library(cBioPortalData)
library(AnVIL)
library(jsonlite)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.json",
package = "cBioPortalData", mustWork = TRUE
)
err_api_info <- fromJSON(api_errs)We can now inspect the contents of the data:
class(err_api_info)## [1] "list"length(err_api_info)## [1] 9lengths(err_api_info)## Inconsistent build numbers found
## 160
## subscript out of bounds
## 13
## Can't select columns that don't exist.\n✖ Column `value` doesn't exist.
## 1
## Only two build types at a time can be used
## 4
## Not Found (HTTP 404).
## 1
## Barcodes must start with 'TCGA'
## 1
## Frequency of NA values higher than the cutoff tolerance
## 1
## `n` must be a single number, not an integer `NA`.
## 3
## each range must have an end that is greater or equal to its start minus one
## 1There were about 9 unique errors during the last build run.
names(err_api_info)## [1] "Inconsistent build numbers found"
## [2] "subscript out of bounds"
## [3] "Can't select columns that don't exist.\n✖ Column `value` doesn't exist."
## [4] "Only two build types at a time can be used"
## [5] "Not Found (HTTP 404)."
## [6] "Barcodes must start with 'TCGA'"
## [7] "Frequency of NA values higher than the cutoff tolerance"
## [8] "`n` must be a single number, not an integer `NA`."
## [9] "each range must have an end that is greater or equal to its start minus one"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] "acc_tcga_gdc" "bladder_aubmc_2026"
## [3] "bladder_msk_2024" "blca_iatlas_imvigor210_2017"
## [5] "blca_msk_tcga_2020" "blca_tcga_gdc"
## [7] "bll_target_gdc" "brain_cptac_gdc"
## [9] "brca_aurora_2023" "brca_mapk_hp_msk_2021"
## [11] "brca_metabric" "brca_tcga_gdc"
## [13] "breast_cptac_gdc" "breast_msk_2018"
## [15] "breast_msk_2025" "ccle_broad_2019"
## [17] "ccle_broad_2025" "ccrcc_iatlas_choueiri_2016"
## [19] "ccrcc_tcga_gdc" "cellline_ccle_broad"
## [21] "cesc_tcga" "cesc_tcga_gdc"
## [23] "cesc_tcga_pan_can_atlas_2018" "coad_cptac_gdc"
## [25] "coad_silu_2022" "coad_tcga_gdc"
## [27] "coadread_dfci_2016" "coadread_tcga_pan_can_atlas_2018"
## [29] "crc_eo_2020" "crc_hta11_htan_2021"
## [31] "crc_msk_2017" "crc_orion_2024"
## [33] "crc_sysucc_2022" "cscc_ranson_2022"
## [35] "desm_broad_2015" "difg_tcga_gdc"
## [37] "dlbcl_duke_2017" "dlbclnos_tcga_gdc"
## [39] "esca_tcga_gdc" "gbm_tcga_gdc"
## [41] "hcc_tcga_gdc" "heme_msk_impact_2022"
## [43] "hgsoc_tcga_gdc" "hnsc_a5consortium_2025"
## [45] "hnsc_tcga_gdc" "lgg_tcga"
## [47] "lgg_tcga_pan_can_atlas_2018" "lgggbm_tcga_pub"
## [49] "lgsoc_mapk_msk_2022" "lms_msk_2026"
## [51] "luad_broad" "luad_cptac_2020"
## [53] "luad_cptac_gdc" "luad_msk_npjpo_2021"
## [55] "luad_tcga_gdc" "luad_tcga_pan_can_atlas_2018"
## [57] "lung_msk_2017" "lusc_cptac_2021"
## [59] "lusc_cptac_gdc" "lusc_tcga_gdc"
## [61] "lusc_tcga_pan_can_atlas_2018" "mbn_msk_2024"
## [63] "mbn_sfu_2023" "mel_dfci_2019"
## [65] "mel_iatlas_hugo_ucla_2016" "mel_iatlas_liu_2019"
## [67] "mel_iatlas_riaz_nivolumab_2017" "mel_mskimpact_2020"
