Last updated: 2023-11-01
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| Rmd | 3fc037e | Dave Tang | 2023-11-01 | Using the GenomicDataCommons package |
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 standardisation 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 harmonised 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 harmonises 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 GenomicDataCommons Bioconductor package provides basic infrastructure for querying, accessing, and mining genomic datasets available from the GDC.
See The GDC API page.
Install the GenomicDataCommons
package using BiocManager.
if (! "GenomicDataCommons" %in% installed.packages()[, 1]){
BiocManager::install("GenomicDataCommons")
}
library(GenomicDataCommons)
packageVersion("GenomicDataCommons")[1] '1.26.0'Check status to see if we can query the GDC.
GenomicDataCommons::status()$commit
[1] "023da73eee3c17608db1a9903c82852428327b88"
$data_release
[1] "Data Release 38.0 - August 31, 2023"
$status
[1] "OK"
$tag
[1] "5.0.6"
$version
[1] 1stopifnot(GenomicDataCommons::status()$status=="OK")The following code builds a manifest that can be used to
guide the download of raw data. Here, filtering finds open gene
expression files quantified as raw counts using STAR from TCGA ovarian
cancer patients.
ge_manifest <- files() %>%
filter(cases.project.project_id == 'TCGA-OV') %>%
filter(type == 'gene_expression' ) %>%
filter(access == 'open') %>%
filter(analysis.workflow_type == 'STAR - Counts') %>%
manifest()
DT::datatable(ge_manifest)The gdcdata function is used to download GDC files.
fnames <- lapply(ge_manifest$id[1:3], gdcdata)
fnames[[1]]
96aca0af-a776-460d-95ff-87e364e4ac99
"~/.cache/GenomicDataCommons/96aca0af-a776-460d-95ff-87e364e4ac99/21ff9928-00f0-4b96-8d70-35e9bfad5d40.rna_seq.augmented_star_gene_counts.tsv"
[[2]]
b668c86b-fa56-4d39-9529-5b47081a3faa
"~/.cache/GenomicDataCommons/b668c86b-fa56-4d39-9529-5b47081a3faa/41bdbd88-b4b2-4884-8a44-b34656ae4156.rna_seq.augmented_star_gene_counts.tsv"
[[3]]
60678f17-e3d7-40cd-99ff-73706497968a
"~/.cache/GenomicDataCommons/60678f17-e3d7-40cd-99ff-73706497968a/03c8e4fe-1e07-4ea3-a154-c17c2e8af508.rna_seq.augmented_star_gene_counts.tsv" Files are downloaded and stored in the directory specified by
gdc_cache().
gdc_cache()[1] "~/.cache/GenomicDataCommons"Tally the total number of available STAR gene counts that are open for download.
open_star_manifest <- files() %>%
filter(analysis.workflow_type == 'STAR - Counts') %>%
filter(access == 'open') %>%
manifest()
dim(open_star_manifest)[1] 23111 16Queries in the GenomicDataCommons package follow the
four metadata endpoints available at the GDC; there are four convenience
functions that each create GDCQuery objects:
projects()cases()files()annotations()Four endpoints: projects, cases, files, and annotations that have various associated fields. These are the default fields.
