Package 'GenomicDataCommons'

aggregations

Description

aggregations

Usage

aggregations(x)

## S3 method for class 'GDCQuery'
aggregations(x)

## S3 method for class 'GDCResponse'
aggregations(x)

Arguments

x	a GDCQuery object

Value

a list of data.frame with one member for each requested facet. The data frames each have two columns, key and doc_count.

Methods (by class)

• aggregations(GDCQuery):

• aggregations(GDCResponse):

Examples

# Number of each file type
res = files() |> facet(c('type','data_type')) |> aggregations()
res$type

---

Return valid values for "expand"

Description

The GDC allows a shorthand for specifying groups of fields to be returned by the metadata queries. These can be specified in a select method call to easily supply groups of fields.

Usage

available_expand(entity)

## S3 method for class 'character'
available_expand(entity)

## S3 method for class 'GDCQuery'
available_expand(entity)

Arguments

entity

Either a GDCQuery object or a character(1) specifying a GDC entity ('cases', 'files', 'annotations', 'projects')

Value

A character vector

See Also

See https://docs.gdc.cancer.gov/API/Users_Guide/Search_and_Retrieval/#expand for details

Examples

head(available_expand('files'))

---

S3 Generic to return all GDC fields

Description

S3 Generic to return all GDC fields

Usage

available_fields(x)

## S3 method for class 'GDCQuery'
available_fields(x)

## S3 method for class 'character'
available_fields(x)

Arguments

x	A character(1) string ('cases','files','projects', 'annotations') or an subclass of GDCQuery.

Value

a character vector of the default fields

Methods (by class)

• available_fields(GDCQuery): GDCQuery method

• available_fields(character): character method

Examples

available_fields('projects')
projQuery = query('projects')
available_fields(projQuery)

---

Find common values for a GDC field

Description

Find common values for a GDC field

Usage

available_values(entity, field)

Arguments

entity	character(1), a GDC entity ("cases", "files", "annotations", "projects")
field	character(1), a field that is present in the entity record

Value

character vector of the top 100 (or fewer) most frequent values for a the given field

Examples

available_values('files','cases.project.project_id')[1:5]

---

provide count of records in a GDCQuery

Description

provide count of records in a GDCQuery

Usage

count(x, ...)

## S3 method for class 'GDCQuery'
count(x, ...)

## S3 method for class 'GDCResponse'
count(x, ...)

Arguments

x	a GDCQuery object
...	passed to httr (good for passing config info, etc.)

Value

integer(1) representing the count of records that will be returned by the current query

Methods (by class)

• count(GDCQuery):

• count(GDCResponse):

Examples

# total number of projects
projects() |> count()

# total number of cases
cases() |> count()

---

S3 Generic to return default GDC fields

Description

S3 Generic to return default GDC fields

Usage

default_fields(x)

## S3 method for class 'character'
default_fields(x)

## S3 method for class 'GDCQuery'
default_fields(x)

Arguments

x	A character string ('cases','files','projects', 'annotations') or an subclass of GDCQuery.

Value

a character vector of the default fields

Methods (by class)

• default_fields(character): character method

• default_fields(GDCQuery): GDCQuery method

Examples

default_fields('projects')
projQuery = query('projects')
default_fields(projQuery)

---

Get the entity name from a GDCQuery object

Description

An "entity" is simply one of the four medata endpoints.

• cases

• projects

• files

• annotations

All GDCQuery objects will have an entity name. This S3 method is simply a utility accessor for those names.

Usage

entity_name(x)

## S3 method for class 'GDCQuery'
entity_name(x)

## S3 method for class 'GDCResults'
entity_name(x)

Arguments

x	a GDCQuery object

Value

character(1) name of an associated entity; one of "cases", "files", "projects", "annotations".

Examples

qcases = cases()
qprojects = projects()

entity_name(qcases)
entity_name(qprojects)

---

Set the expand parameter

Description

S3 generic to set GDCQuery expand parameter

Usage

expand(x, expand)

## S3 method for class 'GDCQuery'
expand(x, expand)

Arguments

x	the objects on which to set fields
expand	a character vector specifying the fields

Value

A GDCQuery object, with the expand member altered.