## [69] "metastatic_solid_tumors_mich_2017" "mixed_allen_2018"
## [71] "mm_broad" "mnet_tcga_gdc"
## [73] "msk_ch_2020" "msk_ch_2023"
## [75] "msk_impact_2017" "mycf_msk_2026"
## [77] "nbl_target_gdc" "nfib_ctf_biobank_2025"
## [79] "nsclc_ctdx_msk_2022" "nsclc_mskcc_2018"
## [81] "nsclc_tcga_broad_2016" "nsgct_tcga_gdc"
## [83] "nst_nfosi_ntap" "ohnca_cptac_gdc"
## [85] "ovary_cptac_gdc" "paad_iatlas_prince_2022"
## [87] "paad_tcga_gdc" "paired_bladder_2022"
## [89] "pan_origimed_2020" "pan_pdo_wcm_2026"
## [91] "pancan_hcmi_2025" "pancan_mappyacts_2022"
## [93] "pancan_mimsi_msk_2024" "pancan_pcawg_2020"
## [95] "pancan_pdmr_2025" "pancreas_cptac_gdc"
## [97] "pcnsl_msk_2024" "pcpg_tcga"
## [99] "pcpg_tcga_pan_can_atlas_2018" "pcpg_tcga_pub"
## [101] "pdac_msk_2024" "plmeso_tcga_gdc"
## [103] "pog570_bcgsc_2020" "pptc_2019"
## [105] "prad_cdk12_mskcc_2020" "prad_msk_mdanderson_2023"
## [107] "prad_msk_stopsack_2021" "prad_mskcc_2017"
## [109] "prad_p1000" "prad_pik3r1_msk_2021"
## [111] "prad_tcga" "prad_tcga_gdc"
## [113] "prad_tcga_pan_can_atlas_2018" "prad_tcga_pub"
## [115] "prcc_tcga_gdc" "prostate_msk_2024"
## [117] "prostate_pcbm_swiss_2019" "ptad_msk_2024"
## [119] "ptad_msk_2026" "rbl_cfdna_msk_2020"
## [121] "rcc_cptac_gdc" "rcc_iatlas_immotion150_2018"
## [123] "read_tcga_gdc" "rectal_msk_2019"
## [125] "rectal_radiation_msk_2024" "sarcoma_msk_2022"
## [127] "sarcoma_msk_2023" "schw_ctf_synodos_2025"
## [129] "sft_sysucc_2023" "skcm_mskcc_2014"
## [131] "skcm_tcga" "skcm_tcga_gdc"
## [133] "skcm_tcga_pan_can_atlas_2018" "skcm_tcga_pub_2015"
## [135] "skcm_yale" "soft_tissue_tcga_gdc"
## [137] "stad_tcga" "stad_tcga_gdc"
## [139] "stad_tcga_pan_can_atlas_2018" "stad_tcga_pub"
## [141] "stes_tcga_pub" "summit_2018"
## [143] "thpa_tcga_gdc" "thym_tcga_gdc"
## [145] "thyroid_gatci_2024" "tmb_mskcc_2018"
## [147] "ucec_ccr_msk_2022" "ucec_msk_2024"
## [149] "ucec_msk_dacruzpaula_2025" "ucec_tcga"
## [151] "ucec_tcga_gdc" "ucec_tcga_pan_can_atlas_2018"
## [153] "ucec_tcga_pub" "ucs_msk_2024"
## [155] "ucs_tcga_gdc" "uec_cptac_gdc"
## [157] "um_tcga_gdc" "utuc_cornell_baylor_mdacc_2019"
## [159] "utuc_msk_2019" "utuc_pdx_msk_2019"We can also have a look at the entirety of the dataset.
err_api_info## $`Inconsistent build numbers found`
## [1] "acc_tcga_gdc" "bladder_aubmc_2026"
## [3] "bladder_msk_2024" "blca_iatlas_imvigor210_2017"
## [5] "blca_msk_tcga_2020" "blca_tcga_gdc"
## [7] "bll_target_gdc" "brain_cptac_gdc"
## [9] "brca_aurora_2023" "brca_mapk_hp_msk_2021"
## [11] "brca_metabric" "brca_tcga_gdc"
## [13] "breast_cptac_gdc" "breast_msk_2018"
## [15] "breast_msk_2025" "ccle_broad_2019"
## [17] "ccle_broad_2025" "ccrcc_iatlas_choueiri_2016"
## [19] "ccrcc_tcga_gdc" "cellline_ccle_broad"
## [21] "cesc_tcga" "cesc_tcga_gdc"
## [23] "cesc_tcga_pan_can_atlas_2018" "coad_cptac_gdc"
## [25] "coad_silu_2022" "coad_tcga_gdc"
## [27] "coadread_dfci_2016" "coadread_tcga_pan_can_atlas_2018"
## [29] "crc_eo_2020" "crc_hta11_htan_2021"
## [31] "crc_msk_2017" "crc_orion_2024"