endpoints <- c("projects", "cases", "files", "annotations")
sapply(endpoints, default_fields)$projects
[1] "dbgap_accession_number" "disease_type" "intended_release_date"
[4] "name" "primary_site" "project_autocomplete"
[7] "project_id" "releasable" "released"
[10] "state"
$cases
[1] "aliquot_ids" "analyte_ids"
[3] "case_autocomplete" "case_id"
[5] "consent_type" "created_datetime"
[7] "days_to_consent" "days_to_lost_to_followup"
[9] "diagnosis_ids" "disease_type"
[11] "index_date" "lost_to_followup"
[13] "portion_ids" "primary_site"
[15] "sample_ids" "slide_ids"
[17] "state" "submitter_aliquot_ids"
[19] "submitter_analyte_ids" "submitter_diagnosis_ids"
[21] "submitter_id" "submitter_portion_ids"
[23] "submitter_sample_ids" "submitter_slide_ids"
[25] "updated_datetime"
$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] "msi_score" "msi_status"
[27] "pairs_on_diff_chr" "plate_name"
[29] "plate_well" "platform"
[31] "proc_internal" "proportion_base_mismatch"
[33] "proportion_coverage_10x" "proportion_coverage_10X"
[35] "proportion_coverage_30x" "proportion_coverage_30X"
[37] "proportion_reads_duplicated" "proportion_reads_mapped"
[39] "proportion_targets_no_coverage" "read_pair_number"
[41] "revision" "stain_type"
[43] "state" "state_comment"
[45] "submitter_id" "tags"
[47] "total_reads" "tumor_ploidy"
[49] "tumor_purity" "type"
[51] "updated_datetime" "wgs_coverage"
$annotations
[1] "annotation_autocomplete" "annotation_id"
[3] "case_id" "case_submitter_id"
[5] "category" "classification"
[7] "created_datetime" "entity_id"
[9] "entity_submitter_id" "entity_type"
[11] "legacy_created_datetime" "legacy_updated_datetime"
[13] "notes" "state"
[15] "status" "submitter_id"
[17] "updated_datetime" Available fields for each endpoint.
all_fields <- sapply(endpoints, available_fields)
names(all_fields) <- endpoints
sapply(all_fields, length) projects cases files annotations
22 1001 1022 30 These fields can be used for filtering purposes.
head(all_fields$files)[1] "access" "acl"
[3] "analysis.analysis_id" "analysis.analysis_type"
[5] "analysis.created_datetime" "analysis.input_files.access"Use the facet function to aggregate on values used for a
particular field.
files() %>% facet("access") %>% aggregations()$access
doc_count key
1 678416 controlled
2 325331 openUse grep to search for fields of interest, for example
“project”.
grep("project", all_fields$files, ignore.case = TRUE, value = TRUE) [1] "cases.project.dbgap_accession_number"
[2] "cases.project.disease_type"
[3] "cases.project.intended_release_date"
[4] "cases.project.name"
[5] "cases.project.primary_site"
[6] "cases.project.program.dbgap_accession_number"
[7] "cases.project.program.name"
[8] "cases.project.program.program_id"
[9] "cases.project.project_id"
[10] "cases.project.releasable"
[11] "cases.project.released"
[12] "cases.project.state"
[13] "cases.tissue_source_site.project" Look for “days_to_collection”.
grep("collection", all_fields$cases, ignore.case = TRUE, value = TRUE)[1] "samples.days_to_collection" "samples.tissue_collection_type"Look for “workflow_type”.
grep("workflow_type", all_fields$cases, ignore.case = TRUE, value = TRUE)[1] "files.analysis.metadata.read_groups.read_group_qcs.workflow_type"
[2] "files.analysis.workflow_type"
[3] "files.downstream_analyses.workflow_type" Look for “treatment”.
grep("treatment", all_fields$cases, ignore.case = TRUE, value = TRUE) [1] "diagnoses.prior_treatment"
[2] "diagnoses.treatments.chemo_concurrent_to_radiation"
[3] "diagnoses.treatments.created_datetime"
[4] "diagnoses.treatments.days_to_treatment_end"
[5] "diagnoses.treatments.days_to_treatment_start"
[6] "diagnoses.treatments.initial_disease_status"
[7] "diagnoses.treatments.number_of_cycles"
[8] "diagnoses.treatments.reason_treatment_ended"
[9] "diagnoses.treatments.regimen_or_line_of_therapy"
[10] "diagnoses.treatments.route_of_administration"
[11] "diagnoses.treatments.state"
[12] "diagnoses.treatments.submitter_id"
[13] "diagnoses.treatments.therapeutic_agents"
[14] "diagnoses.treatments.treatment_anatomic_site"
[15] "diagnoses.treatments.treatment_arm"
[16] "diagnoses.treatments.treatment_dose"
[17] "diagnoses.treatments.treatment_dose_units"
[18] "diagnoses.treatments.treatment_effect"
[19] "diagnoses.treatments.treatment_effect_indicator"
[20] "diagnoses.treatments.treatment_frequency"
[21] "diagnoses.treatments.treatment_id"
[22] "diagnoses.treatments.treatment_intent_type"
[23] "diagnoses.treatments.treatment_or_therapy"
[24] "diagnoses.treatments.treatment_outcome"
[25] "diagnoses.treatments.treatment_type"
[26] "diagnoses.treatments.updated_datetime"
[27] "follow_ups.diabetes_treatment_type"
[28] "follow_ups.haart_treatment_indicator"
[29] "follow_ups.immunosuppressive_treatment_type"
[30] "follow_ups.reflux_treatment_type"
[31] "follow_ups.risk_factor_treatment" Note that each entry above is separated by a period (.);
this indicates the hierarchical structure. Summarise the top level
fields by using sub.
