2.5.1 Clinical data

Accessing clinical data is a very common task. Given a set of case_ids, the gdc_clinical() function will return a list of four tibbles.

• demographic
• diagnoses
• exposures
• main

case_ids = cases() %>% results(size=10) %>% ids()
clindat = gdc_clinical(case_ids)
names(clindat)

## [1] "demographic" "diagnoses"   "exposures"   "main"

head(clindat[["main"]])

head(clindat[["diagnoses"]])

2.5.2 General metadata queries

The GenomicDataCommons package can access the significant clinical, demographic, biospecimen, and annotation information contained in the NCI GDC. The gdc_clinical() function will often be all that is needed, but the API and GenomicDataCommons package make much flexibility if fine-tuning is required.

expands = c("diagnoses","annotations",
             "demographic","exposures")
clinResults = cases() %>%
    GenomicDataCommons::select(NULL) %>%
    GenomicDataCommons::expand(expands) %>%
    results(size=50)
str(clinResults[[1]],list.len=6)

## List of 50
##  $ df78950c-198d-4afc-a81e-8cfa4dea0abd:'data.frame':    1 obs. of  22 variables:
##   ..$ classification_of_tumor          : chr "not reported"
##   ..$ last_known_disease_status        : chr "not reported"
##   ..$ updated_datetime                 : chr "2018-09-10T15:06:41.433124-05:00"
##   ..$ primary_diagnosis                : chr "Adenocarcinoma, NOS"
##   ..$ submitter_id                     : chr "TCGA-L5-A8NF_diagnosis"
##   ..$ tumor_stage                      : chr "stage iva"
##   .. [list output truncated]
##  $ cccd0568-e267-44a2-8421-6471736bb45f:'data.frame':    1 obs. of  22 variables:
##   ..$ classification_of_tumor          : chr "not reported"
##   ..$ last_known_disease_status        : chr "not reported"
##   ..$ updated_datetime                 : chr "2018-09-10T15:06:41.433124-05:00"
##   ..$ primary_diagnosis                : chr "Adenocarcinoma, NOS"
##   ..$ submitter_id                     : chr "TCGA-R6-A8W8_diagnosis"
##   ..$ tumor_stage                      : chr "not reported"
##   .. [list output truncated]
##  $ ab2755d2-5bd9-4e2f-b255-e813afc8d268:'data.frame':    1 obs. of  22 variables:
##   ..$ classification_of_tumor          : chr "not reported"
##   ..$ last_known_disease_status        : chr "not reported"
##   ..$ updated_datetime                 : chr "2018-09-10T15:06:41.433124-05:00"
##   ..$ primary_diagnosis                : chr "Adenocarcinoma, NOS"
##   ..$ submitter_id                     : chr "TCGA-2H-A9GL_diagnosis"
##   ..$ tumor_stage                      : chr "stage iii"
##   .. [list output truncated]
##  $ 91e37435-16a1-4077-b342-83826480fdc9:'data.frame':    1 obs. of  22 variables:
##   ..$ classification_of_tumor          : chr "not reported"
##   ..$ last_known_disease_status        : chr "not reported"
##   ..$ updated_datetime                 : chr "2018-09-05T23:16:44.045570-05:00"
##   ..$ primary_diagnosis                : chr "Pheochromocytoma, malignant"
##   ..$ submitter_id                     : chr "TCGA-TT-A6YN_diagnosis"
##   ..$ tumor_stage                      : chr "not reported"
##   .. [list output truncated]
##  $ f49438a7-c5e5-47e7-bef4-5e6881a0732a:'data.frame':    1 obs. of  22 variables:
##   ..$ classification_of_tumor          : chr "not reported"
##   ..$ last_known_disease_status        : chr "not reported"
##   ..$ updated_datetime                 : chr "2018-09-05T23:16:44.045570-05:00"
##   ..$ primary_diagnosis                : chr "Pheochromocytoma, NOS"
##   ..$ submitter_id                     : chr "TCGA-RW-A689_diagnosis"
##   ..$ tumor_stage                      : chr "not reported"
##   .. [list output truncated]
##  $ 117eb38b-0c62-4336-a866-fa5bd013256a:'data.frame':    1 obs. of  22 variables:
##   ..$ classification_of_tumor          : chr "not reported"
##   ..$ last_known_disease_status        : chr "not reported"
##   ..$ updated_datetime                 : chr "2018-09-06T18:42:58.220092-05:00"
##   ..$ primary_diagnosis                : chr "Adenocarcinoma, NOS"
##   ..$ submitter_id                     : chr "TCGA-BR-4279_diagnosis"
##   ..$ tumor_stage                      : chr "stage ii"
##   .. [list output truncated]
##   [list output truncated]

