library(cBioPortalData)
library(AnVIL)
This document serves as a reporting tool for errors that occur when running our utility functions on the cBioPortal datasets.
cBioPortalData()
)Typically, the number of errors encountered via the API are low. There are only a handful of packages that error when we apply the utility functions to provide a MultiAssayExperiment data representation.
First, we load the error Rda
dataset.
api_errs <- system.file(
"extdata", "api", "err_api_info.rda",
package = "cBioPortalData", mustWork = TRUE
)
load(api_errs)
We can now inspect the contents of the data:
class(err_api_info)
## [1] "list"
length(err_api_info)
## [1] 6
lengths(err_api_info)
## Barcodes must start with 'TCGA'
## 2
## group length is 0 but data length > 0
## 1
## Frequency of NA values higher than the cutoff tolerance
## 2
## Inconsistent build numbers found
## 33
## `n` must be a single number, not an integer `NA`.
## 1
## Argument 1 must be a data frame or a named atomic vector.
## 1
There were about 6 unique errors during the last build run.
names(err_api_info)
## [1] "Barcodes must start with 'TCGA'"
## [2] "group length is 0 but data length > 0"
## [3] "Frequency of NA values higher than the cutoff tolerance"
## [4] "Inconsistent build numbers found"
## [5] "`n` must be a single number, not an integer `NA`."
## [6] "Argument 1 must be a data frame or a named atomic vector."
The most common error was Inconsistent build numbers found
. This is
due to annotations from different build numbers that were not able to
be resolved.
To see what datasets (cancer_study_id
s) have that error we can use:
err_api_info[['Inconsistent build numbers found']]
## [1] "msk_ch_2020" "msk_access_2021"
## [3] "mixed_msk_tcga_2021" "mixed_impact_subset_2022"
## [5] "pan_origimed_2020" "prad_msk_stopsack_2021"
## [7] "pancan_pcawg_2020" "prad_pik3r1_msk_2021"
## [9] "skcm_tcga" "stad_tcga"
## [11] "stad_tcga_pub" "skcm_tcga_pan_can_atlas_2018"
## [13] "stad_tcga_pan_can_atlas_2018" "stes_tcga_pub"
## [15] "summit_2018" "cfdna_msk_2019"
## [17] "blca_bcan_hcrn_2022" "nsclc_ctdx_msk_2022"
## [19] "thyroid_mskcc_2016" "skcm_mskcc_2014"
## [21] "tmb_mskcc_2018" "rectal_msk_2019"
## [23] "skcm_tcga_pub_2015" "msk_spectrum_tme_2022"
## [25] "ucec_ccr_cfdna_msk_2022" "paired_bladder_2022"
## [27] "mtnn_msk_2022" "pog570_bcgsc_2020"
## [29] "sarcoma_msk_2023" "bowel_colitis_msk_2022"
## [31] "luad_mskcc_2023_met_organotropism" "coad_silu_2022"
## [33] "paac_msk_jco_2023"
We can also have a look at the entirety of the dataset.