Methods (by class)

• expand(GDCQuery): set expand fields on a GDCQuery object

Examples

gProj = projects()
gProj$fields
head(available_fields(gProj))
default_fields(gProj)

gProj |>
  select(default_fields(gProj)[1:2]) |>
  response() |>
  str(max_level=2)

---

Set facets for a GDCQuery

Description

Set facets for a GDCQuery

Get facets for a GDCQuery

Usage

facet(x, facets)

get_facets(x)

## S3 method for class 'GDCQuery'
get_facets(x)

Arguments

x	a GDCQuery object
facets	a character vector of fields that will be used for forming aggregations (facets). Default is to set facets for all default fields. See default_fields for details

Value

returns a GDCQuery object, with facets field updated.

Examples

# create a new GDCQuery against the projects endpoint
gProj = projects()

# default facets are NULL
get_facets(gProj)

# set facets and save result
gProjFacet = facet(gProj)

# check facets
get_facets(gProjFacet)

# and get a response, noting that
# the aggregations list member contains
# tibbles for each facet
str(response(gProjFacet,size=2),max.level=2)

---

S3 Generic that returns the field description text, if available

Description

S3 Generic that returns the field description text, if available

Usage

field_description(entity, field)

## S3 method for class 'GDCQuery'
field_description(entity, field)

## S3 method for class 'character'
field_description(entity, field)

Arguments

entity	character(1) string ('cases','files','projects', 'annotations', etc.) or an subclass of GDCQuery.
field	character(1), the name of the field that will be used to look up the description.

Value

character(1) descriptive text or character(0) if no description is available.

Methods (by class)

• field_description(GDCQuery): GDCQuery method

• field_description(character): character method

Examples

field_description('cases', 'annotations.category')
casesQuery = query('cases')
field_description(casesQuery, 'annotations.category')
field_description(cases(), 'annotations.category')

---

Manipulating GDCQuery filters

Description

Manipulating GDCQuery filters

The filter is simply a safe accessor for the filter element in GDCQuery objects.

The get_filter is simply a safe accessor for the filter element in GDCQuery objects.

Usage

filter(x, expr)

## S3 method for class 'GDCQuery'
filter(x, expr)

get_filter(x)

## S3 method for class 'GDCQuery'
get_filter(x)

Arguments

x	the object on which to set the filter list member
expr	a filter expression in the form of the right hand side of a formula, where bare names (without quotes) are allowed if they are available fields associated with the GDCQuery object, x

Value

A GDCQuery object with the filter field replaced by specified filter expression

Examples

# make a GDCQuery object to start
#
# Projects
#
pQuery = projects()

# check for the default fields
# so that we can use one of them to build a filter
default_fields(pQuery)
pQuery = filter(pQuery,~ project_id == 'TCGA-LUAC')
get_filter(pQuery)

#
# Files
#
fQuery = files()
default_fields(fQuery)

fQuery = filter(fQuery,~ data_format == 'VCF')
# OR
# with recent GenomicDataCommons versions:
#   no "~" needed
fQuery = filter(fQuery, data_format == 'VCF')

get_filter(fQuery)

fQuery = filter(fQuery,~ data_format == 'VCF'
                & experimental_strategy == 'WXS'
                & type == 'simple_somatic_mutation')

files() |> filter(~ data_format == 'VCF'
                   & experimental_strategy=='WXS'
                   & type == 'simple_somatic_mutation') |> count()
                   
                   
files() |> filter( data_format == 'VCF'
                   & experimental_strategy=='WXS'
                   & type == 'simple_somatic_mutation') |> count()

# Filters may be chained for the 
# equivalent query
# 
# When chained, filters are combined with logical AND

files() |>
  filter(~ data_format == 'VCF') |>
  filter(~ experimental_strategy == 'WXS') |>
  filter(~ type == 'simple_somatic_mutation') |>
  count()

# OR

files() |>
  filter( data_format == 'VCF') |>
  filter( experimental_strategy == 'WXS') |>
  filter( type == 'simple_somatic_mutation') |>
  count()

# Use str() to get a cleaner picture
str(get_filter(fQuery))

---

Work with gdc cache directory

Description

The GenomicDataCommons package will cache downloaded files to minimize network and allow for offline work. These functions are used to create a cache directory if one does not exist, set a global option, and query that option. The cache directory will default to the user "cache" directory according to specifications in app_dir. However, the user may want to set this to another direcotory with more or higher performance storage.

