Entering edit mode
8.3 years ago
pxf109
▴
20
I try to use TCGAbiolinks in Rstudio(GDCquery function), but it always has a error : cannot open file '~/R/x86_64-pc-linux-gnu-library/3.3/readr/R/sysdata.rdb': No such file or directory. I have try these on Windows and Ubnutu, but the result is the same. I am sure this package exist in right posistion. I am not sure whether this problem is caused by the permission. I tried to reinstall readr, but I still has this problem. However, i try to use TCGAbiolinks through command line, it seems work. Does TCGAbiolinks support Rstudo or it need some special set?
library("TCGAbiolinks", lib.loc="~/R/x86_64-pc-linux-gnu-library/3.3")
query <- GDCquery(project = "TARGET-AML",
data.category = "Transcriptome Profiling",
data.type = "Gene Expression Quantification",
workflow.type = "HTSeq - Counts")
error message: cannot open file '~/R/x86_64-pc-linux-gnu-library/3.3/readr/R/sysdata.rdb': No such file or directory.
Here is my sessioninfo:
R version 3.3.1 (2016-06-21)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 16.04.1 LTS
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8 LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] TCGAbiolinks_2.0.6
loaded via a namespace (and not attached):
[1] circlize_0.3.8 aroma.light_3.2.0 plyr_1.8.4
[4] igraph_1.0.1 lazyeval_0.2.0 ConsensusClusterPlus_1.36.0
[7] splines_3.3.1 BiocParallel_1.6.6 GenomeInfoDb_1.8.3
[10] ggplot2_2.1.0 TH.data_1.0-7 digest_0.6.10
[13] foreach_1.4.3 BiocInstaller_1.22.3 gdata_2.17.0
[16] magrittr_1.5 cluster_2.0.4 doParallel_1.0.10
[19] limma_3.28.20 ComplexHeatmap_1.10.2 Biostrings_2.40.2
[22] readr_1.0.0 annotate_1.50.0 matrixStats_0.50.2
[25] R.utils_2.3.0 sandwich_2.3-4 colorspace_1.2-6
[28] rvest_0.3.2 ggrepel_0.5 haven_0.2.1
[31] dplyr_0.5.0 RCurl_1.95-4.8 jsonlite_1.0
[34] hexbin_1.27.1 TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2 graph_1.50.0
[37] genefilter_1.54.2 lme4_1.1-12 supraHex_1.10.0
[40] survival_2.39-5 zoo_1.7-13 iterators_1.0.8
[43] ape_3.5 gtable_0.2.0 zlibbioc_1.18.0
[46] XVector_0.12.1 sjstats_0.4.0 GetoptLong_0.1.4
[49] sjmisc_1.8 kernlab_0.9-24 Rgraphviz_2.16.0
[52] shape_1.4.2 prabclus_2.2-6 BiocGenerics_0.18.0
[55] DEoptimR_1.0-6 scales_0.4.0 DESeq_1.24.0
[58] mvtnorm_1.0-5 DBI_0.5 GGally_1.2.0
[61] edgeR_3.14.0 ggthemes_3.2.0 Rcpp_0.12.6
[64] xtable_1.8-2 matlab_1.0.2 mclust_5.2
[67] preprocessCore_1.34.0 stats4_3.3.1 httr_1.2.1
[70] gplots_3.0.1 RColorBrewer_1.1-2 fpc_2.1-10
[73] modeltools_0.2-21 reshape_0.8.5 XML_3.98-1.4
[76] R.methodsS3_1.7.1 flexmix_2.3-13 nnet_7.3-12
[79] reshape2_1.4.1 AnnotationDbi_1.34.4 munsell_0.4.3
[82] tools_3.3.1 downloader_0.4 RSQLite_1.0.0
[85] broom_0.4.1 stringr_1.1.0 knitr_1.14
[88] robustbase_0.92-6 caTools_1.17.1 dendextend_1.3.0
[91] coin_1.1-2 EDASeq_2.6.2 nlme_3.1-128
[94] whisker_0.3-2 R.oo_1.20.0 xml2_1.0.0
[97] biomaRt_2.28.0 curl_1.2 affyio_1.42.0
[100] tibble_1.2 geneplotter_1.50.0 stringi_1.1.1
[103] GenomicFeatures_1.24.5 lattice_0.20-33 trimcluster_0.1-2
[106] Matrix_1.2-7 psych_1.6.6 nloptr_1.0.4
[109] effects_3.1-1 stringdist_0.9.4.1 GlobalOptions_0.0.10
[112] data.table_1.9.6 cowplot_0.6.2 bitops_1.0-6
[115] dnet_1.0.9 rtracklayer_1.32.2 GenomicRanges_1.24.2
[118] R6_2.1.3 latticeExtra_0.6-28 affy_1.50.0
[121] hwriter_1.3.2 ShortRead_1.30.0 KernSmooth_2.23-15
[124] IRanges_2.6.1 codetools_0.2-14 MASS_7.3-45
[127] gtools_3.5.0 assertthat_0.1 chron_2.3-47
[130] SummarizedExperiment_1.2.3 rjson_0.2.15 mnormt_1.5-4
[133] GenomicAlignments_1.8.4 Rsamtools_1.24.0 multcomp_1.4-6
[136] S4Vectors_0.10.3 diptest_0.75-7 parallel_3.3.1
[139] sjPlot_2.0.2 grid_3.3.1 tidyr_0.6.0
[142] class_7.3-14 minqa_1.2.4
Taceback():
11: date_names_lang(date_names)
10: locale()
9: default_locale()
8: guess_header_(datasource, tokenizer, locale)
7: guess_header(ds_header, tokenizer, locale)
6: col_spec_standardise(data, skip = skip, comment = comment, n = guess_max,
col_names = col_names, col_types = col_`enter code here`types, tokenizer = tokenizer,
locale = locale)
5: read_delimited(file, tokenizer, col_names = col_names, col_types = col_types,
locale = locale, skip = skip, comment = comment, n_max = n_max,
guess_max = guess_max, progress = progress)
4: read_tsv("https://gdc-api.nci.nih.gov/projects?size=1000&format=tsv",
col_types = "ccccccc")
3: getGDCprojects()
2: checkProjectInput(project)
1: GDCquery(project = "TARGET-AML", data.category = "Transcriptome Profiling",
data.type = "Gene Expression Quantification", workflow.type = "HTSeq - Counts")