unique(sub("^(\\w+)\\..*", "\\1", all_fields$cases)) [1] "aliquot_ids" "analyte_ids"
[3] "annotations" "case_autocomplete"
[5] "case_id" "consent_type"
[7] "created_datetime" "days_to_consent"
[9] "days_to_lost_to_followup" "demographic"
[11] "diagnoses" "diagnosis_ids"
[13] "disease_type" "exposures"
[15] "family_histories" "files"
[17] "follow_ups" "index_date"
[19] "lost_to_followup" "portion_ids"
[21] "primary_site" "project"
[23] "sample_ids" "samples"
[25] "slide_ids" "state"
[27] "submitter_aliquot_ids" "submitter_analyte_ids"
[29] "submitter_diagnosis_ids" "submitter_id"
[31] "submitter_portion_ids" "submitter_sample_ids"
[33] "submitter_slide_ids" "summary"
[35] "tissue_source_site" "updated_datetime" All aggregations are only on one field at a time.
files() %>% facet(c("type", "data_format")) %>% aggregations()$data_format
doc_count key
1 188265 tsv
2 184432 vcf
3 163225 maf
4 149745 bam
5 123119 txt
6 52733 bedpe
7 32898 svs
8 32708 idat
9 24236 cel
10 24002 bcr xml
11 11324 pdf
12 10755 bcr ssf xml
13 2884 bcr auxiliary xml
14 1051 bcr omf xml
15 805 cdc json
16 602 bcr biotab
17 568 bcr pps xml
18 215 jpeg 2000
19 74 mex
20 70 xlsx
21 36 hdf5
$type
doc_count key
1 197177 annotated_somatic_mutation
2 149745 aligned_reads
3 98319 structural_variation
4 94773 simple_somatic_mutation
5 71861 copy_number_segment
6 69806 copy_number_estimate
7 46580 gene_expression
8 34661 aggregated_somatic_mutation
9 34408 mirna_expression
10 33113 slide_image
11 32708 masked_methylation_array
12 26978 biospecimen_supplement
13 24236 submitted_genotyping_array
14 23135 simple_germline_variation
15 16657 masked_somatic_mutation
16 16354 methylation_beta_value
17 13898 clinical_supplement
18 11324 pathology_report
19 7906 protein_expression
20 108 secondary_expression_analysisAggregate on a sub-field.
cases() %>% facet("diagnoses.treatments.treatment_type") %>% aggregations()$diagnoses.treatments.treatment_type
doc_count key
1 12170 radiation therapy, nos
2 11994 pharmaceutical therapy, nos
3 470 chemotherapy
4 520 stem cell transplantation, autologous
5 299 surgery, nos
6 171 targeted molecular therapy
7 168 immunotherapy (including vaccines)
8 96 radiation, external beam
9 53 brachytherapy, low dose
10 38 hormone therapy
11 33 brachytherapy, high dose
12 14 stem cell transplantation, allogeneic
13 9 radiation, 2d conventional
14 7 radiation, 3d conformal
15 6 radiation, intensity-modulated radiotherapy
16 5 radiation, stereotactic/gamma knife/srs
17 3 stereotactic radiosurgery
18 1 ablation, radiofrequency
19 1 external beam radiation
20 1 peptide receptor radionuclide therapy (prrt)
21 1 radiation, proton beam
22 76248 _missingFacet on open analysis.workflow_type.