Usage

gdc_cache()

gdc_set_cache(
  directory = rappdirs::app_dir(appname = "GenomicDataCommons")$cache(),
  verbose = TRUE,
  create_without_asking = !interactive()
)

Arguments

directory	character(1) directory path, will be created recursively if not present.
verbose	logical(1) whether or not to message the location of the cache directory after creation.
create_without_asking	logical(1) specifying whether to allow the function to create the cache directory without asking the user first. In an interactive session, if the cache directory does not exist, the user will be prompted before creation.

Details

The cache structure is currently just a directory with each file being represented by a path constructed as: CACHEDIR/UUID/FILENAME. The cached files can be manipulated using standard file system commands (removing, finding, etc.). In this sense, the cache sytem is minimalist in design.

Value

character(1) directory path that serves as the base directory for GenomicDataCommons downloads.

the created directory (invisibly)

Functions

• gdc_set_cache(): (Re)set the GenomicDataCommons cache directory

Examples

gdc_cache()
## Not run: 
gdc_set_cache(getwd())

## End(Not run)

---

return gdc-client executable path

Description

This function is a convenience function to find and return the path to the GDC Data Transfer Tool executable assumed to be named 'gdc-client'. The assumption is that the appropriate version of the GDC Data Transfer Tool is a separate download available from https://gdc.cancer.gov/access-data/gdc-data-transfer-tool and as a backup from https://github.com/NCI-GDC/gdc-client.

Usage

gdc_client()

Details

The path is checked in the following order:

1. an R option("gdc_client")

2. an environment variable GDC_CLIENT

3. from the search PATH

4. in the current working directory

Value

character(1) the path to the gdc-client executable.

Examples

# this cannot run without first
# downloading the GDC Data Transfer Tool
gdc_client = try(gdc_client(),silent=TRUE)

---

Get clinical information from GDC

Description

The NCI GDC has a complex data model that allows various studies to supply numerous clinical and demographic data elements. However, across all projects that enter the GDC, there are similarities. This function returns four data.frames associated with case_ids from the GDC.

Usage

gdc_clinical(case_ids, include_list_cols = FALSE)

Arguments

case_ids	a character() vector of case_ids, typically from "cases" query.
include_list_cols	logical(1), whether to include list columns in the "main" data.frame. These list columns have values for aliquots, samples, etc. While these may be useful for some situations, they are generally not that useful as clinical annotations.

Details

Note that these data.frames can, in general, have different numbers of rows (or even no rows at all). If one wishes to combine to produce a single data.frame, using the approach of left joining to the "main" data.frame will yield a useful combined data.frame. We do not do that directly given the potential for 1:many relationships. It is up to the user to determine what the best approach is for any given dataset.

Value

A list of four data.frames:

1. main, representing basic case identification and metadata (update date, etc.)

2. diagnoses

3. esposures

4. demographic

Examples

case_ids = cases() |> results(size=10) |> ids()
clinical_data = gdc_clinical(case_ids)

# overview of clinical results
class(clinical_data)
names(clinical_data)
sapply(clinical_data, class)
sapply(clinical_data, nrow)

# available data
head(clinical_data$main)
head(clinical_data$demographic)
head(clinical_data$diagnoses)
head(clinical_data$exposures)

---

return a gdc token from file or environment

Description

The GDC requires an auth token for downloading data that are "controlled access". For example, BAM files for human datasets, germline variant calls, and SNP array raw data all are protected as "controlled access". For these files, a GDC access token is required. See the https://docs.gdc.cancer.gov/Data_Portal/Users_Guide/Authentication/#gdc-authentication-tokens. Note that this function simply returns a string value. It is possible to keep the GDC token in a variable in R or to pass a string directly to the appropriate parameter. This function is simply a convenience function for alternative approaches to get a token from an environment variable or a file.

Usage

gdc_token()

Details

This function will resolve locations of the GDC token in the following order:

• from the environment variable, GDC_TOKEN, expected to contain the token downloaded from the GDC as a string

• using readLines to read a file named in the environment variable, GDC_TOKEN_FILE

• using readLines to read from a file called .gdc_token in the user's home directory

If all of these fail, this function will return an error.

Value

character(1) (invisibly, to protect against inadvertently printing) the GDC token.