## [33] "crc_sysucc_2022" "cscc_ranson_2022"
## [35] "desm_broad_2015" "difg_tcga_gdc"
## [37] "dlbcl_duke_2017" "dlbclnos_tcga_gdc"
## [39] "esca_tcga_gdc" "gbm_tcga_gdc"
## [41] "hcc_tcga_gdc" "heme_msk_impact_2022"
## [43] "hgsoc_tcga_gdc" "hnsc_a5consortium_2025"
## [45] "hnsc_tcga_gdc" "lgg_tcga"
## [47] "lgg_tcga_pan_can_atlas_2018" "lgggbm_tcga_pub"
## [49] "lgsoc_mapk_msk_2022" "lms_msk_2026"
## [51] "luad_broad" "luad_cptac_2020"
## [53] "luad_cptac_gdc" "luad_msk_npjpo_2021"
## [55] "luad_tcga_gdc" "luad_tcga_pan_can_atlas_2018"
## [57] "lung_msk_2017" "lusc_cptac_2021"
## [59] "lusc_cptac_gdc" "lusc_tcga_gdc"
## [61] "lusc_tcga_pan_can_atlas_2018" "mbn_msk_2024"
## [63] "mbn_sfu_2023" "mel_dfci_2019"
## [65] "mel_iatlas_hugo_ucla_2016" "mel_iatlas_liu_2019"
## [67] "mel_iatlas_riaz_nivolumab_2017" "mel_mskimpact_2020"
## [69] "metastatic_solid_tumors_mich_2017" "mixed_allen_2018"
## [71] "mm_broad" "mnet_tcga_gdc"
## [73] "msk_ch_2020" "msk_ch_2023"
## [75] "msk_impact_2017" "mycf_msk_2026"
## [77] "nbl_target_gdc" "nfib_ctf_biobank_2025"
## [79] "nsclc_ctdx_msk_2022" "nsclc_mskcc_2018"
## [81] "nsclc_tcga_broad_2016" "nsgct_tcga_gdc"
## [83] "nst_nfosi_ntap" "ohnca_cptac_gdc"
## [85] "ovary_cptac_gdc" "paad_iatlas_prince_2022"
## [87] "paad_tcga_gdc" "paired_bladder_2022"
## [89] "pan_origimed_2020" "pan_pdo_wcm_2026"
## [91] "pancan_hcmi_2025" "pancan_mappyacts_2022"
## [93] "pancan_mimsi_msk_2024" "pancan_pcawg_2020"
## [95] "pancan_pdmr_2025" "pancreas_cptac_gdc"
## [97] "pcnsl_msk_2024" "pcpg_tcga"
## [99] "pcpg_tcga_pan_can_atlas_2018" "pcpg_tcga_pub"
## [101] "pdac_msk_2024" "plmeso_tcga_gdc"
## [103] "pog570_bcgsc_2020" "pptc_2019"
## [105] "prad_cdk12_mskcc_2020" "prad_msk_mdanderson_2023"
## [107] "prad_msk_stopsack_2021" "prad_mskcc_2017"
## [109] "prad_p1000" "prad_pik3r1_msk_2021"
## [111] "prad_tcga" "prad_tcga_gdc"
## [113] "prad_tcga_pan_can_atlas_2018" "prad_tcga_pub"
## [115] "prcc_tcga_gdc" "prostate_msk_2024"
## [117] "prostate_pcbm_swiss_2019" "ptad_msk_2024"
## [119] "ptad_msk_2026" "rbl_cfdna_msk_2020"
## [121] "rcc_cptac_gdc" "rcc_iatlas_immotion150_2018"
## [123] "read_tcga_gdc" "rectal_msk_2019"
## [125] "rectal_radiation_msk_2024" "sarcoma_msk_2022"
## [127] "sarcoma_msk_2023" "schw_ctf_synodos_2025"
## [129] "sft_sysucc_2023" "skcm_mskcc_2014"
## [131] "skcm_tcga" "skcm_tcga_gdc"
## [133] "skcm_tcga_pan_can_atlas_2018" "skcm_tcga_pub_2015"
## [135] "skcm_yale" "soft_tissue_tcga_gdc"
## [137] "stad_tcga" "stad_tcga_gdc"
## [139] "stad_tcga_pan_can_atlas_2018" "stad_tcga_pub"
## [141] "stes_tcga_pub" "summit_2018"
## [143] "thpa_tcga_gdc" "thym_tcga_gdc"
## [145] "thyroid_gatci_2024" "tmb_mskcc_2018"
## [147] "ucec_ccr_msk_2022" "ucec_msk_2024"
## [149] "ucec_msk_dacruzpaula_2025" "ucec_tcga"
## [151] "ucec_tcga_gdc" "ucec_tcga_pan_can_atlas_2018"
## [153] "ucec_tcga_pub" "ucs_msk_2024"
## [155] "ucs_tcga_gdc" "uec_cptac_gdc"
## [157] "um_tcga_gdc" "utuc_cornell_baylor_mdacc_2019"
## [159] "utuc_msk_2019" "utuc_pdx_msk_2019"
##
## $`subscript out of bounds`
## [1] "acyc_mgh_2016" "acyc_mskcc_2013"
## [3] "alal_target_gdc" "alk_msk_2026"
## [5] "angs_painter_2025" "luad_mskcc_2020"
## [7] "luad_mskcc_2023_met_organotropism" "luad_mskimpact_2021"
## [9] "lung_nci_2022" "thym_tcga_pan_can_atlas_2018"
## [11] "ucec_ancestry_cds_msk_2023" "ucec_msk_2018"
## [13] "ucs_tcga"
##
## $`Can't select columns that don't exist.