| title | author | date | output | abstract | vignette | ||||
|---|---|---|---|---|---|---|---|---|---|
The GenomicDataCommons Package |
Sean Davis & Martin Morgan |
`r format(Sys.Date(), '%A, %B %d, %Y')` |
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The National Cancer Institute (NCI) has established the [Genomic Data Commons](https://gdc.nci.nih.gov/) (GDC). The GDC provides the cancer research community with an open and unified repository for sharing and accessing data across numerous cancer studies and projects via a high-performance data transfer and query infrastructure. The *GenomicDataCommons* Bioconductor package provides basic infrastructure for querying, accessing, and mining genomic datasets available from the GDC. We expect that the Bioconductor developer and the larger bioinformatics communities will build on the *GenomicDataCommons* package to add higher-level functionality and expose cancer genomics data to the plethora of state-of-the-art bioinformatics methods available in Bioconductor.
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%\VignetteIndexEntry{Introduction to Accessing the NCI Genomic Data Commons} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8}
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library(knitr)
From the Genomic Data Commons (GDC) website:
The National Cancer Institute's (NCI's) Genomic Data Commons (GDC) is a data sharing platform that promotes precision medicine in oncology. It is not just a database or a tool; it is an expandable knowledge network supporting the import and standardization of genomic and clinical data from cancer research programs.
The GDC contains NCI-generated data from some of the largest and most comprehensive cancer genomic datasets, including The Cancer Genome Atlas (TCGA) and Therapeutically Applicable Research to Generate Effective Therapies (TARGET). For the first time, these datasets have been harmonized using a common set of bioinformatics pipelines, so that the data can be directly compared.
As a growing knowledge system for cancer, the GDC also enables researchers to submit data, and harmonizes these data for import into the GDC. As more researchers add clinical and genomic data to the GDC, it will become an even more powerful tool for making discoveries about the molecular basis of cancer that may lead to better care for patients.
The data model for the GDC is complex, but it worth a quick overview. The data model is encoded as a so-called property graph. Nodes represent entities such as Projects, Cases, Diagnoses, Files (various kinds), and Annotations. The relationships between these entities are maintained as edges. Both nodes and edges may have Properties that supply instance details. The GDC API exposes these nodes and edges in a somewhat simplified set of RESTful endpoints.
Installation is available from GitHub as of now.
source('https://bioconductor.org/biocLite.R')
biocLite('Bioconductor/GenomicDataCommons')
library(GenomicDataCommons)
GenomicDataCommons::status()
If this statement results in an error such as SSL connect error, you may need to
tell httr to ignore ssl verification or update your system SSL library and then
reinstall RCurl and httr. To disable SSL verification (not the preferred route,
but it is the quickest approach):
httr::set_config(httr::config(ssl_verifypeer=0L))
The following code builds a manifest that can be used to guide the
download of raw data. Here, filtering finds gene expression files
quantified as raw counts using HTSeq from ovarian cancer patients.
ge_manifest = files() %>%
filter( ~ cases.project.project_id == 'TCGA-OV' &
type == 'gene_expression' &
analysis.workflow_type == 'HTSeq - Counts') %>%
manifest()
This code block downloads the r nrow(ge_manifest) gene expression files specified in the query above. Using multiple processes to do the download very significantly speeds up the transfer in many cases. On a standard 1Gb connection using 10 processes, the following completes in about 15 seconds.
library(BiocParallel)
register(MulticoreParam())
destdir = tempdir()
fnames = bplapply(ge_manifest$id,gdcdata,
token=gdc_token(),destination_dir=destdir,
BPPARAM = MulticoreParam(progressbar=TRUE))
expands = c("diagnoses","annotations",
"demographic","exposures")
clinResults = cases() %>%
GenomicDataCommons::select(NULL) %>%
GenomicDataCommons::expand(expands) %>%
results(size=50)
clinDF = as.data.frame(clinResults)
library(DT)
datatable(clinDF, extensions = 'Scroller', options = list(
deferRender = TRUE,
scrollY = 200,
scrollX = TRUE,
scroller = TRUE
))
This package design is meant to have some similarities to the "hadleyverse" approach of dplyr. Roughly, the functionality for finding and accessing files and metadata can be divided into:
- Simple query constructors based on GDC API endpoints.