References

https://docs.gdc.cancer.gov/Data_Portal/Users_Guide/Cart/#gdc-authentication-tokens

Examples

# This will not run before a GDC token
# is in place.  
token = try(gdc_token(),silent=TRUE)

---

Download GDC files

Description

Download one or more files from GDC. Files are downloaded using the UUID and renamed to the file name on the remote system. By default, neither the uuid nor the file name on the remote system can exist.

Usage

gdcdata(
  uuids,
  use_cached = TRUE,
  progress = interactive(),
  token = NULL,
  access_method = "api",
  transfer_args = character(),
  ...
)

Arguments

uuids	character() of GDC file UUIDs.
use_cached	logical(1) default TRUE indicating that, if found in the cache, the file will not be downloaded again. If FALSE, all supplied uuids will be re-downloaded.
progress	logical(1) default TRUE in interactive sessions, FALSE otherwise indicating whether a progress par should be produced for each file download.
token	(optional) character(1) security token allowing access to restricted data. See https://gdc-docs.nci.nih.gov/API/Users_Guide/Authentication_and_Authorization/.
access_method	character(1), either 'api' or 'client'. See details.
transfer_args	character(1), additional arguments to pass to the gdc-client command line. See gdc_client and transfer_help for details.
...	further arguments passed to files

Details

This function is appropriate for one or several files; for large downloads use manifest to create a manifest for and the GDC Data Transfer Tool.

When access_method is "api", the GDC "data" endpoint is the transfer mechanism used. The alternative access_method, "client", will utilize the gdc-client transfer tool, which must be downloaded separately and available. See gdc_client for details on specifying the location of the gdc-client executable.

Value

a named vector with file uuids as the names and paths as the value

See Also

manifest for downloading large data.

Examples

# get some example file uuids
uuids <- files() |>
    filter(~ access == 'open' & file_size < 100000) |>
    results(size = 3) |>
    ids()

# and get the data, placing it into the gdc_cache() directory
gdcdata(uuids, use_cached=TRUE)

---

ncigdc: A package for computating the notorious bar statistic.

Description

Cool package for interfacing with NCI GDC

finding data

• query

• cases

• projects

• files

• annotations

• mapping

downloading data

data

Author(s)

Maintainer: Sean Davis [email protected]

Authors:

• Martin Morgan [email protected]

Other contributors:

• Marcel Ramos [email protected] [contributor]

See Also

Useful links:

• https://bioconductor.org/packages/GenomicDataCommons

• http://github.com/Bioconductor/GenomicDataCommons

• http://bioconductor.github.io/GenomicDataCommons/

• Report bugs at https://github.com/Bioconductor/GenomicDataCommons/issues/new

---

Find matching field names

Description

This utility function allows quick text-based search of available fields for using grep

Usage

grep_fields(entity, pattern, ..., value = TRUE)

Arguments

entity	one of the available gdc entities ('files','cases',...) against which to gather available fields for matching
pattern	A regular expression that will be used in a call to grep
...	passed on to grep
value	logical(1) whether to return values as opposed to indices (passed along to grep)

Value

character() vector of field names matching pattern

Examples

grep_fields('files','analysis')

---

get the name of the id field

Description

In many places in the GenomicDataCommons package, the entity ids are stored in a column or a vector with a specific name that corresponds to the field name at the GDC. The format is the entity name (singular) "_id". This generic simply returns that name from a given object.

Usage

id_field(x)

## S3 method for class 'GDCQuery'
id_field(x)

## S3 method for class 'GDCResults'
id_field(x)

Arguments

x	An object representing the query or results of an entity from the GDC ("cases", "files", "annotations", "projects")

Value

character(1) such as "case_id", "file_id", etc.

Methods (by class)

• id_field(GDCQuery): GDCQuery method

• id_field(GDCResults): GDCResults method

Examples

id_field(cases())

---

Get the ids associated with a GDC query or response

Description

The GDC assigns ids (in the form of uuids) to objects in its database. Those ids can be used for relationships, searching on the website, and as unique ids. All

Usage

ids(x)

## S3 method for class 'GDCManifest'
ids(x)

## S3 method for class 'GDCQuery'
ids(x)

## S3 method for class 'GDCResults'
ids(x)

## S3 method for class 'GDCResponse'
ids(x)

Arguments

x	A GDCQuery or GDCResponse object

Value

a character vector of all the entity ids

Examples

# use with a GDC query, in this case for "cases"
ids(cases() |> filter(~ project.project_id == "TCGA-CHOL"))
# also works for responses
ids(response(files()))
# and results
ids(results(cases()))

---

Create NCI GDC filters for limiting GDC query results

Description

Searching the NCI GDC allows for complex filtering based on logical operations and simple comparisons. This function facilitates writing such filter expressions in R-like syntax with R code evaluation.

