TCGAbiolinksGUI was created to help users more comfortable with graphical user interfaces (GUI) to search, download and analyze Cancer data. It offers a graphical user interface to the R/Bioconductor package TCGAbiolinks (Colaprico et al. 2016), which is able to access The National Cancer Institute (NCI) Genomic Data Commons (GDC) through its
GDC Application Programming Interface (API). Additional packages from Bioconductor are included, such as ComplexHeatmap package (Gu, Eils, and Schlesner 2016) to aid in visualizing the data, ELMER (Yao et al. 2015) to identify regulatory enhancers using gene expression + DNA methylation data + motif analysis and Pathview (Luo and Brouwer 2013) for pathway-based data integration and visualization.
The GUI was created using Shiny, a Web Application Framework for R, and uses several packages to provide advanced features that can enhance Shiny apps, such as shinyjs to add JavaScript actions for the app, shinydashboard to add dashboards and shinyFiles to provide an API for client side access to the server file system. A running version of the GUI is found in shinyapps.io
This work has been supported by a grant from Henry Ford Hospital (H.N.) and by the São Paulo Research Foundation FAPESP (2016/01389-7 to T.C.S. & H.N. and 2015/07925-5 to H.N.) the BridgeIRIS project, funded by INNOVIRIS, Region de Bruxelles Capitale, Brussels, Belgium, and by GENomic profiling of Gastrointestinal Inflammatory-Sensitive CANcers (GENGISCAN), Belgian FNRS PDR (T100914F to A.C., C.O. & G.B.). T.C.S. and B.P.B. were supported by the NCI Informatics Technology for Cancer Research program, NIH/NCI grant 1U01CA184826.
To install the package from the Bioconductor repository please use the following code.
source("https://bioconductor.org/biocLite.R")
biocLite("TCGAbiolinksGUI", dependencies = TRUE)
To install the development version of the package via GitHub:
source("https://bioconductor.org/biocLite.R")
deps <- c("pathview","clusterProfiler","ELMER", "DO.db","GO.db",
"ComplexHeatmap","EDASeq", "TCGAbiolinks")
for(pkg in deps)
if (!pkg %in% installed.packages()) biocLite(pkg, dependencies = TRUE)
deps <- c("devtools","shape","shiny","readr","googleVis",
"shinydashboard","shinyFiles","shinyjs","shinyBS")
for(pkg in deps)
if (!pkg %in% installed.packages()) install.packages(pkg,dependencies = TRUE)
devtools::install_github("BioinformaticsFMRP/TCGAbiolinksGUI")
The following commands should be used to start the graphical user interface.
library(TCGAbiolinksGUI)
TCGAbiolinksGUI()
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/
This PDF shows how to install and execute the image using kitematic, which offers a graphical user interface (GUI) to control your app containers.
sudo docker run -d -P -v /home/$USER/docker:/home/rstudio -p 3333:8787 -p 3334:3838 tiagochst/tcgabiolinksgui
sudo docker run -d -P -v /home/$USER/docker:/home/rstudio -p 3333:8787 -p 3334:3838 tiagochst/tcgabiolinksgui
/home/$USER/docker
to the correct system path. Examples can be found at this GitHub pageAn online version of the manual is available at bioconductor website.
To facilitate the use of this package, we have created some tutorial videos demonstrating the tool. Some sections have video tutorials that if clicked will redirect to the video on youtube. For the complete list of videos, please check this youtube list.
For each section we created some PDFs with detailing the steps of each section: Link to folder with PDFs
Please use Github issues if you want to file bug reports or feature requests.