- A set of verbs that when applied, adjust filtering, field selection, and faceting (fields for aggregation) and result in a new query object (an endomorphism)
- A set of verbs that take a query and return results from the GDC
In addition, there are exhiliary functions for asking the GDC API for information about available and default fields, slicing BAM files, and downloading actual data files. Here is an overview of functionality1.
- Creating a query
projects()cases()files()annotations()
- Manipulating a query
filter()facet()select()
- Introspection on the GDC API fields
mapping()available_fields()default_fields()grep_fields()field_picker()available_values()available_expand()
- Executing an API call to retrieve query results
results()count()response()
- Raw data file downloads
gdcdata()transfer()gdc_client()
- Summarizing and aggregating field values (faceting)
aggregations()
- Authentication
gdc_token()
- BAM file slicing
slicing()
There are two main classes of operations when working with the NCI GDC.
- Querying metadata and finding data files (e.g., finding all gene expression quantifications data files for all colon cancer patients).
- Transferring raw or processed data from the GDC to another computer (e.g., downloading raw or processed data)
Both classes of operation are reviewed in detail in the following sections.
Vast amounts of metadata about cases (patients, basically), files, projects, and so-called annotations are available via the NCI GDC API. Typically, one will want to query metadata to either focus in on a set of files for download or transfer or to perform so-called aggregations (pivot-tables, facets, similar to the R table() functionality).
Querying metadata starts with creating a "blank" query. One will often then want to filter the query to limit results prior to retrieving results. The GenomicDataCommons package has helper functions for listing fields that are available for filtering.
In addition to fetching results, the GDC API allows faceting, or aggregating,, useful for compiling reports, generating dashboards, or building user interfaces to GDC data (see GDC web query interface for a non-R-based example).
A query of the GDC starts its life in R. Queries follow the four metadata endpoints available at the GDC. In particular, there are four convenience functions that each create GDCQuery objects (actually, specific subclasses of GDCQuery):
projects()cases()files()annotations()
pquery = projects()
The pquery object is now an object of (S3) class, GDCQuery (and gdc_projects and list). The object contains the following elements:
- fields: This is a character vector of the fields that will be returned when we retrieve data. If no fields are specified to, for example, the
projects()function, the default fields from the GDC are used (seedefault_fields()) - filters: This will contain results after calling the
filter()method and will be used to filter results on retrieval. - facets: A character vector of field names that will be used for aggregating data in a call to
aggregations(). - archive: One of either "default" or "legacy".
- token: A character(1) token from the GDC. See the authentication section for details, but note that, in general, the token is not necessary for metadata query and retrieval, only for actual data download.
Looking at the actual object (get used to using str()!), note that the query contains no results.
str(pquery)
[ GDC pagination documentation ]
With a query object available, the next step is to retrieve results from the GDC. The GenomicDataCommons package. The most basic type of results we can get is a simple count() of records available that satisfy the filter criteria. Note that we have not set any filters, so a count() here will represent all the project records publicly available at the GDC in the "default" archive"
pcount = count(pquery)
# or
pcount = pquery %>% count()
pcount
The results() method will fetch actual results.
presults = pquery %>% results()
These results are
returned from the GDC in JSON format and
converted into a (potentially nested) list in R. The str() method is useful for taking a quick glimpse of the data.
str(presults)
A default of only 10 records are returned. We can use the size and from arguments to results() to either page through results or to change the number of results. Finally, there is a convenience method, results_all() that will simply fetch all the available results given a query. Note that results_all() may take a long time and return HUGE result sets if not used carefully. Use of a combination of count() and results() to get a sense of the expected data size is probably warranted before calling results_all()
length(ids(presults))
presults = pquery %>% results_all()
length(ids(presults))
# includes all records
length(ids(presults)) == count(pquery)
Extracting subsets of results or manipulating the results into a more conventional R data structure is not easily generalizable. However, the purrr, rlist, and data.tree packages are all potentially of interest for manipulating complex, nested list structures. For viewing the results in an interactive viewer, consider the listviewer package.
In the case of the projects entity, the default results (using default fields, that is) can be simplified easily with as.data.frame.
head(as.data.frame(presults))
Central to querying and retrieving data from the GDC is the ability to specify which fields to return, filtering by fields and values, and faceting or aggregating. The GenomicDataCommons package includes two simple functions, available_fields() and default_fields(). Each can operate on a character(1) endpoint name ("cases", "files", "annotations", or "projects") or a GDCQuery object.
default_fields('files')
# The number of fields available for files endpoint
length(available_fields('files'))
# The first few fields available for files endpoint
head(available_fields('files'))
The fields to be returned by a query can be specified following a similar paradigm to that of the dplyr package. The select() function is a verb that resets the fields slot of a GDCQuery; note that this is not quite analogous to the dplyr select() verb that limits from already-present fields. We completely replace the fields when using select() on a GDCQuery.
