquerylib_0.1.3
## [45] generics_0.1.0 jsonlite_1.7.2
## [47] BiocParallel_1.24.1 RCurl_1.98-1.3
## [49] magrittr_2.0.1 GenomeInfoDbData_1.2.4
## [51] futile.logger_1.4.3 Matrix_1.3-2
## [53] Rcpp_1.0.6 fansi_0.4.2
## [55] lifecycle_1.0.0 stringi_1.5.3
## [57] yaml_2.2.1 RaggedExperiment_1.14.2
## [59] RJSONIO_1.3-1.4 zlibbioc_1.36.0
## [61] BiocFileCache_1.14.0 grid_4.0.5
## [63] blob_1.2.1 crayon_1.4.1
## [65] lattice_0.20-41 Biostrings_2.58.0
## [67] splines_4.0.5 GenomicFeatures_1.42.3
## [69] hms_1.0.0 ps_1.6.0
## [71] knitr_1.32.8 pillar_1.6.0
## [73] codetools_0.2-18 biomaRt_2.46.3
## [75] futile.options_1.0.1 XML_3.99-0.6
## [77] glue_1.4.2 evaluate_0.14
## [79] lambda.r_1.2.4 data.table_1.14.0
## [81] BiocManager_1.30.12 vctrs_0.3.7
## [83] tidyr_1.1.3 openssl_1.4.3
## [85] purrr_0.3.4 assertthat_0.2.1
## [87] cachem_1.0.4 xfun_0.22
## [89] survival_3.2-10 tibble_3.1.1
## [91] RTCGAToolbox_2.20.0 GenomicAlignments_1.26.0
## [93] AnnotationDbi_1.52.0 memoise_2.0.0
## [95] ellipsis_0.3.1