err_api_info
## $`Barcodes must start with 'TCGA'`
## [1] "blca_msk_tcga_2020" "nsclc_tcga_broad_2016"
##
## $`group length is 0 but data length > 0`
## [1] "glioma_msk_2018"
##
## $`Frequency of NA values higher than the cutoff tolerance`
## [1] "mixed_selpercatinib_2020" "ucec_ccr_msk_2022"
##
## $`Inconsistent build numbers found`
## [1] "msk_ch_2020" "msk_access_2021"
## [3] "mixed_msk_tcga_2021" "mixed_impact_subset_2022"
## [5] "pan_origimed_2020" "prad_msk_stopsack_2021"
## [7] "pancan_pcawg_2020" "prad_pik3r1_msk_2021"
## [9] "skcm_tcga" "stad_tcga"
## [11] "stad_tcga_pub" "skcm_tcga_pan_can_atlas_2018"
## [13] "stad_tcga_pan_can_atlas_2018" "stes_tcga_pub"
## [15] "summit_2018" "cfdna_msk_2019"
## [17] "blca_bcan_hcrn_2022" "nsclc_ctdx_msk_2022"
## [19] "thyroid_mskcc_2016" "skcm_mskcc_2014"
## [21] "tmb_mskcc_2018" "rectal_msk_2019"
## [23] "skcm_tcga_pub_2015" "msk_spectrum_tme_2022"
## [25] "ucec_ccr_cfdna_msk_2022" "paired_bladder_2022"
## [27] "mtnn_msk_2022" "pog570_bcgsc_2020"
## [29] "sarcoma_msk_2023" "bowel_colitis_msk_2022"
## [31] "luad_mskcc_2023_met_organotropism" "coad_silu_2022"
## [33] "paac_msk_jco_2023"
##
## $``n` must be a single number, not an integer `NA`.`
## [1] "msk_met_2021"
##
## $`Argument 1 must be a data frame or a named atomic vector.`
## [1] "makeanimpact_ccr_2023"
cBioDataPack()
Now let’s look at the errors in the packaged datasets that are used for
cBioDataPack
:
pack_errs <- system.file(
"extdata", "pack", "err_pack_info.rda",
package = "cBioPortalData", mustWork = TRUE
)
load(pack_errs)
We can do the same for this data:
length(err_pack_info)
## [1] 5
lengths(err_pack_info)
## more columns than column names
## 9
## Frequency of NA values higher than the cutoff tolerance
## 5
## non-character argument
## 2
## invalid class "ExperimentList" object: \n Non-unique names provided
## 2
## 'wget' call had nonzero exit status
## 11
We can get a list of all the errors present:
names(err_pack_info)
## [1] "more columns than column names"
## [2] "Frequency of NA values higher than the cutoff tolerance"
## [3] "non-character argument"
## [4] "invalid class \"ExperimentList\" object: \n Non-unique names provided"
## [5] "'wget' call had nonzero exit status"
And finally the full list of errors:
err_pack_info
## $`more columns than column names`
## [1] "ccrcc_utokyo_2013" "coadread_tcga_pan_can_atlas_2018"
## [3] "gbm_cptac_2021" "ov_tcga_pan_can_atlas_2018"
## [5] "pan_origimed_2020" "sarc_tcga_pan_can_atlas_2018"
## [7] "luad_mskimpact_2021" "mbl_dkfz_2017"
## [9] "brca_tcga_pan_can_atlas_2018"
##
## $`Frequency of NA values higher than the cutoff tolerance`
## [1] "ihch_mskcc_2020" "ihch_msk_2021"
## [3] "mixed_selpercatinib_2020" "mixed_msk_tcga_2021"
## [5] "ucec_ccr_msk_2022"
##
## $`non-character argument`
## [1] "mbn_mdacc_2013" "pcpg_tcga_pub"
##
## $`invalid class "ExperimentList" object: \n Non-unique names provided`
## [1] "stad_tcga_pub" "mpnst_mskcc"
##
## $`'wget' call had nonzero exit status`
## [1] "makeanimpact_ccr_2023" "prad_organoids_msk_2022"
## [3] "mtnn_msk_2022" "sarcoma_msk_2023"
## [5] "bowel_colitis_msk_2022" "bladder_mskcc_2022"
## [7] "paac_msk_jco_2023" "nbl_msk_2023"
## [9] "rms_msk_2023" "gist_msk_2023"
## [11] "egc_trap_ccr_msk_2023"
sessionInfo()