files() %>%
filter(access == 'open') %>%
facet("analysis.workflow_type") %>%
aggregations()$analysis.workflow_type
doc_count key
1 49062 SeSAMe Methylation Beta Estimation
2 45258 DNAcopy
3 34408 BCGSC miRNA Profiling
4 23164 ASCAT2
5 23111 STAR - Counts
6 21264 ASCAT3
7 16522 Aliquot Ensemble Somatic Variant Merging and Masking
8 10677 ABSOLUTE LiftOver
9 8776 AscatNGS
10 108 Seurat - 10x Chromium
11 38 CellRanger - 10x Raw Counts
12 36 CellRanger - 10x Filtered Counts
13 92907 _missingFacet on open experimental_strategy.
files() %>%
filter(access == 'open') %>%
facet("experimental_strategy") %>%
aggregations()$experimental_strategy
doc_count key
1 100363 Genotyping Array
2 49062 Methylation Array
3 34408 miRNA-Seq
4 23111 RNA-Seq
5 21348 Tissue Slide
6 16075 WXS
7 11765 Diagnostic Slide
8 8776 WGS
9 7906 Reverse Phase Protein Array
10 447 Targeted Sequencing
11 182 scRNA-Seq
12 51888 _missingAll BAM files are under controlled access.
files() %>%
filter(data_format == 'bam') %>%
facet("access") %>%
aggregations()$access
doc_count key
1 149745 controlledAll VCF files are also under controlled access.
files() %>%
filter(data_format == 'vcf') %>%
facet("access") %>%
aggregations()$access
doc_count key
1 184432 controlledMutation Annotation Format (MAF) are openly available. These files are tab-delimited text files with aggregated mutation information from VCF files.
files() %>%
filter(access == 'open') %>%
filter(experimental_strategy == 'WXS') %>%
facet("data_format") %>%
aggregations()$data_format
doc_count key
1 16075 mafProject fields.
all_fields$projects [1] "dbgap_accession_number"
[2] "disease_type"
[3] "intended_release_date"
[4] "name"
[5] "primary_site"
[6] "program.dbgap_accession_number"
[7] "program.name"
[8] "program.program_id"
[9] "project_autocomplete"
[10] "project_id"
[11] "releasable"
[12] "released"
[13] "state"
[14] "summary.case_count"
[15] "summary.data_categories.case_count"
[16] "summary.data_categories.data_category"
[17] "summary.data_categories.file_count"
[18] "summary.experimental_strategies.case_count"
[19] "summary.experimental_strategies.experimental_strategy"
[20] "summary.experimental_strategies.file_count"
[21] "summary.file_count"
[22] "summary.file_size" Use projects to fetch project information and
ids to list all available projects.
projects() %>% results_all() -> project_info
sort(ids(project_info)) [1] "APOLLO-LUAD" "BEATAML1.0-COHORT"
[3] "BEATAML1.0-CRENOLANIB" "CDDP_EAGLE-1"
[5] "CGCI-BLGSP" "CGCI-HTMCP-CC"
[7] "CGCI-HTMCP-DLBCL" "CGCI-HTMCP-LC"
[9] "CMI-ASC" "CMI-MBC"
[11] "CMI-MPC" "CPTAC-2"
[13] "CPTAC-3" "CTSP-DLBCL1"
[15] "EXCEPTIONAL_RESPONDERS-ER" "FM-AD"
[17] "GENIE-DFCI" "GENIE-GRCC"
[19] "GENIE-JHU" "GENIE-MDA"
[21] "GENIE-MSK" "GENIE-NKI"
[23] "GENIE-UHN" "GENIE-VICC"
[25] "HCMI-CMDC" "MATCH-B"
[27] "MATCH-N" "MATCH-Q"
[29] "MATCH-Y" "MATCH-Z1D"
[31] "MMRF-COMMPASS" "MP2PRT-ALL"
[33] "MP2PRT-WT" "NCICCR-DLBCL"
[35] "OHSU-CNL" "ORGANOID-PANCREATIC"
[37] "REBC-THYR" "TARGET-ALL-P1"
[39] "TARGET-ALL-P2" "TARGET-ALL-P3"
[41] "TARGET-AML" "TARGET-CCSK"
[43] "TARGET-NBL" "TARGET-OS"
[45] "TARGET-RT" "TARGET-WT"
[47] "TCGA-ACC" "TCGA-BLCA"
[49] "TCGA-BRCA" "TCGA-CESC"
[51] "TCGA-CHOL" "TCGA-COAD"
[53] "TCGA-DLBC" "TCGA-ESCA"
[55] "TCGA-GBM" "TCGA-HNSC"
[57] "TCGA-KICH" "TCGA-KIRC"
[59] "TCGA-KIRP" "TCGA-LAML"
[61] "TCGA-LGG" "TCGA-LIHC"
[63] "TCGA-LUAD" "TCGA-LUSC"
[65] "TCGA-MESO" "TCGA-OV"
[67] "TCGA-PAAD" "TCGA-PCPG"
[69] "TCGA-PRAD" "TCGA-READ"
[71] "TCGA-SARC" "TCGA-SKCM"
[73] "TCGA-STAD" "TCGA-TGCT"
[75] "TCGA-THCA" "TCGA-THYM"
[77] "TCGA-UCEC" "TCGA-UCS"
[79] "TCGA-UVM" "TRIO-CRU"
[81] "VAREPOP-APOLLO" "WCDT-MCRPC" The results() method will fetch actual results.
