Integrative analysis workshop with TCGAbiolinks and ELMER - Get DATA GUI
Tiago Chedraoui Silva, Simon Coetzee, Dennis Hazelett, Ben Berman, Houtan Noushmehr
2017-07-24
Introduction
In this section, we will download the same data using TCGAbiolinks, but instead of doing it programmatically we will use TCGAbiolinksGUI (Silva et al. 2017).
First we will launch the TCGAbiolinksGUI.
library(TCGAbiolinksGUI)
TCGAbiolinksGUI()Downloading data
Gene expression
After launching the GUI select the GDC Data/Get GDC data/Molecular data.
Fill the search fields with the same information below and click on Visualize Data. If you select Filter using clinical data under the clinical filter we will also plot the clinical information.
A plot with the summary of the data will be shown.
Also, if you want more details you can also open the GDC search results: Results section.
After the query is completed, you will be able to download the data and convert it to an R object in the Download & Prepare section.
If successful it will give you a message where the data was saved.
## Visualizing the Summarized Experiment
To visualize the SummarizedExperiment object select GDC Data/Manage SummarizedExperiment:
And click on Select Summarized Experiment file.
Select the file downloaded from GDC.
You can access sample metadata
the assay data
Accessing assay information from SummarizedExperiment
or the features metadata
Accessing features information from SummarizedExperiment
DNA methylation
Again, fill the search fields with the same information below and click on Visualize Data. If you select Filter using clinical data under the clinical filter we will also plot the clinical information.
A plot with the summary of the data will be shown.
After the query is completed, you will be able to download the data and convert it to an R object in the Download & Prepare section.
If successful it will give you a message where the data was saved.
Session Info
sessionInfo()## R version 3.4.1 (2017-06-30)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS Sierra 10.12.5
##
## Matrix products: default
## BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] parallel stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] Bioc2017.TCGAbiolinks.ELMER_0.0.0.9000
## [2] BiocInstaller_1.26.0
## [3] ELMER_2.0.1
## [4] TCGAbiolinks_2.5.6
## [5] dplyr_0.7.2
## [6] SummarizedExperiment_1.6.3
## [7] DelayedArray_0.2.7
## [8] matrixStats_0.52.2
## [9] Biobase_2.36.2
## [10] GenomicRanges_1.28.4
## [11] GenomeInfoDb_1.12.2
## [12] IRanges_2.10.2
## [13] S4Vectors_0.14.3
## [14] BiocGenerics_0.22.0
## [15] bindrcpp_0.2
## [16] MultiAssayExperiment_1.2.1
## [17] DT_0.2
## [18] ELMER.data_2.0.1
##
## loaded via a namespace (and not attached):
## [1] rtracklayer_1.36.4 ggthemes_3.4.0
## [3] prabclus_2.2-6 R.methodsS3_1.7.1
## [5] tidyr_0.6.3 ggplot2_2.2.1
## [7] acepack_1.4.1 bit64_0.9-7
## [9] knitr_1.16 aroma.light_3.6.0
## [11] R.utils_2.5.0 data.table_1.10.4
## [13] rpart_4.1-11 hwriter_1.3.2
## [15] RCurl_1.95-4.8 AnnotationFilter_1.0.0
## [17] doParallel_1.0.10 