Menu | Sub-menu | Button | Data input |
---|---|---|---|
Clinical analysis | Profile Plot | Select file | A table with at least two categorical columns |
Clinical analysis | Survival Plot | Select file | A table with at least the following columns: days_to_death, days_to_last_followup and one column with a group |
Epigenetic analysis | Differential methylation analysis | Select data (.rda) | A summarizedExperiment object |
Epigenetic analysis | Volcano Plot | Select results | A csv file with the following pattern: DMR_results_GroupCol_group1_group2_pcut_1e-30_meancut_0.55.csv (Where GroupCol, group1, group2 are the names of the columns selected in the DMR steps. |
Epigenetic analysis | Heatmap plot | Select file | A summarizedExperiment object |
Epigenetic analysis | Heatmap plot | Select results | Same as Epigenetic analysis >Volcano Plot > Select results |
Epigenetic analysis | Mean DNA methylation | Select file | A summarizedExperiment object |
Transcriptomic Analysis | Volcano Plot | Select results | A csv file with the following pattern: DEA_results_GroupCol_group1_group2_pcut_1e-30_meancut_0.55.csv (Where GroupCol, group1, group2 are the names of the columns selected in the DEA steps. |
Transcriptomic Analysis | Heatmap plot | Select file | A summarizedExperiment object |
Transcriptomic Analysis | OncoPrint plot | Select MAF file | A MAF file (columns needed: Hugo_Symbol,Tumor_Sample_Barcode,Variant_Type) |
Transcriptomic Analysis | OncoPrint plot | Select Annotation file | A file with at least the following columns: bcr_patient_barcode |
Integrative analysis | Starburst plot | DMR result | A csv file with the following pattern: DMR_results_GroupCol_group1_group2_pcut_1e-30_meancut_0.55.csv (Where GroupCol, group1, group2 are the names of the columns selected in the DMR steps. |
Integrative analysis | Starburst plot | DEA result | A csv file with the following pattern: DEA_results_GroupCol_group1_group2_pcut_1e-30_meancut_0.55.csv (Where GroupCol, group1, group2 are the names of the columns selected in the DEA steps. |
Integrative analysis | ELMER | Create mee > Select DNA methylation object | An rda file with a summarized Experiment object |
Integrative analysis | ELMER | Select results > Select expression object | An rda file with the RNAseq data frame |
Integrative analysis | ELMER | Select mee | An rda file with a mee object |
Integrative analysis | ELMER | Select results | An rda file with the results of the ELMER analysis |
Please cite both TCGAbiolinks package and TCGAbiolinksGUI:
Other related publications to this package:
If you used ELMER please cite:
If you used OncoPrint plot and Heatmap Plot please cite:
If you used Pathway plot please cite:
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Ceccarelli, Michele, FlorisP. Barthel, TathianeM. Malta, ThaisS. Sabedot, SofieR. Salama, BradleyA. Murray, Olena Morozova, et al. 2016. “Molecular Profiling Reveals Biologically Discrete Subsets and Pathways of Progression in Diffuse Glioma.” Cell 164 (3): 550–63. doi:http://dx.doi.org/10.1016/j.cell.2015.12.028.
Colaprico, Antonio, Tiago C. Silva, Catharina Olsen, Luciano Garofano, Claudia Cava, Davide Garolini, Thais S. Sabedot, et al. 2016. “TCGAbiolinks: An R/Bioconductor Package for Integrative Analysis of Tcga Data.” Nucleic Acids Research 44 (8): e71. doi:10.1093/nar/gkv1507.
Davis, Caleb F, Christopher J Ricketts, Min Wang, Lixing Yang, Andrew D Cherniack, Hui Shen, Christian Buhay, et al. 2014. “The Somatic Genomic Landscape of Chromophobe Renal Cell Carcinoma.” Cancer Cell 26 (3). Elsevier: 319–30.
Faith, Jeremiah J, Boris Hayete, Joshua T Thaden, Ilaria Mogno, Jamey Wierzbowski, Guillaume Cottarel, Simon Kasif, James J Collins, and Timothy S Gardner. 2007. “Large-Scale Mapping and Validation of Escherichia Coli Transcriptional Regulation from a Compendium of Expression Profiles.” PLoS Biol 5 (1): e8.
Giorgio, Elisa, Daniel Robyr, Malte Spielmann, Enza Ferrero, Eleonora Di Gregorio, Daniele Imperiale, Giovanna Vaula, et al. 2015. “A Large Genomic Deletion Leads to Enhancer Adoption by the Lamin B1 Gene: A Second Path to Autosomal Dominant Leukodystrophy (Adld).” Human Molecular Genetics. Oxford Univ Press, ddv065.
Gröschel, Stefan, Mathijs A Sanders, Remco Hoogenboezem, Elzo de Wit, Britta AM Bouwman, Claudia Erpelinck, Vincent HJ van der Velden, et al. 2014. “A Single Oncogenic Enhancer Rearrangement Causes Concomitant Evi1 and Gata2 Deregulation in Leukemia.” Cell 157 (2). Elsevier: 369–81.