projects() %>% results(size = 10) -> my_proj
str(my_proj, max.level = 1)List of 9
$ id : chr [1:10] "CGCI-HTMCP-CC" "TARGET-AML" "GENIE-JHU" "GENIE-MSK" ...
$ primary_site :List of 10
$ dbgap_accession_number: chr [1:10] "phs000528" "phs000465" NA NA ...
$ project_id : chr [1:10] "CGCI-HTMCP-CC" "TARGET-AML" "GENIE-JHU" "GENIE-MSK" ...
$ disease_type :List of 10
$ name : chr [1:10] "HIV+ Tumor Molecular Characterization Project - Cervical Cancer" "Acute Myeloid Leukemia" "AACR Project GENIE - Contributed by Johns Hopkins Sidney Kimmel Comprehensive Cancer Center" "AACR Project GENIE - Contributed by Memorial Sloan Kettering Cancer Center" ...
$ releasable : logi [1:10] TRUE TRUE TRUE TRUE TRUE TRUE ...
$ state : chr [1:10] "open" "open" "open" "open" ...
$ released : logi [1:10] TRUE TRUE TRUE TRUE TRUE TRUE ...
- attr(*, "row.names")= int [1:10] 1 2 3 4 5 6 7 8 9 10
- attr(*, "class")= chr [1:3] "GDCprojectsResults" "GDCResults" "list"my_proj$project_id [1] "CGCI-HTMCP-CC" "TARGET-AML" "GENIE-JHU" "GENIE-MSK"
[5] "GENIE-VICC" "GENIE-MDA" "TCGA-MESO" "TARGET-ALL-P3"
[9] "TCGA-UVM" "TCGA-KICH" Accessing clinical data.
case_ids <- cases() %>% results(size=10) %>% ids()
clindat <- gdc_clinical(case_ids)
names(clindat)[1] "demographic" "diagnoses" "exposures" "main" View available clinical data.
idx <- apply(clindat$diagnoses, 2, function(x) all(is.na(x)))
DT::datatable(clindat$diagnoses[, !idx])Find all files related to a specific case, or sample donor.
case1 <- cases() %>% results(size=1)
str(case1, max.level = 1)List of 25
$ id : chr "935ca1d3-2445-4f59-95a6-19f3311c1900"
$ lost_to_followup : chr "No"
$ slide_ids :List of 1
$ submitter_slide_ids :List of 1
$ days_to_lost_to_followup: logi NA
$ disease_type : chr "Squamous Cell Neoplasms"
$ analyte_ids :List of 1
$ submitter_id : chr "HTMCP-03-06-02345"
$ submitter_analyte_ids :List of 1
$ days_to_consent : logi NA
$ aliquot_ids :List of 1
$ submitter_aliquot_ids :List of 1
$ created_datetime : chr "2019-11-21T18:06:42.617487-06:00"
$ diagnosis_ids :List of 1
$ sample_ids :List of 1
$ consent_type : logi NA
$ submitter_sample_ids :List of 1
$ primary_site : chr "Cervix uteri"
$ submitter_diagnosis_ids :List of 1
$ updated_datetime : chr "2020-04-28T11:49:05.699379-05:00"
$ case_id : chr "935ca1d3-2445-4f59-95a6-19f3311c1900"
$ index_date : chr "Diagnosis"
$ state : chr "released"
$ portion_ids :List of 1
$ submitter_portion_ids :List of 1
- attr(*, "row.names")= int 1
- attr(*, "class")= chr [1:3] "GDCcasesResults" "GDCResults" "list"Sample IDs.