# Default fields here
qcases = cases()
qcases$fields
# set up query to use ALL available fields
# Note that checking of fields is done by select()
qcases = cases() %>% GenomicDataCommons::select(available_fields('cases'))
head(qcases$fields)
Finding fields of interest is such a common operation that the GenomicDataCommons includes the grep_fields() function and the field_picker() widget. See the appropriate help pages for details.
The GDC API offers a feature known as aggregation or faceting. By
specifying one or more fields (of appropriate type), the GDC can
return to us a count of the number of records matching each potential
value. This is similar to the R table method. Multiple fields can be
returned at once, but the GDC API does not have a cross-tabulation
feature; all aggregations are only on one field at a time. Results of
aggregation() calls come back as a list of data.frames (actually,
tibbles).
# total number of files of a specific type
res = files() %>% facet(c('type','data_type')) %>% aggregations()
res$type
Using aggregations() is an also easy way to learn the contents of individual fields and forms the basis for faceted search pages.
[ GDC filtering documentation ]
The GenomicDataCommons package uses a form of non-standard evaluation to specify R-like queries that are then translated into an R list. That R list is, upon calling a method that fetches results from the GDC API, translated into the appropriate JSON string. The R expression uses the formula interface as suggested by Hadley Wickham in his vignette on non-standard evaluation
It’s best to use a formula because a formula captures both the expression to evaluate and the environment where the evaluation occurs. This is important if the expression is a mixture of variables in a data frame and objects in the local environment [for example].
For the user, these details will not be too important except to note that a filter expression must begin with a "~".
qfiles = files()
qfiles %>% count() # all files
To limit the file type, we can refer back to the section on faceting to see the possible values for the file field "type". For example, to filter file results to only "gene_expression" files, we simply specify a filter.
qfiles = files() %>% filter(~ type == 'gene_expression')
# here is what the filter looks like after translation
str(get_filter(qfiles))
What if we want to create a filter based on the project ('TCGA-OVCA', for example)? Well, we have a couple of possible ways to discover available fields. The first is based on base R functionality and some intuition.
grep('pro',available_fields('files'),value=TRUE)
Interestingly, the project information is "nested" inside the case. We don't need to know that detail other than to know that we now have a few potential guesses for where our information might be in the files records. We need to know where because we need to construct the appropriate filter.
files() %>% facet('cases.project.project_id') %>% aggregations()
We note that cases.project.project_id looks like it is a good fit. We also note that TCGA-OV is the correct project_id, not TCGA-OVCA. Note that unlike with dplyr and friends, the filter() method here replaces the filter and does not build on any previous filters.
qfiles = files() %>% filter( ~ cases.project.project_id == 'TCGA-OV' & type == 'gene_expression')
str(get_filter(qfiles))
qfiles %>% count()
Asking for a count() of results given these new filter criteria gives r qfiles %>% count() results. Generating a manifest for bulk downloads is as simple as asking for the manifest from the current query.
manifest_df = qfiles %>% manifest()
head(manifest_df)
Note that we might still not be quite there. Looking at filenames, there are suspiciously named files that might include "FPKM", "FPKM-UQ", or "counts". Another round of grep and available_fields, looking for "type" turned up that the field "analysis.workflow_type" has the appropriate filter criteria.
qfiles = files() %>% filter( ~ cases.project.project_id == 'TCGA-OV' &
type == 'gene_expression' &
analysis.workflow_type == 'HTSeq - Counts')
manifest_df = qfiles %>% manifest()
nrow(manifest_df)
The GDC Data Transfer Tool can be used (from R, transfer() or from the command-line) to orchestrate high-performance, restartable transfers of all the files in the manifest. See the bulk downloads section for details.
[ GDC authentication documentation ]
The GDC offers both "controlled-access" and "open" data. As of this writing, only data stored as files is "controlled-access"; that is, metadata accessible via the GDC is all "open" data and some files are "open" and some are "controlled-access". Controlled-access data are only available after going through the process of obtaining access.
After controlled-access to one or more datasets has been granted, logging into the GDC web portal will allow you to access a GDC authentication token, which can be downloaded and then used to access available controlled-access data via the GenomicDataCommons package.
