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mukhamadeeva.r
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@mukhamadeevar-10991
Last seen 8.1 years ago
Hi,
I have a data type error when using GDCquery:
query_normal <- GDCquery(project = "TCGA-ACC", data.category = "Gene expression", data.type = "Gene expression quantification", platform = "Illumina HiSeq", file.type = "normalized_results", sample.type = "Solid Tissue Normal", legacy = TRUE) Accessing GDC. This might take a while... Error in GDCquery(project = "TCGA-ACC", data.category = "Gene expression", : Please set a valid data.type argument from the list below: =>
And there is no list that shows valid data types.
I checked it on GDC portal that such data type exists in data category "Gene expression" and the data is open.
Please, can you tell what to do to get such data using TCGAbiolinks.
Thank you!
Session Info:
R version 3.3.1 (2016-06-21) Platform: x86_64-w64-mingw32/x64 (64-bit) Running under: Windows 7 x64 (build 7601) Service Pack 1 locale: [1] LC_COLLATE=Russian_Russia.1251 LC_CTYPE=Russian_Russia.1251 [3] LC_MONETARY=Russian_Russia.1251 LC_NUMERIC=C [5] LC_TIME=Russian_Russia.1251 attached base packages: [1] stats4 parallel stats graphics grDevices utils datasets methods base other attached packages: [1] TCGAbiolinks_2.0.13 SummarizedExperiment_1.2.3 Biobase_2.32.0 [4] GenomicRanges_1.24.2 GenomeInfoDb_1.8.3 IRanges_2.6.1 [7] S4Vectors_0.10.3 BiocGenerics_0.18.0 loaded via a namespace (and not attached): [1] TH.data_1.0-7 colorspace_1.3-1 [3] rjson_0.2.15 hwriter_1.3.2 [5] class_7.3-14 modeltools_0.2-21 [7] mclust_5.2 circlize_0.3.9 [9] XVector_0.12.1 GlobalOptions_0.0.10 [11] parmigene_1.0.2 matlab_1.0.2 [13] hexbin_1.27.1 affyio_1.42.0 [15] ggrepel_0.6.3 flexmix_2.3-13 [17] AnnotationDbi_1.34.4 mvtnorm_1.0-5 [19] xml2_1.0.0 coin_1.1-2 [21] codetools_0.2-15 splines_3.3.1 [23] R.methodsS3_1.7.1 doParallel_1.0.10 [25] DESeq_1.24.0 robustbase_0.92-6 [27] knitr_1.15 geneplotter_1.50.0 [29] jsonlite_1.1 Rsamtools_1.24.0 [31] annotate_1.50.1 cluster_2.0.5 [33] kernlab_0.9-25 R.oo_1.21.0 [35] supraHex_1.10.0 graph_1.50.0 [37] readr_1.0.0 httr_1.2.1 [39] assertthat_0.1 Matrix_1.2-7.1 [41] lazyeval_0.2.0 TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2 [43] limma_3.28.21 tools_3.3.1 [45] igraph_1.0.1 gtable_0.2.0 [47] affy_1.50.0 dplyr_0.5.0 [49] ggthemes_3.2.0 ShortRead_1.30.0 [51] Rcpp_0.12.8 trimcluster_0.1-2 [53] Biostrings_2.40.2 gdata_2.17.0 [55] ape_3.5 preprocessCore_1.34.0 [57] nlme_3.1-128 rtracklayer_1.32.2 [59] iterators_1.0.8 fpc_2.1-10 [61] stringr_1.1.0 rvest_0.3.2 [63] gtools_3.5.0 XML_3.98-1.5 [65] dendextend_1.3.0 edgeR_3.14.0 [67] DEoptimR_1.0-8 zlibbioc_1.18.0 [69] MASS_7.3-45 zoo_1.7-13 [71] scales_0.4.1 aroma.light_3.2.0 [73] BiocInstaller_1.22.3 sandwich_2.3-4 [75] RColorBrewer_1.1-2 curl_2.2 [77] ComplexHeatmap_1.10.2 ggplot2_2.2.0 [79] downloader_0.4 biomaRt_2.28.0 [81] reshape_0.8.6 latticeExtra_0.6-28 [83] stringi_1.1.2 RSQLite_1.0.0 [85] highr_0.6 genefilter_1.54.2 [87] foreach_1.4.3 caTools_1.17.1 [89] GenomicFeatures_1.24.5 BiocParallel_1.6.6 [91] shape_1.4.2 chron_2.3-47 [93] prabclus_2.2-6 matrixStats_0.51.0 [95] bitops_1.0-6 dnet_1.0.9 [97] lattice_0.20-34 GenomicAlignments_1.8.4 [99] GGally_1.3.0 plyr_1.8.4 [101] magrittr_1.5 R6_2.2.0 [103] gplots_3.0.1 multcomp_1.4-6 [105] DBI_0.5-1 whisker_0.3-2 [107] survival_2.40-1 RCurl_1.95-4.8 [109] nnet_7.3-12 tibble_1.2 [111] EDASeq_2.6.2 KernSmooth_2.23-15 [113] GetoptLong_0.1.5 grid_3.3.1 [115] data.table_1.9.6 Rgraphviz_2.16.0 [117] ConsensusClusterPlus_1.36.0 digest_0.6.10 [119] diptest_0.75-7 xtable_1.8-2 [121] R.utils_2.5.0 munsell_0.4.3
