Introduction

TCGAbiolinksGUI was created to help users without knowledge of programming to search, download and analyze
TCGA data. This package offers a graphical user interface to the R/Bioconductor packages TCGAbiolinks and ELMER packages.
Also, some other useful packages from Bioconductor, such as ComplexHeatmap package has been used for data visualization.

A running version of the GUI is found in http://tcgabiolinks.fmrp.usp.br:3838/

Installing TCGAbiolinksGUI

To install the package from Bioconductor devel repository, please, use the code below.

# for the moment it must be devel version of Bioconductor
if (!requireNamespace("BiocManager", quietly=TRUE))
    install.packages("BiocManager")
BiocManager::install("TCGAbiolinksGUI", dependencies = TRUE)

To install the package development version from Github, please, use the code below.

if (!requireNamespace("BiocManager", quietly=TRUE))
    install.packages("BiocManager")
deps <- c("devtools")
for(pkg in deps)  if (!pkg %in% installed.packages()) BiocManager::install(pkg, dependencies = TRUE)
devtools::install_github("tiagochst/ELMER.data")
devtools::install_github("BioinformaticsFMRP/TCGAbiolinksGUI.data",ref = "R_3.4")
devtools::install_github("BioinformaticsFMRP/TCGAbiolinksGUI")

Docker image

TCGAbiolinksGUI is available as Docker image (self-contained environments that contain everything needed to run the software),
which can be easily run on Mac OS, Windows and Linux systems.

The image can be obtained from Docker Hub: https://hub.docker.com/r/tiagochst/tcgabiolinksgui/

For more information please check: https://docs.docker.com/ and https://www.bioconductor.org/help/docker/

Setting up image using graphical user interface (GUI)

This PDF
shows how to install and execute the image using kitematic, which
offers a graphical user interface (GUI) to control your app containers.

Setting up image using command-line

• Download image: docker pull tiagochst/tcgabiolinksgui
• To run RStudio Server and shiny-server, but the data is not saved if container is stopped:
  sudo docker run --name tcgabiolinksgui -d -P -v /home/$USER/docker:/home/rstudio -p 3333:8787 -p 3334:3838 tiagochst/tcgabiolinksgui

1. For more information how data can be saved please read this wiki and see command below

• To run RStudio Server, shiny-server and save the results in the host machine please use the code below:
  sudo docker run --name tcgabiolinksgui -d -P -v /home/$USER/docker:/home/rstudio -p 3333:8787 -p 3334:3838 tiagochst/tcgabiolinksgui

1. In case rstudio is not accessible please check if the folder created (docker) has the right permission
2. If your system is windows or mac you will need to change /home/$USER/docker
   to the correct system path. Examples can be found in this github page

• To stop the image:

1. Run sudo docker stop tcgabiolinksgui to stop it

• To start the image again (after the first time ran with docker run and stopped).

1. Run sudo docker start tcgabiolinksgui

Accessing tools after the image is running:

• TCGAbiolinksGUI will be available at :3334/tcgabiolinksgui
• RStudio will be available at :3333 (username: rstudio , password:rstudio)

Quick start

The following commands should be used in order to start the graphical user interface.

library(TCGAbiolinksGUI)
TCGAbiolinksGUI()

Video tutorials

To facilitate the use of this package, we have created some tutorial videos demonstrating the tool.
Please check this youtube list.

PDF tutorials

For each section we created some PDFs with detailing the steps of each section:
Link to folder with PDFs

Citation

Please cite both TCGAbiolinks package and TCGAbiolinksGUI:

• Colaprico A, Silva TC, Olsen C, Garofano L, Cava C, Garolini D, Sabedot T, Malta TM, Pagnotta SM, Castiglioni I, Ceccarelli M, Bontempi G and Noushmehr H. “TCGAbiolinks: an R/Bioconductor package for integrative analysis of TCGA data.” Nucleic acids research (2015): gkv1507.

• TCGAbiolinksGUI: A Graphical User Interface to analyze TCGA data. Manuscript in preparation.

Also, if you have used ELMER analysis please cite:

• Yao, L., Shen, H., Laird, P. W., Farnham, P. J., & Berman, B. P. “Inferring regulatory element landscapes and transcription factor networks from cancer methylomes.” Genome Biol 16 (2015): 105.
• Yao, Lijing, Benjamin P. Berman, and Peggy J. Farnham. “Demystifying the secret mission of enhancers: linking distal regulatory elements to target genes.” Critical reviews in biochemistry and molecular biology 50.6 (2015): 550-573.

If you have used OncoPrint plot and Heatmap Plot please cite:

• Gu, Zuguang, Roland Eils, and Matthias Schlesner. “Complex heatmaps reveal patterns and correlations in multidimensional genomic data.” Bioinformatics (2016): btw313

If you have used Pathway plot please cite:

• Luo, Weijun, Brouwer and Cory (2013). “Pathview: an R/Bioconductor package for pathway-based data integration and visualization.” Bioinformatics, 29(14), pp. 1830-1831.

Increasing loaded DLL

If you receive this error message: maximal number of DLLs reached...
You will need to increase the maximum number of DLL R can load. R_MAX_NUM_DLLS
In MACOS just modify the file /Library/Frameworks/R.framework/Resources/etc/Renviron
and add R_MAX_NUM_DLLS=110 in the end.

For other OS check https://stat.ethz.ch/R-manual/R-patched/library/base/html/Startup.html.