case1$sample_ids$`935ca1d3-2445-4f59-95a6-19f3311c1900`
[1] "f7706af8-c4e6-4e94-95f1-b6b4901dfe28"
[2] "bb3365f7-7bf9-46c6-ac60-4b7e77268ed8"
[3] "a35a4c87-86f9-4400-b43a-2b0999c69c19"All case fields.
case_fields <- available_fields("cases")Grep case_fields.
grep("sample_ids", case_fields, value = TRUE)[1] "sample_ids" "submitter_sample_ids"grep("sample_type", case_fields, value = TRUE)[1] "samples.sample_type" "samples.sample_type_id"grep("workflow_type", case_fields, value = TRUE)[1] "files.analysis.metadata.read_groups.read_group_qcs.workflow_type"
[2] "files.analysis.workflow_type"
[3] "files.downstream_analyses.workflow_type" Get case data.
n_star_cases <- cases() %>%
filter(files.analysis.workflow_type == 'STAR - Counts') %>%
filter(files.access == 'open') %>%
count()
star_cases <- cases() %>%
filter(files.analysis.workflow_type == 'STAR - Counts') %>%
filter(files.access == 'open') %>%
results(size = n_star_cases)
sapply(star_cases, length) id lost_to_followup slide_ids
19101 19101 19101
submitter_slide_ids days_to_lost_to_followup disease_type
19101 19101 19101
analyte_ids submitter_id submitter_analyte_ids
19101 19101 19101
days_to_consent aliquot_ids submitter_aliquot_ids
19101 19101 19101
created_datetime diagnosis_ids sample_ids
19101 19101 19101
consent_type submitter_sample_ids primary_site
19101 19101 19101
submitter_diagnosis_ids updated_datetime case_id
19101 19101 19101
index_date state portion_ids
19101 19101 19101
submitter_portion_ids
19101 case_id is the same as id.
table(star_cases$case_id == star_cases$id)
TRUE
19101 One case ID to multiple sample IDs.
head(star_cases$sample_ids, 3)$`9453db51-fff8-4a78-a29c-bb9151e9bd2a`
[1] "6662a85c-37b7-48b1-a8c6-f00171bb8226"
[2] "9bab246d-4a0d-4f28-ba1f-56b19a6f93bb"
[3] "6b8ea6bb-d10b-474a-9b4b-f406285dfb2f"
$`9485e946-f569-46fb-b77e-e5af68f7961a`
[1] "e3f781a2-f087-4abb-8f36-af799e837557"
[2] "cc8c2432-4107-4b5a-9452-3c536dac8baf"
[3] "330292a0-80dd-4fc4-a64c-4fce119dcbb6"
$`981300da-9136-402a-88df-2c76b1e3ad87`
[1] "42c67b29-94a1-4520-9122-b2daa02a03ad"
[2] "9d351761-59cb-40f7-aee2-ce2c6365acc2"
[3] "9276070c-cab5-4ba3-978d-2d18976a8758"Sample IDs to case IDs.