# or listviewer::jsonedit(clinResults)

4.1.1 Creating a query

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 (see default_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)

## List of 5
##  $ fields : chr [1:10] "dbgap_accession_number" "disease_type" "intended_release_date" "name" ...
##  $ filters: NULL
##  $ facets : NULL
##  $ legacy : logi FALSE
##  $ expand : NULL
##  - attr(*, "class")= chr [1:3] "gdc_projects" "GDCQuery" "list"

4.1.2 Retrieving results

[ GDC pagination documentation ]

[ GDC sorting 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

## [1] 45

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)

## List of 9
##  $ dbgap_accession_number: chr [1:10] NA "phs000466" NA NA ...
##  $ disease_type          :List of 10
##   ..$ TCGA-READ   : chr [1:2] "Cystic, Mucinous and Serous Neoplasms" "Adenomas and Adenocarcinomas"
##   ..$ TARGET-CCSK : chr "Clear Cell Sarcoma of the Kidney"
##   ..$ TCGA-MESO   : chr "Mesothelial Neoplasms"
##   ..$ TCGA-CHOL   : chr "Adenomas and Adenocarcinomas"
##   ..$ NCICCR-DLBCL: chr "Lymphoid Neoplasm Diffuse Large B-cell Lymphoma"
##   ..$ TARGET-WT   : chr "High-Risk Wilms Tumor"
##   ..$ TCGA-TGCT   : chr "Germ Cell Neoplasms"
##   ..$ TCGA-PRAD   : chr [1:3] "Cystic, Mucinous and Serous Neoplasms" "Adenomas and Adenocarcinomas" "Ductal and Lobular Neoplasms"
##   ..$ TCGA-LAML   : chr "Myeloid Leukemias"
##   ..$ TCGA-ESCA   : chr [1:3] "Cystic, Mucinous and Serous Neoplasms" "Adenomas and Adenocarcinomas" "Squamous Cell Neoplasms"
##  $ releasable            : logi [1:10] FALSE FALSE FALSE FALSE FALSE FALSE ...
##  $ released              : logi [1:10] TRUE TRUE TRUE TRUE TRUE TRUE ...
##  $ state                 : chr [1:10] "open" "open" "open" "open" ...
##  $ primary_site          :List of 10
##   ..$ TCGA-READ   : chr [1:5] "Rectosigmoid junction" "Unknown" "Rectum" "Colon" ...
##   ..$ TARGET-CCSK : chr "Kidney"
##   ..$ TCGA-MESO   : chr [1:2] "Heart, mediastinum, and pleura" "Bronchus and lung"
##   ..$ TCGA-CHOL   : chr [1:3] "Other and unspecified parts of biliary tract" "Gallbladder" "Liver and intrahepatic bile ducts"
##   ..$ NCICCR-DLBCL: chr "Lymph Nodes"
##   ..$ TARGET-WT   : chr "Kidney"
##   ..$ TCGA-TGCT   : chr "Testis"
##   ..$ TCGA-PRAD   : chr "Prostate gland"
##   ..$ TCGA-LAML   : chr "Hematopoietic and reticuloendothelial systems"
##   ..$ TCGA-ESCA   : chr [1:2] "Esophagus" "Stomach"
##  $ project_id            : chr [1:10] "TCGA-READ" "TARGET-CCSK" "TCGA-MESO" "TCGA-CHOL" ...
##  $ id                    : chr [1:10] "TCGA-READ" "TARGET-CCSK" "TCGA-MESO" "TCGA-CHOL" ...
##  $ name                  : chr [1:10] "Rectum Adenocarcinoma" "Clear Cell Sarcoma of the Kidney" "Mesothelioma" "Cholangiocarcinoma" ...
##  - attr(*, "row.names")= int [1:10] 1 2 3 4 5 6 7 8 9 10
##  - attr(*, "class")= chr [1:3] "GDCprojectsResults" "GDCResults" "list"

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))

## [1] 10

presults = pquery %>% results_all()
length(ids(presults))

## [1] 45

# includes all records
length(ids(presults)) == count(pquery)

## [1] TRUE

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.