Usage

make_filter(expr, available_fields)

Arguments

expr	a lazy-wrapped expression or a formula RHS equivalent
available_fields	a character vector of the additional names that will be injected into the filter evaluation environment

Details

If used with available_fields, "bare" fields that are named in the available_fields character vector can be used in the filter expression without quotes.

Value

a list that represents an R version of the JSON that will ultimately be used in an NCI GDC search or other query.

---

Prepare GDC manifest file for bulk download

Description

The manifest function/method creates a manifest of files to be downloaded using the GDC Data Transfer Tool. There are methods for creating manifest data frames from GDCQuery objects that contain file information ("cases" and "files" queries).

Usage

manifest(x, from = 0, size = count(x), ...)

## S3 method for class 'gdc_files'
manifest(x, from = 0, size = count(x), ...)

## S3 method for class 'GDCfilesResponse'
manifest(x, from = 0, size = count(x), ...)

## S3 method for class 'GDCcasesResponse'
manifest(x, from = 0, size = count(x), ...)

Arguments

x	An GDCQuery object of subclass "gdc_files" or "gdc_cases".
from	Record number from which to start when returning the manifest.
size	The total number of records to return. Default will return the usually desirable full set of records.
...	passed to PUT.

Value

A tibble, also of type "gdc_manifest", with five columns:

• id

• filename

• md5

• size

• state

Methods (by class)

• manifest(gdc_files):

• manifest(GDCfilesResponse):

• manifest(GDCcasesResponse):

Examples

gFiles = files()
shortManifest = gFiles |> manifest(size=10)
head(shortManifest,n=3)

---

Query GDC for available endpoint fields

Description

Query GDC for available endpoint fields

Usage

mapping(endpoint)

Arguments

endpoint

character(1) corresponding to endpoints for which users may specify additional or alternative fields. Endpoints include “projects”, “cases”, “files”, and “annotations”.

Value

A data frame describing the field (field name), full (full data model name), type (data type), and four additional columns describing the "set" to which the fields belong–“default”, “expand”, “multi”, and “nested”.

Examples

map <- mapping("projects")
head(map)
# get only the "default" fields
subset(map,defaults)
# And get just the text names of the "default" fields
subset(map,defaults)$field

---

Start a query of GDC metadata

Description

The basis for all functionality in this package starts with constructing a query in R. The GDCQuery object contains the filters, facets, and other parameters that define the returned results. A token is required for accessing certain datasets.

Usage

query(
  entity,
  filters = NULL,
  facets = NULL,
  expand = NULL,
  fields = default_fields(entity),
  ...
)

cases(...)

files(...)

projects(...)

annotations(...)

ssms(...)

ssm_occurrences(...)

cnvs(...)

cnv_occurrences(...)

genes(...)

Arguments

entity	character vector, including one of the entities in .gdc_entities
filters	a filter list, typically created using make_filter, or added to an existing GDCQuery object using filter.
facets	a character vector of facets for counting common values. See available_fields. In general, one will not specify this parameter but will use facet instead.
expand	a character vector of "expands" to include in returned data. See available_expand
fields	a character vector of fields to return. See available_fields. In general, one will not specify fields directly, but instead use select
...	passed through to query

Value

An S3 object, the GDCQuery object. This is a list with the following members.

• filters

• facets

• fields

• expand

• archive

• token

Functions

• cases(): convenience constructor for a GDCQuery for cases

• files(): convenience contructor for a GDCQuery for files

• projects(): convenience contructor for a GDCQuery for projects

• annotations(): convenience contructor for a GDCQuery for annotations

• ssms(): convenience contructor for a GDCQuery for ssms

• ssm_occurrences(): convenience contructor for a GDCQuery for ssm_occurrences

• cnvs(): convenience contructor for a GDCQuery for cnvs

• cnv_occurrences(): convenience contructor for a GDCQuery for cnv_occurrences

• genes(): convenience contructor for a GDCQuery for genes

Examples

qcases = query('cases')
# equivalent to:
qcases = cases()

---

Read DNAcopy results into GRanges object

Description

Read DNAcopy results into GRanges object

Usage

readDNAcopy(fname, ...)