Gu, Zuguang, Roland Eils, and Matthias Schlesner. 2016. “Complex Heatmaps Reveal Patterns and Correlations in Multidimensional Genomic Data.” Bioinformatics. doi:10.1093/bioinformatics/btw313.
Huber, Wolfgang, Vincent J Carey, Robert Gentleman, Simon Anders, Marc Carlson, Benilton S Carvalho, Hector Corrada Bravo, et al. 2015. “Orchestrating High-Throughput Genomic Analysis with Bioconductor.” Nature Methods 12 (2). Nature Publishing Group: 115–21.
Luo, Weijun, and Cory Brouwer. 2013. “Pathview: An R/Bioconductor Package for Pathway-Based Data Integration and Visualization.” Bioinformatics 29 (14). Oxford Univ Press: 1830–1.
Margolin, A.A., I. Nemenman, and K. Basso et al. 2006. “ARACNE: An Algorithm for the Reconstruction of Gene Regulatory Networks in a Mammalian Cellular Context.” BMC Bioinformatics 7.
Meyer, Patrick E, Kevin Kontos, Frederic Lafitte, and Gianluca Bontempi. 2007. “Information-Theoretic Inference of Large Transcriptional Regulatory Networks.” EURASIP Journal on Bioinformatics and Systems Biology 2007. Hindawi Publishing Corp.: 8–8.
Meyer, Patrick E., Frédéric Lafitte, and Gianluca Bontempi. 2008. “Minet: A R/Bioconductor Package for Inferring Large Transcriptional Networks Using Mutual Information.” BMC Bioinformatics 9 (1): 1–10. doi:10.1186/1471-2105-9-461.
Network, Cancer Genome Atlas Research, and others. 2012. “Comprehensive Genomic Characterization of Squamous Cell Lung Cancers.” Nature 489 (7417). Nature Publishing Group: 519–25.
———. 2013a. “Comprehensive Molecular Characterization of Clear Cell Renal Cell Carcinoma.” Nature 499 (7456). Nature Publishing Group: 43–49.
———. 2013b. “Integrated Genomic Characterization of Endometrial Carcinoma.” Nature 497 (7447). Nature Research: 67–73.
———. 2014a. “Comprehensive Molecular Profiling of Lung Adenocarcinoma.” Nature 511 (7511). Nature Publishing Group: 543–50.
———. 2014b. “Integrated Genomic Characterization of Papillary Thyroid Carcinoma.” Cell 159 (3). Elsevier: 676–90.
———. 2015. “The Molecular Taxonomy of Primary Prostate Cancer.” Cell 163 (4). Elsevier: 1011–25.
———. 2016. “Comprehensive Molecular Characterization of Papillary Renal-Cell Carcinoma.” N Engl J Med 2016 (374). Mass Medical Soc: 135–45.
Network, Cancer Genome Atlas, and others. 2012a. “Comprehensive Molecular Characterization of Human Colon and Rectal Cancer.” Nature 487 (7407). Nature Publishing Group: 330–37.
———. 2012b. “Comprehensive Molecular Characterization of Human Colon and Rectal Cancer.” Nature 487 (7407). Nature Publishing Group: 330–37.
———. 2012c. “Comprehensive Molecular Portraits of Human Breast Tumours.” Nature 490 (7418). Nature Publishing Group: 61–70.
———. 2015a. “Comprehensive Genomic Characterization of Head and Neck Squamous Cell Carcinomas.” Nature 517 (7536). Nature Publishing Group: 576–82.
———. 2015b. “Genomic Classification of Cutaneous Melanoma.” Cell 161 (7). Elsevier: 1681–96.
Risso, Davide, Katja Schwartz, Gavin Sherlock, and Sandrine Dudoit. 2011. “GC-Content Normalization for Rna-Seq Data.” BMC Bioinformatics 12 (1). BioMed Central Ltd: 480.
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Yao, L, H Shen, PW Laird, PJ Farnham, and BP Berman. 2015. “Inferring Regulatory Element Landscapes and Transcription Factor Networks from Cancer Methylomes.” Genome Biology 16 (1): 105–5.
Yao, Lijing, Benjamin P Berman, and Peggy J Farnham. 2015. “Demystifying the Secret Mission of Enhancers: Linking Distal Regulatory Elements to Target Genes.” Critical Reviews in Biochemistry and Molecular Biology 50 (6). Taylor & Francis: 550–73.