4.1.3 Fields and Values

[ GDC fields documentation ]

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')

##  [1] "access"                         "acl"                           
##  [3] "average_base_quality"           "average_insert_size"           
##  [5] "average_read_length"            "channel"                       
##  [7] "chip_id"                        "chip_position"                 
##  [9] "contamination"                  "contamination_error"           
## [11] "created_datetime"               "data_category"                 
## [13] "data_format"                    "data_type"                     
## [15] "error_type"                     "experimental_strategy"         
## [17] "file_autocomplete"              "file_id"                       
## [19] "file_name"                      "file_size"                     
## [21] "imaging_date"                   "magnification"                 
## [23] "md5sum"                         "mean_coverage"                 
## [25] "origin"                         "pairs_on_diff_chr"             
## [27] "plate_name"                     "plate_well"                    
## [29] "platform"                       "proportion_base_mismatch"      
## [31] "proportion_coverage_10X"        "proportion_coverage_30X"       
## [33] "proportion_reads_duplicated"    "proportion_reads_mapped"       
## [35] "proportion_targets_no_coverage" "read_pair_number"              
## [37] "revision"                       "state"                         
## [39] "state_comment"                  "submitter_id"                  
## [41] "tags"                           "total_reads"                   
## [43] "type"                           "updated_datetime"

# The number of fields available for files endpoint
length(available_fields('files'))

## [1] 734

# The first few fields available for files endpoint
head(available_fields('files'))

## [1] "access"                      "acl"                        
## [3] "analysis.analysis_id"        "analysis.analysis_type"     
## [5] "analysis.created_datetime"   "analysis.input_files.access"

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

##  [1] "aliquot_ids"              "analyte_ids"             
##  [3] "case_autocomplete"        "case_id"                 
##  [5] "created_datetime"         "days_to_index"           
##  [7] "days_to_lost_to_followup" "disease_type"            
##  [9] "index_date"               "lost_to_followup"        
## [11] "portion_ids"              "primary_site"            
## [13] "sample_ids"               "slide_ids"               
## [15] "state"                    "submitter_aliquot_ids"   
## [17] "submitter_analyte_ids"    "submitter_id"            
## [19] "submitter_portion_ids"    "submitter_sample_ids"    
## [21] "submitter_slide_ids"      "updated_datetime"

# 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)

## [1] "case_id"                       "aliquot_ids"                  
## [3] "analyte_ids"                   "annotations.annotation_id"    
## [5] "annotations.case_id"           "annotations.case_submitter_id"

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.

4.1.4 Facets and aggregation

[ GDC facet documentation ]

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.

4.1.5 Filtering

[ 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

## [1] 365463

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))

## List of 2
##  $ op     : 'scalar' chr "="
##  $ content:List of 2
##   ..$ field: chr "type"
##   ..$ value: chr "gene_expression"

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) %>% 
    head()

## [1] "analysis.input_files.proportion_base_mismatch"      
## [2] "analysis.input_files.proportion_coverage_10X"       
## [3] "analysis.input_files.proportion_coverage_30X"       
## [4] "analysis.input_files.proportion_reads_duplicated"   
## [5] "analysis.input_files.proportion_reads_mapped"       
## [6] "analysis.input_files.proportion_targets_no_coverage"

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() %>% 
    head()

## $cases.project.project_id
##               key doc_count
## 1           FM-AD     36134
## 2       TCGA-BRCA     31524
## 3       TCGA-LUAD     17052
## 4       TCGA-UCEC     16174
## 5       TCGA-HNSC     15289
## 6         TCGA-OV     15166
## 7       TCGA-THCA     14421
## 8       TCGA-LUSC     15324
## 9        TCGA-LGG     14728
## 10      TCGA-KIRC     15091
## 11      TCGA-PRAD     14288
## 12      TCGA-COAD     14279
## 13       TCGA-GBM     11996
## 14      TCGA-SKCM     12725
## 15      TCGA-STAD     12846
## 16      TCGA-BLCA     11711
## 17      TCGA-LIHC     10815
## 18      TCGA-CESC      8595
## 19      TCGA-KIRP      8507
## 20      TCGA-SARC      7494
## 21      TCGA-PAAD      5307
## 22      TCGA-ESCA      5271
## 23      TCGA-PCPG      5035
## 24      TCGA-READ      4925
## 25      TCGA-TGCT      4284
## 26      TCGA-LAML      4434
## 27      TCGA-THYM      3445
## 28     TARGET-NBL      2809
## 29       TCGA-ACC      2547
## 30      TCGA-KICH      2325
## 31      TCGA-MESO      2339
## 32     TARGET-AML      2459
## 33        CPTAC-3      4268
## 34       TCGA-UVM      2180
## 35       TCGA-UCS      1659
## 36      TARGET-WT      1408
## 37  TARGET-ALL-P3      2502
## 38      TCGA-CHOL      1349
## 39      TCGA-DLBC      1229
## 40   NCICCR-DLBCL       957
## 41      TARGET-OS        66
## 42      TARGET-RT       381
## 43    CTSP-DLBCL1        89
## 44    TARGET-CCSK        15
## 45 VAREPOP-APOLLO        21

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))