Arguments

fname	The path to a DNAcopy-like file.
...	passed to read_tsv

Value

a GRanges object

Examples

fname = system.file(package='GenomicDataCommons',
                    'extdata/dnacopy.tsv.gz')
dnac = readDNAcopy(fname)
class(dnac)
length(dnac)

---

Read a single htseq-counts result file.

Description

The htseq package is used extensively to count reads relative to regions (see http://www-huber.embl.de/HTSeq/doc/counting.html). The output of htseq-count is a simple two-column table that includes features in column 1 and counts in column 2. This function simply reads in the data from one such file and assigns column names.

Usage

readHTSeqFile(fname, samplename = "sample", ...)

Arguments

fname	character(1), the path of the htseq-count file.
samplename	character(1), the name of the sample. This will become the name of the second column on the resulting data.frame, making for easier merging if necessary.
...	passed to read_tsv)

Value

a two-column data frame

Examples

fname = system.file(package='GenomicDataCommons',
                    'extdata/example.htseq.counts.gz')
dat = readHTSeqFile(fname)
head(dat)

---

Fetch GDCQuery metadata from GDC

Description

Fetch GDCQuery metadata from GDC

Usage

response(x, ...)

## S3 method for class 'GDCQuery'
response(x, from = 0, size = 10, ..., response_handler = jsonlite::fromJSON)

response_all(x, ...)

Arguments

x	a GDCQuery object
...	passed to httr (good for passing config info, etc.)
from	integer index from which to start returning data
size	number of records to return
response_handler	a function that processes JSON (as text) and returns an R object. Default is fromJSON.

Value

A GDCResponse object which is a list with the following members:

• results

• query

• aggregations

• pages

Examples

# basic class stuff
gCases = cases()
resp = response(gCases)
class(resp)
names(resp)

# And results from query
resp$results[[1]]

---

results

Description

results

Usage

results(x, ...)

## S3 method for class 'GDCQuery'
results(x, ...)

## S3 method for class 'GDCResponse'
results(x, ...)

Arguments

x	a GDCQuery object
...	passed on to response

Value

A (typically nested) list of GDC records

Methods (by class)

• results(GDCQuery):

• results(GDCResponse):

Examples

qcases = cases() |> results()
length(qcases)

---

results_all

Description

results_all

Usage

results_all(x)

## S3 method for class 'GDCQuery'
results_all(x)

## S3 method for class 'GDCResponse'
results_all(x)

Arguments

x	a GDCQuery object

Value

A (typically nested) list of GDC records

Methods (by class)

• results_all(GDCQuery):

• results_all(GDCResponse):

Examples

# details of all available projects
projResults = projects() |> results_all()
length(projResults)
count(projects())

---

S3 generic to set GDCQuery fields

Description

S3 generic to set GDCQuery fields

Usage

select(x, fields)

## S3 method for class 'GDCQuery'
select(x, fields)

Arguments

x	the objects on which to set fields
fields	a character vector specifying the fields

Value

A GDCQuery object, with the fields member altered.

Methods (by class)

• select(GDCQuery): set fields on a GDCQuery object

Examples

gProj = projects()
gProj$fields
head(available_fields(gProj))
default_fields(gProj)

gProj |>
  select(default_fields(gProj)[1:2]) |>
  response() |>
  str(max_level=2)

---

Query GDC for data slices

Description

This function returns a BAM file representing reads overlapping regions specified either as chromosomal regions or as gencode gene symbols.

Usage

slicing(
  uuid,
  regions,
  symbols,
  destination = file.path(tempdir(), paste0(uuid, ".bam")),
  overwrite = FALSE,
  progress = interactive(),
  token = gdc_token()
)

Arguments

uuid	character(1) identifying the BAM file resource
regions	character() vector describing chromosomal regions, e.g., c("chr1", "chr2:10000", "chr3:10000-20000") (all of chromosome 1, chromosome 2 from position 10000 to the end, chromosome 3 from 10000 to 20000).
symbols	character() vector of gencode gene symbols, e.g., c("BRCA1", "PTEN")
destination	character(1) default tempfile() file path for BAM file slice
overwrite	logical(1) default FALSE can destination be overwritten?
progress	logical(1) default interactive() should a progress bar be used?
token	character(1) security token allowing access to restricted data. Almost all BAM data is restricted, so a token is usually required. See https://docs.gdc.cancer.gov/Data/Data_Security/Data_Security/#authentication-tokens.

