---
bibliography: bibliography.bib
date: "`r BiocStyle::doc_date()`"
vignette: >
%\VignetteIndexEntry{"2. Data menu"}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r message=FALSE, warning=FALSE, include=FALSE}
library(TCGAbiolinks)
library(SummarizedExperiment)
library(dplyr)
library(DT)
```
# Detailed explanation: Get GDC data menu
For a detailed manual for this section please access this link:
1. [Data menu manual](https://drive.google.com/file/d/0B0-8N2fjttG-X1BoNlNnUnpVMWc/view?usp=sharing)
# 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.
## Useful information
Different sources: Legacy vs Harmonized
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](https://cghub.ucsc.edu/) 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.
Understanding the barcode
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](https://wiki.nci.nih.gov/display/TCGA/TCGA+barcode)
## 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.
Repeated cases (barcodes)
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".
## 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](http://www.nature.com/nmeth/journal/v12/n2/abs/nmeth.3252.html)
[@huber2015orchestrating]: 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.
# Sub-menu: Subtype data
The Cancer Genome Atlas (TCGA) Research Network has reported integrated genome-wide
studies of various diseases. We have added some of the subtypes defined by these
reports in our package.
- [ACC](doi:10.1016/j.ccell.2016.04.002)[@zheng2016comprehensive]
- [BRCA](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3465532/)[@cancer2012comprehensive_brca]
- [COAD](http://www.nature.com/nature/journal/v487/n7407/abs/nature11252.html) [@cancer2012comprehensive_colon]
- [GBM](http://dx.doi.org/10.1016/j.cell.2015.12.028) [@Cell]
- [HNSC](http://www.nature.com/nature/journal/v517/n7536/abs/nature14129.html) [@cancer2015comprehensive]
- [KICH](http://www.sciencedirect.com/science/article/pii/S1535610814003043) [@davis2014somatic]
- [KIRC](http://www.nature.com/nature/journal/v499/n7456/abs/nature12222.html)[@cancer2013comprehensive]
- [KIRP](http://www.nejm.org/doi/full/10.1056/NEJMoa1505917) [@cancer2016comprehensive]
- [LGG](http://dx.doi.org/10.1016/j.cell.2015.12.028)[@Cell]
- [LUAD](http://www.nature.com/nature/journal/v511/n7511/abs/nature13385.html) [@cancer2014comprehensive]
- [LUSC](http://www.nature.com/nature/journal/v489/n7417/abs/nature11404.html)[@cancer2012comprehensive_lusc]
- [PCPG](http://dx.doi.org/10.1016/j.ccell.2017.01.001)[@fishbein2017comprehensive]
- [PRAD](http://www.sciencedirect.com/science/article/pii/S0092867415013392)[@cancer2015molecular_prad]
- [READ](http://www.nature.com/nature/journal/v487/n7407/abs/nature11252.html) [@cancer2012comprehensive]
- [SKCM](http://www.sciencedirect.com/science/article/pii/S0092867415006340) [@cancer2015genomic_skcm]
- [STAD](http://www.nature.com/nature/journal/v511/n7511/abs/nature13385.html) [@cancer2014comprehensive]
- [THCA](http://www.sciencedirect.com/science/article/pii/S0092867414012380) [@cancer2014integrated]
- [UCEC](http://www.nature.com/nature/journal/v497/n7447/abs/nature12113.html) [@cancer2013integrated]
The user can visualize the table with the information and save it as csv or R object (rda) file.
# Sub-menu: Clinical data
TCGAbiolinks has provided a few functions to search, download and parse GDC clinical data.
## Useful information
Different sources
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:
## Clinical data inconsistencies
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
### Vital status inconsistencies
```{r results = 'hide', echo=TRUE, message=FALSE, warning=FALSE}
# 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")
```
```{r echo = TRUE, message = FALSE, warning = FALSE}
select(clinical.patient,vital_status,days_to_death,days_to_last_followup) %>% datatable
select(clinical.patient.followup, vital_status,days_to_death,days_to_last_followup) %>% datatable
# Vital status should be the same in the follow up table
filter(clinical.index,submitter_id == "TCGA-F5-6702") %>% select(vital_status,days_to_death,days_to_last_follow_up) %>% datatable
```
### Progression or Recurrence and Grade inconsistencies
```{r results = 'hide', echo=TRUE, message=FALSE, warning=FALSE}
# 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")
```
```{r echo = TRUE, message = FALSE, warning = FALSE}
# Get indexed data
GDCquery_clinic("TCGA-LIHC") %>% filter(submitter_id %in% recurrent.patients) %>%
select(progression_or_recurrence,days_to_recurrence,tumor_grade) %>% datatable
# XML data
clinical.patient %>% select(bcr_patient_barcode,neoplasm_histologic_grade) %>% datatable
```
## Tutorial Video
[](http://www.youtube.com/watch?v=54NBug9ycwM "Tutorial Video: Fetching GDC clinical data - (http://www.youtube.com/watch?v=54NBug9ycwM)")
**Tutorial Video:** Fetching GDC clinical data - (http://www.youtube.com/watch?v=54NBug9ycwM)
# Sub-menu: Mutation data
In order to download the Mutation Annotation Format (MAF), we provide the user
with an interface to the `GDCquery_maf` function. Briefly, it will download the open access MAF files from
[https://gdc-docs.nci.nih.gov/Data/Release_Notes/Data_Release_Notes/](https://gdc-docs.nci.nih.gov/Data/Release_Notes/Data_Release_Notes/).
Four separate variant calling pipelines are implemented for GDC data harmonization which are described [here](https://gdc-docs.nci.nih.gov/Data/Bioinformatics_Pipelines/DNA_Seq_Variant_Calling_Pipeline/).
## Mutation data search box
The available filters are:
* Tumor filter: a TCGA tumor, such as Breast invasive Carcinoma (TCGA-BRCA)
* Variant calling pipelines: Four separate variant calling pipelines are implemented for GDC data harmonization. Options: muse, varscan2, somaticsniper, mutect.
# 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.
[](http://www.youtube.com/watch?v=54rP_yZlpQs "Tutorial Video: Managing a SummarizedExperiment object - (http://www.youtube.com/watch?v=54NBug9ycwM)")
**Tutorial Video:** Managing a SummarizedExperiment object - (http://www.youtube.com/watch?v=54NBug9ycwM)
# Detailed explanation: Manage SummarizedExperiment menu
For a detailed manual for this section please access this link: [Manage SummarizedExperiment](https://drive.google.com/file/d/0B0-8N2fjttG-TVVOcTNFRlh3YXM/view?usp=sharing)
# References