\n✖ Column `value` doesn't exist.`
## [1] "braf_msk_archer_2024"
##
## $`Only two build types at a time can be used`
## [1] "breast_msk_2026" "crc_msk_2026" "msk_ctdna_vte_2024"
## [4] "sarcoma_mskcc_2022"
##
## $`Not Found (HTTP 404).`
## [1] "glioma_msk_2018"
##
## $`Barcodes must start with 'TCGA'`
## [1] "mixed_msk_tcga_2021"
##
## $`Frequency of NA values higher than the cutoff tolerance`
## [1] "mixed_selpercatinib_2020"
##
## $``n` must be a single number, not an integer `NA`.`
## [1] "msk_chord_2024" "msk_impact_50k_2026" "msk_met_2021"
##
## $`each range must have an end that is greater or equal to its start minus one`
## [1] "ucec_ccr_cfdna_msk_2022"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.json",
package = "cBioPortalData", mustWork = TRUE
)
err_pack_info <- fromJSON(pack_errs)We can do the same for this data:
length(err_pack_info)## [1] 5lengths(err_pack_info)## more columns than column names
## 12
## Frequency of NA values higher than the cutoff tolerance
## 5
## invalid class "ExperimentList" object: \n Non-unique names provided
## 2
## non-character argument
## 2
## 'wget' call had nonzero exit status
## 13We 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] "invalid class \"ExperimentList\" object: \n Non-unique names provided"
## [4] "non-character argument"
## [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" "gbm_cptac_2021"
## [3] "luad_mskimpact_2021" "mbl_dkfz_2017"
## [5] "pan_origimed_2020" "sarcoma_msk_2022"
## [7] "bowel_colitis_msk_2022" "prad_msk_mdanderson_2023"
## [9] "brca_tcga_pan_can_atlas_2018" "coadread_tcga_pan_can_atlas_2018"
## [11] "ov_tcga_pan_can_atlas_2018" "sarc_tcga_pan_can_atlas_2018"
##
## $`Frequency of NA values higher than the cutoff tolerance`
## [1] "ihch_mskcc_2020" "mixed_selpercatinib_2020"
## [3] "ucec_ccr_msk_2022" "mixed_msk_tcga_2021"
## [5] "ihch_msk_2021"
##
## $`invalid class "ExperimentList" object: \n Non-unique names provided`
## [1] "mpnst_mskcc" "stad_tcga_pub"
##
## $`non-character argument`
## [1] "pcpg_tcga_pub" "mbn_mdacc_2013"
##
## $`'wget' call had nonzero exit status`
## [1] "braf_msk_impact_2024" "braf_msk_archer_2024" "prostate_msk_2024"
## [4] "pcnsl_msk_2024" "pdac_msk_2024" "ucs_msk_2024"
## [7] "asclc_msk_2024" "lms_msk_2024" "crc_orion_2024"
## [10] "brca_aurora_2023" "hcc_msk_2024" "pancreas_msk_2024"
## [13] "pancan_mimsi_msk_2024"sessionInfo()## R version 4.6.1 (2026-06-24)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.5 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.24-bioc/R/lib/libRblas.so
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0 LAPACK version 3.12.0
