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ammarsabir15
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0
@ammarsabir15-12089
Last seen 8.2 years ago
I am trying to perform enrichment Analysis on GBM datasets from TCGA. I have downloaded the datasets from TCGA using following code.
library(TCGAbiolinks)
query <- GDCquery(project = "TCGA-GBM",
data.category = "Transcriptome Profiling",
data.type = "Gene Expression Quantification")
GDCdownload(query)data <- GDCprepare(query)
The data was downloaded successfully but when I try to normalize the data using following code
dataNorm <- TCGAanalyze_Normalization(tabDF = data , geneInfo = geneInfo)
Then following error comes
Error in as.vector(x) : no method for coercing this S4 class to a vector In addition: Warning messages: 1: In NSBS(i, x, exact = exact, upperBoundIsStrict = !allow.append) : subscript is an array, passing it thru as.vector() first 2: In NSBS(i, x, exact = exact, upperBoundIsStrict = !allow.append) : subscript is an array, passing it thru as.vector() first 3: In NSBS(i, x, exact = exact, upperBoundIsStrict = !allow.append) : subscript is an array, passing it thru as.vector() first 4: In NSBS(i, x, exact = exact, upperBoundIsStrict = !allow.append) : subscript is an array, passing it thru as.vector() first
Here is the sessioninfo()
> sessionInfo() R version 3.3.1 (2016-06-21) Platform: x86_64-pc-linux-gnu (64-bit) Running under: Ubuntu 15.10 locale: [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=ur_PK LC_COLLATE=en_US.UTF-8 [5] LC_MONETARY=ur_PK LC_MESSAGES=en_US.UTF-8 LC_PAPER=ur_PK LC_NAME=C [9] LC_ADDRESS=C LC_TELEPHONE=C LC_MEASUREMENT=ur_PK LC_IDENTIFICATION=C attached base packages: [1] stats4 parallel stats graphics grDevices utils datasets methods base other attached packages: [1] Biostrings_2.40.2 XVector_0.12.1 devtools_1.12.0 [4] SummarizedExperiment_1.2.3 TCGAbiolinks_2.0.13 GenomicRanges_1.24.3 [7] GenomeInfoDb_1.8.7 IRanges_2.6.1 S4Vectors_0.10.3 [10] Biobase_2.32.0 BiocGenerics_0.18.0 loaded via a namespace (and not attached): [1] TH.data_1.0-7 colorspace_1.3-0 [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] GlobalOptions_0.0.10 parmigene_1.0.2 [11] matlab_1.0.2 hexbin_1.27.1 [13] affyio_1.42.0 ggrepel_0.6.5 [15] flexmix_2.3-13 AnnotationDbi_1.34.4 [17] mvtnorm_1.0-5 xml2_1.0.0 [19] coin_1.1-3 codetools_0.2-14 [21] splines_3.3.1 R.methodsS3_1.7.1 [23] doParallel_1.0.10 DESeq_1.24.0 [25] robustbase_0.92-6 knitr_1.15.1 [27] geneplotter_1.50.0 jsonlite_1.1 [29] Rsamtools_1.24.0 annotate_1.50.1 [31] cluster_2.0.4 kernlab_0.9-25 [33] R.oo_1.21.0 supraHex_1.10.0 [35] graph_1.50.0 readr_1.0.0 [37] httr_1.2.1 assertthat_0.1 [39] Matrix_1.2-7.1 lazyeval_0.2.0 [41] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2 limma_3.28.21 [43] tools_3.3.1 igraph_1.0.1 [45] gtable_0.2.0 affy_1.50.0 [47] dplyr_0.5.0 ggthemes_3.3.0 [49] ShortRead_1.30.0 Rcpp_0.12.7 [51] trimcluster_0.1-2 gdata_2.17.0 [53] ape_3.5 preprocessCore_1.34.0 [55] nlme_3.1-128 rtracklayer_1.32.2 [57] iterators_1.0.8 fpc_2.1-10 [59] stringr_1.1.0 rvest_0.3.2 [61] gtools_3.5.0 XML_3.98-1.3 [63] dendextend_1.3.0 edgeR_3.14.0 [65] DEoptimR_1.0-6 zlibbioc_1.18.0 [67] MASS_7.3-44 zoo_1.7-14 [69] scales_0.4.1 aroma.light_3.2.0 [71] BiocInstaller_1.22.3 sandwich_2.3-4 [73] RColorBrewer_1.1-2 ComplexHeatmap_1.10.2 [75] memoise_1.0.0 ggplot2_2.2.0 [77] downloader_0.4 biomaRt_2.28.0 [79] reshape_0.8.6 latticeExtra_0.6-28 [81] stringi_1.1.2 RSQLite_1.0.0 [83] genefilter_1.54.2 foreach_1.4.3 [85] GenomicFeatures_1.24.5 caTools_1.17.1 [87] BiocParallel_1.6.6 shape_1.4.2 [89] prabclus_2.2-6 matrixStats_0.51.0 [91] bitops_1.0-6 dnet_1.0.9 [93] lattice_0.20-34 GenomicAlignments_1.8.4 [95] GGally_1.3.0 plyr_1.8.4 [97] magrittr_1.5 R6_2.2.0 [99] gplots_3.0.1 multcomp_1.4-6 [101] DBI_0.5-1 withr_1.0.2 [103] whisker_0.3-2 survival_2.40-1 [105] RCurl_1.95-4.8 nnet_7.3-12 [107] tibble_1.2 EDASeq_2.6.2 [109] KernSmooth_2.23-15 GetoptLong_0.1.5 [111] grid_3.3.1 data.table_1.10.0 [113] Rgraphviz_2.16.0 ConsensusClusterPlus_1.36.0 [115] digest_0.6.10 diptest_0.75-7 [117] xtable_1.8-2 R.utils_2.5.0 [119] munsell_0.4.3