sample_id_len <- sapply(star_cases$sample_ids, length)
my_ids <- rep(names(sample_id_len), sample_id_len)
sample_id_lookup <- data.frame(
sample_ids = unlist(star_cases$sample_ids),
case_id = my_ids,
row.names = NULL
)
head(sample_id_lookup) sample_ids case_id
1 6662a85c-37b7-48b1-a8c6-f00171bb8226 9453db51-fff8-4a78-a29c-bb9151e9bd2a
2 9bab246d-4a0d-4f28-ba1f-56b19a6f93bb 9453db51-fff8-4a78-a29c-bb9151e9bd2a
3 6b8ea6bb-d10b-474a-9b4b-f406285dfb2f 9453db51-fff8-4a78-a29c-bb9151e9bd2a
4 e3f781a2-f087-4abb-8f36-af799e837557 9485e946-f569-46fb-b77e-e5af68f7961a
5 cc8c2432-4107-4b5a-9452-3c536dac8baf 9485e946-f569-46fb-b77e-e5af68f7961a
6 330292a0-80dd-4fc4-a64c-4fce119dcbb6 9485e946-f569-46fb-b77e-e5af68f7961aThe Cancer Genome Atlas (TCGA), a landmark cancer genomics program, molecularly characterized over 20,000 primary cancer and matched normal samples spanning 33 cancer types. This joint effort between NCI and the National Human Genome Research Institute began in 2006, bringing together researchers from diverse disciplines and multiple institutions.
Acronyms for TCGA cancer types:
From https://www.bioconductor.org/packages/release/bioc/vignettes/TCGAbiolinks/inst/doc/query.html
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. For example:
Aliquot barcode: TCGA-G4-6317-02A-11D-2064-05 Participant: TCGA-G4-6317 Sample: TCGA-G4-6317-02
Fetch projects.
projects() %>% results(size=100) -> my_projects
str(my_projects, max.level = 1)List of 9
$ id : chr [1:82] "CGCI-HTMCP-CC" "TARGET-AML" "GENIE-JHU" "GENIE-MSK" ...
$ primary_site :List of 82
$ dbgap_accession_number: chr [1:82] "phs000528" "phs000465" NA NA ...
$ project_id : chr [1:82] "CGCI-HTMCP-CC" "TARGET-AML" "GENIE-JHU" "GENIE-MSK" ...
$ disease_type :List of 82
$ name : chr [1:82] "HIV+ Tumor Molecular Characterization Project - Cervical Cancer" "Acute Myeloid Leukemia" "AACR Project GENIE - Contributed by Johns Hopkins Sidney Kimmel Comprehensive Cancer Center" "AACR Project GENIE - Contributed by Memorial Sloan Kettering Cancer Center" ...
$ releasable : logi [1:82] TRUE TRUE TRUE TRUE TRUE TRUE ...
$ state : chr [1:82] "open" "open" "open" "open" ...
$ released : logi [1:82] TRUE TRUE TRUE TRUE TRUE TRUE ...
- attr(*, "row.names")= int [1:82] 1 2 3 4 5 6 7 8 9 10 ...
- attr(*, "class")= chr [1:3] "GDCprojectsResults" "GDCResults" "list"Project IDs.
my_projects$id [1] "CGCI-HTMCP-CC" "TARGET-AML"
[3] "GENIE-JHU" "GENIE-MSK"
[5] "GENIE-VICC" "GENIE-MDA"
[7] "TCGA-MESO" "TARGET-ALL-P3"
[9] "TCGA-UVM" "TCGA-KICH"
[11] "TARGET-WT" "TARGET-OS"
[13] "TCGA-DLBC" "GENIE-UHN"
[15] "APOLLO-LUAD" "CDDP_EAGLE-1"
[17] "EXCEPTIONAL_RESPONDERS-ER" "MP2PRT-WT"
[19] "CGCI-HTMCP-DLBCL" "CMI-MPC"
[21] "WCDT-MCRPC" "TCGA-CHOL"
[23] "TCGA-UCS" "TCGA-PCPG"
[25] "CPTAC-2" "TCGA-CESC"
[27] "TCGA-LIHC" "TCGA-ACC"
[29] "CMI-MBC" "TCGA-BRCA"
[31] "CPTAC-3" "TCGA-COAD"
[33] "TCGA-GBM" "TCGA-TGCT"
[35] "NCICCR-DLBCL" "TCGA-LGG"
[37] "FM-AD" "GENIE-GRCC"
[39] "CTSP-DLBCL1" "TARGET-CCSK"
[41] "GENIE-NKI" "TARGET-ALL-P1"
[43] "MATCH-N" "TRIO-CRU"
[45] "CMI-ASC" "TARGET-RT"
[47] "ORGANOID-PANCREATIC" "MATCH-Z1D"
[49] "MATCH-B" "VAREPOP-APOLLO"
[51] "MATCH-Q" "BEATAML1.0-CRENOLANIB"
[53] "MATCH-Y" "OHSU-CNL"
[55] "CGCI-HTMCP-LC" "TARGET-NBL"
[57] "TCGA-SARC" "TCGA-PAAD"
[59] "TCGA-LUAD" "TCGA-PRAD"
[61] "MP2PRT-ALL" "TCGA-LUSC"
[63] "TCGA-LAML" "TCGA-SKCM"
[65] "HCMI-CMDC" "BEATAML1.0-COHORT"
[67] "TCGA-BLCA" "TCGA-READ"
[69] "TCGA-UCEC" "TCGA-THCA"
[71] "TCGA-OV" "TCGA-KIRC"
[73] "MMRF-COMMPASS" "GENIE-DFCI"
[75] "TCGA-HNSC" "TCGA-ESCA"
[77] "CGCI-BLGSP" "TARGET-ALL-P2"
[79] "TCGA-STAD" "REBC-THYR"
[81] "TCGA-KIRP" "TCGA-THYM" Treatment type.