The GenomicDataCommons uses authentication tokens only for downloading
data (see transfer and gdcdata documentation). The package
includes a helper function, gdc_token, that looks for the token to
be stored in one of three ways (resolved in this order):
- As a string stored in the environment variable,
GDC_TOKEN - As a file, stored in the file named by the environment variable,
GDC_TOKEN_FILE - In a file in the user home directory, called
.gdc_token
As a concrete example:
token = gdc_token()
transfer(...,token=token)
# or
transfer(...,token=get_token())
The gdcdata function takes a character vector of one or more file
ids. A simple way of producing such a vector is to produce a
manifest data frame and then pass in the first column, which will
contain file ids.
fnames = gdcdata(manifest_df$id[1:2],progress=FALSE)
Note that for controlled-access data, a
GDC authentication token is required. Using the
BiocParallel package may be useful for downloading in parallel,
particularly for large numbers of smallish files.
The bulk download functionality is only efficient (as of v1.2.0 of the GDC Data Transfer Tool) for relatively large files, so use this approach only when transferring BAM files or larger VCF files, for example. Otherwise, consider using the approach shown above, perhaps in parallel.
mfile = tempfile()
write.table(manifest_df[1:50,],mfile,
col.names=TRUE, row.names=FALSE, quote=FALSE,sep="\t")
transfer(mfile,gdc_client='gdc-client')
res = cases() %>% facet("project.project_id") %>% aggregations()
head(res)
library(ggplot2)
ggplot(res$project.project_id,aes(x = key, y = doc_count)) +
geom_bar(stat='identity') +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
cases() %>% filter(~ project.program.name=='TARGET') %>% count()
cases() %>% filter(~ project.program.name=='TCGA') %>% count()
# The need to do the "&" here is a requirement of the
# current version of the GDC API. I have filed a feature
# request to remove this requirement.
resp = cases() %>% filter(~ project.project_id=='TCGA-BRCA' &
project.project_id=='TCGA-BRCA' ) %>%
facet('samples.sample_type') %>% aggregations()
resp$samples.sample_type
# The need to do the "&" here is a requirement of the
# current version of the GDC API. I have filed a feature
# request to remove this requirement.
resp = cases() %>% filter(~ project.project_id=='TCGA-BRCA' &
samples.sample_type=='Solid Tissue Normal') %>%
GenomicDataCommons::select(c(default_fields(cases()),'samples.sample_type')) %>%
response_all()
count(resp)
res = resp %>% results()
str(res[1],list.len=6)
head(ids(resp))
res = files() %>% facet(c('type','data_type')) %>% aggregations()
res$type
ggplot(res$type,aes(x = key,y = doc_count)) + geom_bar(stat='identity') +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
res = files() %>% facet('type') %>% aggregations()
res$type
ggplot(res$type,aes(x = key,y = doc_count)) + geom_bar(stat='identity') +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
q = files() %>%
GenomicDataCommons::select(available_fields('files')) %>%
filter(~ cases.project.project_id=='TCGA-GBM' &
data_type=='Gene Expression Quantification')
q %>% facet('analysis.workflow_type') %>% aggregations()
# so need to add another filter
file_ids = q %>% filter(~ cases.project.project_id=='TCGA-GBM' &
data_type=='Gene Expression Quantification' &
analysis.workflow_type == 'HTSeq - Counts') %>%
GenomicDataCommons::select('file_id') %>%
response_all() %>%
ids()
I need to figure out how to do slicing reproducibly in a testing environment and for vignette building.
q = files() %>%
GenomicDataCommons::select(available_fields('files')) %>%
filter(~ cases.project.project_id == 'TCGA-GBM' &
data_type == 'Aligned Reads' &
experimental_strategy == 'RNA-Seq' &
data_format == 'BAM')
file_ids = q %>% response_all() %>% ids()
bamfile = slicing(file_ids[1],regions="chr12:6534405-6538375",token=gdc_token())
library(GenomicAlignments)
aligns = readGAlignments(bamfile)
sessionInfo()
- The
S3object-oriented programming paradigm is used. - We have adopted a functional programming style with functions and methods that often take an "object" as the first argument. This style lends itself to pipeline-style programming.
- The GenomicDataCommons package uses the alternative request format (POST) to allow very large request bodies.
Footnotes
-
See individual function and methods documentation for specific details. ↩