Question: Problem with using GDC_queryMaf()
gravatar for d r
9 weeks ago by
d r150
d r150 wrote:


I am trying to use the TCGAbiolinks package to extract mutation data for several types of tumors I am interested in.

I am using GDCquery_Maf, using exactly the syntax indicated in the help file:

acc.maf <- GDCquery_Maf("ACC")

However I then get the error:

Error in missing(tumor) : 'missing' can only be used for arguments

The maf file is actually written in my working directory, but for some reason is not imported into the R workspace. Also when I'm using save.csv=TRUE, no csv file is created.

I have tried this with several tumor types, and always got the same error.

Any idea what may be causing this?

Thanks in advacne

Dolev Rahat


> sessionInfo()
R version 3.3.2 (2016-10-31)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 14.04.5 LTS

 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C               LC_TIME=en_US.UTF-8       
 [7] LC_PAPER=he_IL.UTF-8       LC_NAME=C                  LC_ADDRESS=C              

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] TCGA2STAT_1.2        BiocInstaller_1.22.3 TCGAbiolinks_2.0.13  curl_2.3            

loaded via a namespace (and not attached):
  [1] TH.data_1.0-8                           colorspace_1.3-2                       
  [3] rjson_0.2.15                            hwriter_1.3.2                          
  [5] class_7.3-14                            modeltools_0.2-21                      
  [7] mclust_5.2.2                            circlize_0.3.9                         
  [9] XVector_0.12.1                          GenomicRanges_1.24.3                   
 [11] GlobalOptions_0.0.10                    parmigene_1.0.2                        
 [13] matlab_1.0.2                            hexbin_1.27.1                          
 [15] affyio_1.42.0                           ggrepel_0.6.5                          
 [17] flexmix_2.3-13                          AnnotationDbi_1.34.4                   
 [19] mvtnorm_1.0-5                           xml2_1.1.1                             
 [21] coin_1.1-3                              codetools_0.2-15                       
 [23] splines_3.3.2                           R.methodsS3_1.7.1                      
 [25] doParallel_1.0.10                       DESeq_1.24.0                           
 [27] robustbase_0.92-7                       knitr_1.15.2                           
 [29] geneplotter_1.50.0                      jsonlite_1.2                           
 [31] Rsamtools_1.24.0                        annotate_1.50.1                        
 [33] cluster_2.0.5                           kernlab_0.9-25                         
 [35] R.oo_1.21.0                             supraHex_1.10.0                        
 [37] graph_1.50.0                            readr_1.0.0                            
 [39] httr_1.2.1                              assertthat_0.1                         
 [41] Matrix_1.2-8                            lazyeval_0.2.0                         
 [43] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2 limma_3.28.21                          
 [45] tools_3.3.2                             igraph_1.0.1                           
 [47] gtable_0.2.0                            affy_1.50.0                            
 [49] dplyr_0.5.0                             ggthemes_3.3.0                         
 [51] ShortRead_1.30.0                        Rcpp_0.12.9                            
 [53] Biobase_2.32.0                          trimcluster_0.1-2                      
 [55] Biostrings_2.40.2                       gdata_2.17.0                           
 [57] ape_4.1                                 preprocessCore_1.34.0                  
 [59] nlme_3.1-131                            rtracklayer_1.32.2                     
 [61] iterators_1.0.8                         fpc_2.1-10                             
 [63] stringr_1.2.0                           rvest_0.3.2                            
 [65] gtools_3.5.0                            XML_3.98-1.5                           
 [67] dendextend_1.4.0                        edgeR_3.14.0                           
 [69] DEoptimR_1.0-8                          zlibbioc_1.18.0                        
 [71] MASS_7.3-45                             zoo_1.7-14                             
 [73] scales_0.4.1                            aroma.light_3.2.0                      
 [75] parallel_3.3.2                          SummarizedExperiment_1.2.3             
 [77] sandwich_2.3-4                          RColorBrewer_1.1-2                     
 [79] ComplexHeatmap_1.13.1                   memoise_1.0.0                          
 [81] gridExtra_2.2.1                         ggplot2_2.2.1                          
 [83] downloader_0.4                          biomaRt_2.28.0                         
 [85] reshape_0.8.6                           latticeExtra_0.6-28                    
 [87] stringi_1.1.2                           RSQLite_1.1-2                          
 [89] highr_0.6                               genefilter_1.54.2                      
 [91] S4Vectors_0.10.3                        foreach_1.4.3                          
 [93] caTools_1.17.1                          GenomicFeatures_1.24.5                 
 [95] BiocGenerics_0.18.0                     BiocParallel_1.6.6                     
 [97] shape_1.4.2                             GenomeInfoDb_1.8.7                     
 [99] prabclus_2.2-6                          matrixStats_0.51.0                     
[101] bitops_1.0-6                            dnet_1.0.10                            
[103] lattice_0.20-34                         GenomicAlignments_1.8.4                
[105] GGally_1.3.0                            plyr_1.8.4                             
[107] magrittr_1.5                            R6_2.2.0                               
[109] gplots_3.0.1                            IRanges_2.6.1                          
[111] multcomp_1.4-6                          DBI_0.5-1                              
[113] whisker_0.3-2                           survival_2.40-1                        
[115] RCurl_1.95-4.8                          nnet_7.3-12                            
[117] tibble_1.2                              EDASeq_2.6.2                           
[119] KernSmooth_2.23-15                      viridis_0.3.4                          
[121] GetoptLong_0.1.5                        grid_3.3.2                             
[123] data.table_1.10.4                       Rgraphviz_2.16.0                       
[125] ConsensusClusterPlus_1.36.0             digest_0.6.12                          
[127] diptest_0.75-7                          xtable_1.8-2                           
[129] R.utils_2.5.0                           stats4_3.3.2                           
[131] munsell_0.4.3 
ADD COMMENTlink modified 5 weeks ago by tiagochst70 • written 9 weeks ago by d r150
gravatar for tiagochst
5 weeks ago by
Brazil - University of São Paulo/ Los Angeles - Cedars-Sinai Medical Center
tiagochst70 wrote:


GDC updated the MAF files available and there path. As the version of the package is old,  you will need to update TCGAbiolinks.

The new documentation can be found at:

Best regards,


ADD COMMENTlink written 5 weeks ago by tiagochst70
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