cases() %>%
filter(project.project_id == 'TCGA-OV') %>%
facet("diagnoses.treatments.treatment_type") %>%
aggregations()$diagnoses.treatments.treatment_type
doc_count key
1 587 pharmaceutical therapy, nos
2 587 radiation therapy, nos
3 21 _missing
sessionInfo()R version 4.3.2 (2023-10-31)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 22.04.3 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so; LAPACK version 3.10.0
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
time zone: Etc/UTC
tzcode source: system (glibc)
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] GenomicDataCommons_1.26.0 magrittr_2.0.3
[3] lubridate_1.9.3 forcats_1.0.0
[5] stringr_1.5.0 dplyr_1.1.3
[7] purrr_1.0.2 readr_2.1.4
[9] tidyr_1.3.0 tibble_3.2.1
[11] ggplot2_3.4.4 tidyverse_2.0.0
[13] workflowr_1.7.1
loaded via a namespace (and not attached):
[1] gtable_0.3.4 xfun_0.40 bslib_0.5.1
[4] htmlwidgets_1.6.2 processx_3.8.2 callr_3.7.3
[7] tzdb_0.4.0 crosstalk_1.2.0 vctrs_0.6.4
[10] tools_4.3.2 ps_1.7.5 bitops_1.0-7
[13] generics_0.1.3 curl_5.1.0 stats4_4.3.2
[16] fansi_1.0.5 pkgconfig_2.0.3 S4Vectors_0.40.1
[19] lifecycle_1.0.3 GenomeInfoDbData_1.2.11 compiler_4.3.2
[22] git2r_0.32.0 munsell_0.5.0 getPass_0.2-2
[25] httpuv_1.6.12 GenomeInfoDb_1.38.0 htmltools_0.5.6.1
[28] sass_0.4.7 RCurl_1.98-1.12 yaml_2.3.7
[31] crayon_1.5.2 later_1.3.1 pillar_1.9.0
[34] jquerylib_0.1.4 whisker_0.4.1 ellipsis_0.3.2
[37] DT_0.30 cachem_1.0.8 tidyselect_1.2.0
[40] digest_0.6.33 stringi_1.7.12 rprojroot_2.0.3
[43] fastmap_1.1.1 grid_4.3.2 colorspace_2.1-0
[46] cli_3.6.1 utf8_1.2.4 withr_2.5.1
[49] rappdirs_0.3.3 scales_1.2.1 promises_1.2.1
[52] timechange_0.2.0 XVector_0.42.0 rmarkdown_2.25
[55] httr_1.4.7 hms_1.1.3 evaluate_0.22
[58] knitr_1.44 GenomicRanges_1.54.1 IRanges_2.36.0
[61] rlang_1.1.1 Rcpp_1.0.11 glue_1.6.2
[64] xml2_1.3.5 BiocGenerics_0.48.0 rstudioapi_0.15.0
[67] jsonlite_1.8.7 R6_2.5.1 zlibbioc_1.48.0
[70] fs_1.6.3