## R Under development (unstable) (2024-01-16 r85808)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 22.04.3 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.19-bioc/R/lib/libRblas.so
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_GB 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
##
## time zone: America/New_York
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] survminer_0.4.9 ggpubr_0.6.0
## [3] ggplot2_3.4.4 survival_3.5-7
## [5] cBioPortalData_2.15.3 MultiAssayExperiment_1.29.1
## [7] SummarizedExperiment_1.33.3 Biobase_2.63.0
## [9] GenomicRanges_1.55.2 GenomeInfoDb_1.39.5
## [11] IRanges_2.37.1 S4Vectors_0.41.3
## [13] BiocGenerics_0.49.1 MatrixGenerics_1.15.0
## [15] matrixStats_1.2.0 AnVIL_1.15.4
## [17] dplyr_1.1.4 BiocStyle_2.31.0
##
## loaded via a namespace (and not attached):
## [1] jsonlite_1.8.8 magrittr_2.0.3
## [3] magick_2.8.2 GenomicFeatures_1.55.3
## [5] farver_2.1.1 rmarkdown_2.25
## [7] BiocIO_1.13.0 zlibbioc_1.49.0
## [9] vctrs_0.6.5 memoise_2.0.1
## [11] Rsamtools_2.19.3 RCurl_1.98-1.14
## [13] rstatix_0.7.2 BiocBaseUtils_1.5.0
## [15] htmltools_0.5.7 S4Arrays_1.3.3
## [17] progress_1.2.3 lambda.r_1.2.4
## [19] curl_5.2.0 broom_1.0.5
## [21] SparseArray_1.3.3 sass_0.4.8
## [23] bslib_0.6.1 htmlwidgets_1.6.4
## [25] httr2_1.0.0 zoo_1.8-12
## [27] futile.options_1.0.1 cachem_1.0.8
## [29] commonmark_1.9.1 GenomicAlignments_1.39.2
## [31] mime_0.12 lifecycle_1.0.4
## [33] pkgconfig_2.0.3 Matrix_1.6-5
## [35] R6_2.5.1 fastmap_1.1.1
## [37] GenomeInfoDbData_1.2.11 shiny_1.8.0
## [39] digest_0.6.34 colorspace_2.1-0
## [41] RaggedExperiment_1.27.1 AnnotationDbi_1.65.2
## [43] RSQLite_2.3.5 labeling_0.4.3
## [45] filelock_1.0.3 RTCGAToolbox_2.33.2
## [47] km.ci_0.5-6 fansi_1.0.6
## [49] RJSONIO_1.3-1.9 httr_1.4.7
## [51] abind_1.4-5 compiler_4.4.0
## [53] bit64_4.0.5 withr_3.0.0
## [55] backports_1.4.1 BiocParallel_1.37.0
## [57] carData_3.0-5 DBI_1.2.1
## [59] highr_0.10 ggsignif_0.6.4
## [61] biomaRt_2.59.1 rappdirs_0.3.3
## [63] DelayedArray_0.29.1 rjson_0.2.21
## [65] tools_4.4.0 httpuv_1.6.14
## [67] glue_1.7.0 restfulr_0.0.15
## [69] promises_1.2.1 gridtext_0.1.5
## [71] grid_4.4.0 generics_0.1.3
## [73] gtable_0.3.4 KMsurv_0.1-5
## [75] tzdb_0.4.0 tidyr_1.3.1
## [77] data.table_1.15.0 hms_1.1.3
## [79] car_3.1-2 xml2_1.3.6
## [81] utf8_1.2.4 XVector_0.43.1
## [83] markdown_1.12 pillar_1.9.0
## [85] stringr_1.5.1 later_1.3.2
## [87] splines_4.4.0 ggtext_0.1.2
## [89] BiocFileCache_2.11.1 lattice_0.22-5
## [91] rtracklayer_1.63.0 bit_4.0.5
## [93] tidyselect_1.2.0 Biostrings_2.71.2
## [95] miniUI_0.1.1.1 knitr_1.45
## [97] gridExtra_2.3 bookdown_0.37
## [99] futile.logger_1.4.3 xfun_0.41
## [101] DT_0.31 stringi_1.8.3
## [103] yaml_2.3.8 evaluate_0.23
## [105] codetools_0.2-19 tibble_3.2.1
## [107] BiocManager_1.30.22 cli_3.6.2
## [109] xtable_1.8-4 munsell_0.5.0
## [111] jquerylib_0.1.4 survMisc_0.5.6
## [113] Rcpp_1.0.12 GenomicDataCommons_1.27.1
## [115] dbplyr_2.4.0 png_0.1-8
## [117] XML_3.99-0.16.1 rapiclient_0.1.3
## [119] parallel_4.4.0 TCGAutils_1.23.3
## [121] ellipsis_0.3.2 readr_2.1.5
## [123] blob_1.2.4 prettyunits_1.2.0
## [125] bitops_1.0-7 scales_1.3.0
## [127] purrr_1.0.2 crayon_1.5.2
## [129] rlang_1.1.3 KEGGREST_1.43.0
## [131] rvest_1.0.3 formatR_1.14