2 Sub-menu: Molecular data
In this sub-menu, the user will be able to search, download and prepare The NCI’s Genomic Data Commons (GDC) data for analysis. The first step is to search for GDC open data access with the Advanced search box. The search results will be summarized in plots in the center of the screen. The second step is to download and prepare the data into an R object to be used in the analysis steps.
GDC search menu: Main window.
2.1 Useful information
There are two available sources to download GDC data using TCGAbiolinks: - GDC Legacy Archive: provides access to an unmodified copy of data that was previously stored in CGHub and in the TCGA Data Portal hosted by the TCGA Data Coordinating Center (DCC), in which uses as references GRCh37 (hg19) and GRCh36 (hg18). - GDC harmonized database: data available was harmonized against GRCh38 (hg38) using GDC Bioinformatics Pipelines which provides methods to the standardization of biospecimen and clinical data.
A TCGA barcode is composed of a collection of identifiers. Each specifically identifies a TCGA data element. Refer to the following figure for an illustration of how metadata identifiers comprise a barcode. An aliquot barcode contains the highest number of identifiers.
Example:
- Aliquot barcode: TCGA-G4-6317-02A-11D-2064-05
- Participant: TCGA-G4-6317
- Sample: TCGA-G4-6317-02
For more information check TCGA wiki
2.2 Advanced search box
The following filters are available to select the data:
- Database
- Project filter
- Data category filter
- Data type filter
- Workflow filter
- Sample type filter
- Platform filter
- List of barcodes
- Clinical filter: gender, race, vital status, tumor stage.
In the search step, it is important that there are no repeated cases, otherwise, the prepare step might not work. If it happens, the search will give you this warning “There are more than one file for the same case.” To solve this problem, you probably need to use more filters to correctly select your data.
As an example of this problem, if you use only Project (i.e “TCGA-ACC”) and Data category filter (i.e “Copy Number Variation”), for each sample we have two data types “Copy Number segment”" and “Masked Copy Number segment”.
2.3 Download & Prepare box
After the search step, the user can download the data and prepare it into an R object. The data can be prepared into one of two formats:
- SummarizedExperiment (Huber et al. 2015): An R object that contains the sample information matrix (clinical and subtype information), the assay matrix (i.e DNA methylation beta-values), the features matrix (i.e information for each probe in the DNA methylation assay matrix).
- Data Frame: A simple table with only the assay matrix (i.e Gene expression quantification)
In case a SummarizedExperiment object was selected the user will be able to add more metadata information into the sample information matrix. By checking “Add gistic2 and mutation information” and giving a list of genes, the GUI will download GISTIC2 data from the GDAC Firehose and Mutation Annotation Files (MAF) and their information to the object. This might be useful, for example, if the user wants to evaluate the survival of mutant samples vs non-mutant or if he wants to evaluate it on a heatmap, which can be used as labels.
Finally, the object will be saved with the same name as the file name field.
4 Sub-menu: Clinical data
TCGAbiolinks has provided a few functions to search, download and parse GDC clinical data.
4.1 Useful information
In GDC database the clinical data can be retrieved from two sources:
- indexed clinical: a refined clinical data that is created using the XML files.
- XML files
There are two main differences:
- XML has more information: radiation, drugs information, follow-ups, biospecimen, etc. So the indexed one is only a subset of the XML files
- The indexed data contains the updated data with the follow-up information. For example: if the patient is alive in the first time clinical data was collect and the in the next follow-up he is dead, the indexed data will show dead. The XML will have two fields, one for the first time saying he is alive (in the clinical part) and the follow-up saying he is dead. You can see this case here:
4.2 Clinical data inconsistencies
Some inconsistencies have been found in the indexed clinical data and are being investigated by the GDC team. These inconsistencies are:
- Vital status field is not correctly updated
- Tumor Grade field is not being filled
- Progression or Recurrence field is not being filled
4.2.1 Vital status inconsistencies
# Get XML files and parse them
clin.query <- GDCquery(project = "TCGA-READ",
data.category = "Clinical",
barcode = "TCGA-F5-6702")
GDCdownload(clin.query)
clinical.patient <- GDCprepare_clinic(clin.query, clinical.info = "patient")
clinical.patient.followup <- GDCprepare_clinic(clin.query, clinical.info = "follow_up")
# Get indexed data
clinical.index <- GDCquery_clinic("TCGA-READ")4.2.2 Progression or Recurrence and Grade inconsistencies
# Get XML files and parse them
recurrent.samples <- GDCquery(project = "TCGA-LIHC",
data.category = "Transcriptome Profiling",
data.type = "Gene Expression Quantification",
workflow.type = "HTSeq - Counts",
sample.type = "Recurrent Solid Tumor")$results[[1]] %>% select(cases)
recurrent.patients <- unique(substr(recurrent.samples$cases,1,12))
clin.query <- GDCquery(project = "TCGA-LIHC",
data.category = "Clinical",
barcode = recurrent.patients)
GDCdownload(clin.query)
clinical.patient <- GDCprepare_clinic(clin.query, clinical.info = "patient") 4.3 Tutorial Video
6 Menu: Manage summarized Experiment object
To facilitate visualization and modification of the SummarizedExperiment object, we created this menu in which it is possible to visualize the three matrices of the object (assay matrix [i.e. gene expression values], features matrix [i.e. gene information] and sample information matrix). Also, it is possible to download the sample information matrix as a CSV file, and, after modifying it, to upload and update the SummarizedExperiment object. This might be useful if, for example, the user wants to compare two groups not already pre-defined.
8 Menu: Processing raw data
8.1 Sub-menu: DNA methylation
For DNA methylation raw data obtained in the form of Intensity Data (IDAT) files, we provide a pipeline using the R/Bioconductor minfi package (http://bioconductor.org/packages/minfi/) to process the data for downstream analysis (Aryee et al. 2014) performing a background and dye-bias correction with the preprocessnoob function followed by a detection P-value quality masking (sample specific) (Morris and Beck 2015) and probes overlapping repeats or single nucleotide polymorphisms masking (non-sample specific) (Zhou, Laird, and Shen 2016).
IDAT normalization menu: Table shows files identified that will be processed. Data retrieved from GEO (accession GSE61160).
8.1.1 Detailed explanation: IDAT processing and glioma classifier
For a detailed manual for this section please access this link: IDAT processing
References
Aryee, Martin J, Andrew E Jaffe, Hector Corrada-Bravo, Christine Ladd-Acosta, Andrew P Feinberg, Kasper D Hansen, and Rafael A Irizarry. 2014. “Minfi: A Flexible and Comprehensive Bioconductor Package for the Analysis of Infinium Dna Methylation Microarrays.” Bioinformatics 30 (10). Oxford University Press:1363–9.
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.
Morris, Tiffany J, and Stephan Beck. 2015. “Analysis Pipelines and Packages for Infinium Humanmethylation450 Beadchip (450k) Data.” Methods 72. Elsevier:3–8.
Zhou, Wanding, Peter W Laird, and Hui Shen. 2016. “Comprehensive Characterization, Annotation and Innovative Use of Infinium Dna Methylation Beadchip Probes.” Nucleic Acids Research. Oxford Univ Press, gkw967.