Details

This function uses the Genomic Data Commons "slicing" API to get portions of a BAM file specified either using "regions" or using HGNC gene symbols.

Value

character(1) destination to the downloaded BAM file

Examples

## Not run: 
 slicing("df80679e-c4d3-487b-934c-fcc782e5d46e",
        regions="chr17:75000000-76000000",
        token=gdc_token())

# Get 10 BAM files.
bamfiles = files() |> 
           filter(data_format=='BAM') |>
           results(size=10) |> ids()

# Current alignments at the GDC are to GRCh38
library('TxDb.Hsapiens.UCSC.hg38.knownGene')
all_genes = genes(TxDb.Hsapiens.UCSC.hg38.knownGene)

first3genes = all_genes[1:3]
# remove strand info
strand(first3genes) = '*'

# We can get our regions easily now
as.character(first3genes)

# Use parallel downloads to speed processing
library(BiocParallel)
register(MulticoreParam())

fnames = bplapply(bamfiles, slicing, overwrite = TRUE,
                regions=as.character(first3genes))

# 10 BAM files
fnames

library(GenomicAlignments)
lapply(unlist(fnames), readGAlignments)


## End(Not run)

---

Query the GDC for current status

Description

Query the GDC for current status

Usage

status(version = NULL)

Arguments

version

(optional) character(1) version of GDC

Value

List describing current status.

Examples

status()

---

Bulk data download

Description

The GDC maintains a special tool, https://docs.gdc.cancer.gov/Data_Transfer_Tool/Users_Guide/Getting_Started/, that enables high-performance, potentially parallel, and resumable downloads. The Data Transfer Tool is an external program that requires separate download. Due to recent changes in the GDC API, the transfer function now validates the version of the 'gdc-client' to ensure reliable downloads.

Usage

transfer(uuids, args = character(), token = NULL, overwrite = FALSE)

gdc_client_version_validate(valid_version = .GDC_COMPATIBLE_VERSION)

transfer_help()

Arguments

uuids	character() vector of GDC file UUIDs
args	character() vector specifying command-line arguments to be passed to gdc-client. See transfer_help for possible values. The arguments --manifest, --dir, and --token-file are determined by manifest, destination_dir, and token, respectively, and should NOT be provided as elements of args.
token	character(1) containing security token allowing access to restricted data. See https://gdc-docs.nci.nih.gov/API/Users_Guide/Authentication_and_Authorization/. Note that the GDC transfer tool requires a file for data transfer. Therefore, this token will be written to a temporary file (with appropriate permissions set).
overwrite	logical(1) default FALSE indicating whether existing files with identical name should be over-written.
valid_version	character(1) The last known version that works for the current data release for which to validate against, not typically changed by the end-user.

Value

character(1) directory path to which the files were downloaded.

Functions

• gdc_client_version_validate(): If you are using the 'client' option, your 'gdc-client' should be up-to-date (>= 1.3.0).

• transfer_help():

Examples

## Not run: 
uuids = files() |> 
  filter(access == "open") |> 
  results() |>
  ids()
file_paths <- transfer(uuids)
file_paths
names(file_paths)
# and with authenication
# REQUIRES gdc_token 
# destination <- transfer(uuids,token=gdc_token())

## End(Not run)

---

write a manifest data.frame to disk

Description

The manifest method creates a data.frame that represents the data for a manifest file needed by the GDC Data Transfer Tool. While the file format is nothing special, this is a simple helper function to write a manifest data.frame to disk. It returns the path to which the file is written, so it can be used "in-line" in a call to transfer.

Usage

write_manifest(manifest, destfile = tempfile())

Arguments

manifest	A data.frame with five columns, typically created by a call to manifest
destfile	The filename for saving the manifest.

Value

character(1) the destination file name.

Examples

mf = files() |> manifest(size=10)
write_manifest(mf)

---