GenomicFeatures_1.28.4
## [19] RSQLite_2.0 commonmark_1.2
## [21] bit_1.1-12 BiocStyle_2.4.0
## [23] xml2_1.1.1 httpuv_1.3.5
## [25] assertthat_0.2.0 viridis_0.4.0
## [27] hms_0.3 evaluate_0.10.1
## [29] DEoptimR_1.0-8 dendextend_1.5.2
## [31] km.ci_0.5-2 DBI_0.7
## [33] geneplotter_1.54.0 htmlwidgets_0.9
## [35] reshape_0.8.6 EDASeq_2.10.0
## [37] matlab_1.0.2 purrr_0.2.2.2
## [39] selectr_0.3-1 ggpubr_0.1.4
## [41] backports_1.1.0 trimcluster_0.1-2
## [43] annotate_1.54.0 biomaRt_2.32.1
## [45] ensembldb_2.0.3 withr_1.0.2
## [47] Gviz_1.20.0 BSgenome_1.44.0
## [49] robustbase_0.92-7 checkmate_1.8.3
## [51] GenomicAlignments_1.12.1 mclust_5.3
## [53] mnormt_1.5-5 cluster_2.0.6
## [55] lazyeval_0.2.0 genefilter_1.58.1
## [57] edgeR_3.18.1 pkgconfig_2.0.1
## [59] labeling_0.3 nlme_3.1-131
## [61] ProtGenerics_1.8.0 nnet_7.3-12
## [63] devtools_1.13.2 bindr_0.1
## [65] rlang_0.1.1 diptest_0.75-7
## [67] downloader_0.4 AnnotationHub_2.8.2
## [69] dichromat_2.0-0 rprojroot_1.2
## [71] Matrix_1.2-10 KMsurv_0.1-5
## [73] zoo_1.8-0 base64enc_0.1-3
## [75] whisker_0.3-2 GlobalOptions_0.0.12
## [77] viridisLite_0.2.0 rjson_0.2.15
## [79] bitops_1.0-6 shinydashboard_0.6.1
## [81] R.oo_1.21.0 ConsensusClusterPlus_1.40.0
## [83] Biostrings_2.44.1 blob_1.1.0
## [85] shape_1.4.2 stringr_1.2.0
## [87] ShortRead_1.34.0 readr_1.1.1
## [89] scales_0.4.1 memoise_1.1.0
## [91] magrittr_1.5 plyr_1.8.4
## [93] zlibbioc_1.22.0 compiler_3.4.1
## [95] RColorBrewer_1.1-2 Rsamtools_1.28.0
## [97] XVector_0.16.0 htmlTable_1.9
## [99] Formula_1.2-2 MASS_7.3-47
## [101] stringi_1.1.5 yaml_2.1.14
## [103] locfit_1.5-9.1 latticeExtra_0.6-28
## [105] ggrepel_0.6.5 survMisc_0.5.4
## [107] grid_3.4.1 VariantAnnotation_1.22.3
## [109] tools_3.4.1 circlize_0.4.1
## [111] rstudioapi_0.6 foreach_1.4.3
## [113] foreign_0.8-69 git2r_0.18.0
## [115] gridExtra_2.2.1 digest_0.6.12
## [117] shiny_1.0.3 cmprsk_2.2-7
## [119] fpc_2.1-10 Rcpp_0.12.12
## [121] broom_0.4.2 httr_1.2.1
## [123] survminer_0.4.0 AnnotationDbi_1.38.1
## [125] biovizBase_1.24.0 ComplexHeatmap_1.14.0
## [127] psych_1.7.5 kernlab_0.9-25
## [129] colorspace_1.3-2 rvest_0.3.2
## [131] XML_3.98-1.9 splines_3.4.1
## [133] flexmix_2.3-14 plotly_4.7.0
## [135] xtable_1.8-2 jsonlite_1.5
## [137] UpSetR_1.3.3 modeltools_0.2-21
## [139] R6_2.2.2 Hmisc_4.0-3
## [141] htmltools_0.3.6 mime_0.5
## [143] glue_1.1.1 BiocParallel_1.10.1
## [145] DESeq_1.28.0 class_7.3-14
## [147] interactiveDisplayBase_1.14.0 codetools_0.2-15
## [149] mvtnorm_1.0-6 lattice_0.20-35
## [151] tibble_1.3.3 curl_2.7
## [153] survival_2.41-3 limma_3.32.3
## [155] roxygen2_6.0.1 rmarkdown_1.6
## [157] munsell_0.4.3 GetoptLong_0.1.6
## [159] GenomeInfoDbData_0.99.0 iterators_1.0.8
## [161] reshape2_1.4.2 gtable_0.2.0Bibliography
Silva, Tiago C., Antonio Colaprico, Catharina Olsen, Gianluca Bontempi, Michele Ceccarelli, Benjamin P. Berman, and Houtan Noushmehr. 2017. “TCGAbiolinksGUI: A Graphical User Interface to Analyze Cancer Molecular and Clinical Data.” bioRxiv. Cold Spring Harbor Labs Journals. doi:10.1101/147496.