##
## 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] jsonlite_2.0.0 survminer_0.5.2
## [3] ggpubr_1.0.0 ggplot2_4.0.3
## [5] survival_3.8-12 cBioPortalData_2.25.5
## [7] MultiAssayExperiment_1.39.1 SummarizedExperiment_1.43.0
## [9] Biobase_2.73.2 GenomicRanges_1.65.4
## [11] Seqinfo_1.3.2 IRanges_2.47.5
## [13] S4Vectors_0.51.10 BiocGenerics_0.59.12
## [15] generics_0.1.4 MatrixGenerics_1.25.0
## [17] matrixStats_1.5.0 AnVIL_1.25.3
## [19] AnVILBase_1.7.0 dplyr_1.2.1
## [21] BiocStyle_2.41.0
##
## loaded via a namespace (and not attached):
## [1] RColorBrewer_1.1-3 magrittr_2.0.5
## [3] magick_2.9.1 GenomicFeatures_1.65.0
## [5] farver_2.1.2 rmarkdown_2.32
## [7] BiocIO_1.23.3 vctrs_0.7.3
## [9] memoise_2.0.1 Rsamtools_2.29.0
## [11] RCurl_1.98-1.20 tinytex_0.61
## [13] rstatix_1.1.0 htmltools_0.5.9
## [15] S4Arrays_1.13.2 BiocBaseUtils_1.15.1
## [17] lambda.r_1.2.6 curl_8.1.0
## [19] broom_1.0.13 Formula_1.2-6
## [21] SparseArray_1.13.4 sass_0.4.10
## [23] bslib_0.12.0 htmlwidgets_1.6.4
## [25] keyring_1.4.1 httr2_1.3.0
## [27] futile.options_1.0.1 cachem_1.1.0
## [29] GenomicAlignments_1.49.2 mime_0.13
## [31] lifecycle_1.0.5 pkgconfig_2.0.3
## [33] Matrix_1.7-6 R6_2.6.1
## [35] fastmap_1.2.0 shiny_1.14.0
## [37] digest_0.6.39 GCPtools_1.3.2
## [39] RaggedExperiment_1.37.0 AnnotationDbi_1.75.2
## [41] ps_1.9.3 RSQLite_3.53.3
## [43] labeling_0.4.3 filelock_1.0.3
## [45] RTCGAToolbox_2.43.1 RJSONIO_2.0.5
## [47] httr_1.4.9 abind_1.4-8
## [49] compiler_4.6.1 bit64_4.8.6
## [51] withr_3.0.3 backports_1.5.1
## [53] S7_0.2.2 BiocParallel_1.47.0
## [55] carData_3.0-6 DBI_1.3.0
## [57] ggsignif_0.6.4 rappdirs_0.3.4
## [59] DelayedArray_0.39.8 rjson_0.2.23
## [61] tools_4.6.1 chromote_0.5.1
## [63] otel_0.3.0 httpuv_1.6.17
## [65] glue_1.8.1 restfulr_0.0.17
## [67] promises_1.5.0 gridtext_0.1.6
## [69] grid_4.6.1 gtable_0.3.6
## [71] tzdb_0.5.0 tidyr_1.3.2
## [73] websocket_1.4.4 data.table_1.18.6.1
## [75] hms_1.1.4 car_3.1-5
## [77] xml2_1.6.0 utf8_1.2.6
## [79] XVector_0.53.0 markdown_2.1
## [81] pillar_1.11.1 stringr_1.6.0
## [83] later_1.4.8 splines_4.6.1
## [85] ggtext_0.2.0 BiocFileCache_3.3.3
## [87] lattice_0.23-1 rtracklayer_1.73.0
## [89] bit_4.6.0 tidyselect_1.2.1
## [91] Biostrings_2.81.9 miniUI_0.1.2
## [93] knitr_1.52 gridExtra_2.3.1
## [95] litedown_1.0 bookdown_0.48
## [97] futile.logger_1.4.9 xfun_0.62
## [99] DT_0.34.0 stringi_1.8.9
## [101] UCSC.utils_1.9.0 yaml_2.3.12
## [103] evaluate_1.0.5 codetools_0.2-20
## [105] cigarillo_1.3.1 tibble_3.3.1
## [107] BiocManager_1.30.27 cli_3.6.6
## [109] xtable_1.8-8 processx_3.9.0
## [111] jquerylib_0.1.4 dichromat_2.0-1
## [113] Rcpp_1.1.2 GenomeInfoDb_1.49.2
## [115] GenomicDataCommons_1.37.0 dbplyr_2.6.0
## [117] png_0.1-9 XML_3.99-0.25
## [119] rapiclient_0.1.8 parallel_4.6.1
## [121] TCGAutils_1.33.3 readr_2.2.0
## [123] blob_1.3.0 bitops_1.1-0
## [125] scales_1.4.0 purrr_1.2.2
## [127] crayon_1.5.3 rlang_1.3.0
## [129] KEGGREST_1.53.6 rvest_1.0.5
## [131] formatR_1.14