## List of 2
##  $ op     : 'scalar' chr "and"
##  $ content:List of 2
##   ..$ :List of 2
##   .. ..$ op     : 'scalar' chr "="
##   .. ..$ content:List of 2
##   .. .. ..$ field: chr "cases.project.project_id"
##   .. .. ..$ value: chr "TCGA-OV"
##   ..$ :List of 2
##   .. ..$ op     : 'scalar' chr "="
##   .. ..$ content:List of 2
##   .. .. ..$ field: chr "type"
##   .. .. ..$ value: chr "gene_expression"

qfiles %>% count()

## [1] 1137

Asking for a count() of results given these new filter criteria gives r qfiles %>% count() results. Filters can be chained (or nested) to accomplish the same effect as multiple & conditionals. The count() below is equivalent to the & filtering done above.

qfiles2 = files() %>%
    filter( cases.project.project_id == 'TCGA-OV') %>% 
    filter( type == 'gene_expression') 
qfiles2 %>% count()

## [1] 1137

(qfiles %>% count()) == (qfiles2 %>% count()) #TRUE

## [1] TRUE

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)

## [1] 379

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.

4.3.1 Data downloads via the GDC API

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.

4.3.2 Bulk downloads

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.

fnames = gdcdata(manifest_df$id[3:10], access_method = 'client')

4.3.3 BAM slicing

5.1.1 How many cases are there per project_id?

res = cases() %>% facet("project.project_id") %>% aggregations()
head(res)

## $project.project_id
##               key doc_count
## 1           FM-AD     18004
## 2      TARGET-NBL      1127
## 3       TCGA-BRCA      1098
## 4      TARGET-AML       988
## 5       TARGET-WT       652
## 6        TCGA-GBM       617
## 7         TCGA-OV       608
## 8       TCGA-LUAD       585
## 9       TCGA-UCEC       560
## 10      TCGA-KIRC       537
## 11      TCGA-HNSC       528
## 12       TCGA-LGG       516
## 13      TCGA-THCA       507
## 14      TCGA-LUSC       504
## 15      TCGA-PRAD       500
## 16   NCICCR-DLBCL       489
## 17      TCGA-SKCM       470
## 18      TCGA-COAD       461
## 19      TCGA-STAD       443
## 20      TCGA-BLCA       412
## 21      TARGET-OS       381
## 22      TCGA-LIHC       377
## 23        CPTAC-3       322
## 24      TCGA-CESC       307
## 25      TCGA-KIRP       291
## 26      TCGA-SARC       261
## 27      TCGA-LAML       200
## 28      TCGA-ESCA       185
## 29      TCGA-PAAD       185
## 30      TCGA-PCPG       179
## 31      TCGA-READ       172
## 32      TCGA-TGCT       150
## 33  TARGET-ALL-P3       131
## 34      TCGA-THYM       124
## 35      TCGA-KICH       113
## 36       TCGA-ACC        92
## 37      TCGA-MESO        87
## 38       TCGA-UVM        80
## 39      TARGET-RT        75
## 40      TCGA-DLBC        58
## 41       TCGA-UCS        57
## 42      TCGA-CHOL        51
## 43    CTSP-DLBCL1        45
## 44    TARGET-CCSK        13
## 45 VAREPOP-APOLLO         7

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))

5.1.2 How many cases are included in all TARGET projects?

cases() %>% filter(~ project.program.name=='TARGET') %>% count()

## [1] 3367

5.1.3 How many cases are included in all TCGA projects?

cases() %>% filter(~ project.program.name=='TCGA') %>% count()

## [1] 11315

5.1.4 What is the breakdown of sample types in TCGA-BRCA?

# 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

5.1.5 Fetch all samples in TCGA-BRCA that use “Solid Tissue” as a normal.

# 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)

## [1] 162

res = resp %>% results()
str(res[1],list.len=6)

## List of 1
##  $ updated_datetime: chr [1:162] "2018-09-06T13:49:20.245333-05:00" "2018-11-28T10:07:18.958951-06:00" "2018-09-06T13:49:20.245333-05:00" "2018-09-06T13:49:20.245333-05:00" ...

head(ids(resp))

## [1] "7bcf6d42-eb8f-4aa2-825b-38671d4f9e3a"
## [2] "4a032bad-e726-48f2-8f39-e3acc109cc91"
## [3] "26573441-eedb-4364-966c-e7f803deef19"
## [4] "d9fd2724-7db0-4af3-ac14-217bdfa5203f"
## [5] "6cdc0d53-f813-4101-81e5-9bee68270536"
## [6] "cb9f5e50-f49d-4899-8895-9367afcc1015"

5.2.1 How many of each type of file are available?

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))

5.2.2 Find gene-level RNA-seq quantification files for GBM

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()

## $analysis.workflow_type
##               key doc_count
## 1  HTSeq - Counts       174
## 2    HTSeq - FPKM       174
## 3 HTSeq - FPKM-UQ       174

# 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()

5.3.1 Get all BAM file ids from TCGA-GBM

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)